From f6ee44e5c8711030394f3e4fd6995661741ffd66 Mon Sep 17 00:00:00 2001 From: "Christine W. Wang" Date: Wed, 31 Aug 2016 11:55:24 +0800 Subject: [PATCH 01/21] Create DTD_SMC --- DTD_SMC | 1 + 1 file changed, 1 insertion(+) create mode 100644 DTD_SMC diff --git a/DTD_SMC b/DTD_SMC new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/DTD_SMC @@ -0,0 +1 @@ + From 27459363f0640fdc1e2d03f530a48f0fa9bccb4a Mon Sep 17 00:00:00 2001 From: "Christine W. Wang" Date: Wed, 31 Aug 2016 11:57:27 +0800 Subject: [PATCH 02/21] Delete DTD_SMC --- DTD_SMC | 1 - 1 file changed, 1 deletion(-) delete mode 100644 DTD_SMC diff --git a/DTD_SMC b/DTD_SMC deleted file mode 100644 index 8b13789..0000000 --- a/DTD_SMC +++ /dev/null @@ -1 +0,0 @@ - From 8a0c2f30ce065fef573721025cd66e2e4ec16872 Mon Sep 17 00:00:00 2001 From: "Christine W. Wang" Date: Fri, 9 Sep 2016 11:26:49 +0800 Subject: [PATCH 03/21] upload to Quantlet --- Metainfo[1].txt | 38 +++++++ README[1].md | 34 +++++++ RunSMC_est[1].m | 239 +++++++++++++++++++++++++++++++++++++++++++++ Table1[1].png | Bin 0 -> 75904 bytes Table2[1].png | Bin 0 -> 84210 bytes Table3[1].png | Bin 0 -> 158509 bytes Table4[1].png | Bin 0 -> 92365 bytes TableGenerate[1].m | 124 +++++++++++++++++++++++ 8 files changed, 435 insertions(+) create mode 100644 Metainfo[1].txt create mode 100644 README[1].md create mode 100644 RunSMC_est[1].m create mode 100644 Table1[1].png create mode 100644 Table2[1].png create mode 100644 Table3[1].png create mode 100644 Table4[1].png create mode 100644 TableGenerate[1].m diff --git a/Metainfo[1].txt b/Metainfo[1].txt new file mode 100644 index 0000000..e5a4861 --- /dev/null +++ b/Metainfo[1].txt @@ -0,0 +1,38 @@ + +Name of QuantLet: DTD_SMC + +Published in: Applied Quantitative Finance (3rd Edition) + +Description: 'Shows Distance-to-Default (DTD)s using sequential Monte Carlo (SMC) method introduced in the paper. +There are two code files. One is RunSMC_est.m, and the other is TableGenerate.m. In RunSMC_est.m, data from "Input" folder are loaded and all other functions are stored in "subfunciton" folder. The parameters for the initialization sampler are set in funciton SMCsettingbyFirm.m. Then, we sample 1024 particles using the initialization sampler and proceed with the function SMC_Step_MixW.m, which conducts the reweighting and resampling steps. After the resampling step, a function MoveSet_MixW.m runs Metropolis-Hastings (MH) moves. At this stage, the acceptant rate of each block will be displayed in the Command Window. The SMC_Step_MixW.m function will be repeatedly run until it finishesthe likelihood density-tempering for the first 5 firms. Then, it will go back to RunSMC_est.m file. 5 firms are added each time, and re-initialization followed by calling SMC_Step_MixW.m will be conducted again and again until reaching N firms. The following files in "Examples" folder are 12 firms from Airline sector in 2014 and 40 random selected firms from Insurance sector in 2009. OutY2014_Sec20018.xlsx/ Y2014_Sec20018.mat (Time required to estimate: 0.73 hours) OutY2009_Sec20055.xlsx/ Y2009_Sec20055.mat (Time required to estimate: 13.5 hours) +In TableGenerate.m file, Table 1 to Table 4 can be generated from the "Output" data.' + +Keywords: Distance-to-Default, credit risk model, sequential Monte Carlo method, density-tempered, expanding-data + +Author: Jin-Chuan Duan and Christine Wei-Ting Wang + +Submitted: + +Input: +Y2009G020018.mat, Y2014G020018.mat, airlines in 2009 and 2014, Y2009G020051.mat, Y2014G020051.mat, banks in 2009 and 2014, Y2009G020055.mat, Y2014G020055.mat, insurance in 2009 and 2014, Y2014G020082.mat, Y2009G020082.mat, engineering&construction in 2009 and 2014. +- Date: 250 daily observations for each firm up to the end of the year +- FirmCode: classified into 76 industry groups by Bloomberg Industry Classification System (BICS) +- Equity: market capitalization +- ShortD: short term debt +- LongD: long term debt +- otherL: other liability +- TotalA: total asset +- Rf: interest rate + +Output: +RandFirm_20051.mat: 40 Banks selected in paper. RandFirm_20055.mat: 40 Insurances selected in paper. +Y2009_Sec20018.mat, Y2014_Sec20018.mat, Y2009_Sec20082.mat, Y2014_Sec20082.mat, Y2009_Sec20051.mat, Y2014_Sec20051.mat, Y2009_Sec20055.mat, Y2014_Sec20055.mat OutY2009_Sec20018.xlsx, OutY2009_Sec20051.xlsx, OutY2009_Sec20055.xlsx, OutY2009_Sec20082.xlsx, OutY2014_Sec20018.xlsx, OutY2014_Sec20051.xlsx, OutY2014_Sec20055.xlsx, OutY2014_Sec20082.xlsx +Table1.png, Table2.png, Table3.png, Table4.png + +Data files: +deltaSD.mat, deltaSD_step3.mat + +Computer system: +Processor: Intel(R) Xeon(R) +CPU W3530 @ 2.80GHz +Installed memory: 8.00 GB Software: MATLAB R2013b diff --git a/README[1].md b/README[1].md new file mode 100644 index 0000000..fb74f1c --- /dev/null +++ b/README[1].md @@ -0,0 +1,34 @@ + +Name of QuantLet: DTD_SMC + +Published in: Applied Quantitative Finance third Edition + +Description: Shows Distance-to-Default (DTD)s using sequential Monte Carlo (SMC) method introduced in the paper. +There are two code files. One is RunSMC_est.m, and the other is TableGenerate.m. In RunSMC_est.m, data from "Input" folder are loaded and all other functions are stored in "subfunciton" folder. The parameters for the initialization sampler are set in funciton SMCsettingbyFirm.m. Then, we sample 1024 particles using the initialization sampler and proceed with the function SMC_Step_MixW.m, which conducts the reweighting and resampling steps. After the resampling step, a function MoveSet_MixW.m runs Metropolis-Hastings (MH) moves. At this stage, the acceptant rate of each block will be displayed in the Command Window. The SMC_Step_MixW.m function will be repeatedly run until it finishesthe likelihood density-tempering for the first 5 firms. Then, it will go back to RunSMC_est.m file. 5 firms are added each time, and re-initialization followed by calling SMC_Step_MixW.m will be conducted again and again until reaching N firms. The following files in "Examples" folder are 12 firms from Airline sector in 2014 and 40 random selected firms from Insurance sector in 2009. OutY2014_Sec20018.xlsx/ Y2014_Sec20018.mat (Time required to estimate: 0.73 hours) OutY2009_Sec20055.xlsx/ Y2009_Sec20055.mat (Time required to estimate: 13.5 hours) +In TableGenerate.m file, Table 1 to Table 4 can be generated from the "Output" data. + +Keywords: Distance-to-Default, credit risk model, sequential Monte Carlo method, density-tempered, expanding-data + +Author: Jin-Chuan Duan and Christine Wei-Ting Wang + +Submitted: + +Input: +Y2009G020018.mat, Y2014G020018.mat, airlines in 2009 and 2014, Y2009G020051.mat, Y2014G020051.mat, banks in 2009 and 2014, Y2009G020055.mat, Y2014G020055.mat, insurance in 2009 and 2014, Y2014G020082.mat, Y2009G020082.mat, engineering&construction in 2009 and 2014. +• Date: 250 daily observations for each firm up to the end of the year +• FirmCode: classified into 76 industry groups by Bloomberg Industry Classification System (BICS) +• Equity: market capitalization +• ShortD: short term debt +• LongD: long term debt +• otherL: other liability +• TotalA: total asset +• Rf: interest rate +RandFirm_20051.mat: 40 Banks selected in paper. RandFirm_20055.mat: 40 Insurances selected in paper. +Y2009_Sec20018.mat, Y2014_Sec20018.mat, Y2009_Sec20082.mat, Y2014_Sec20082.mat, Y2009_Sec20051.mat, Y2014_Sec20051.mat, Y2009_Sec20055.mat, Y2014_Sec20055.mat OutY2009_Sec20018.xlsx, OutY2009_Sec20051.xlsx, OutY2009_Sec20055.xlsx, OutY2009_Sec20082.xlsx, OutY2014_Sec20018.xlsx, OutY2014_Sec20051.xlsx, OutY2014_Sec20055.xlsx, OutY2014_Sec20082.xlsx + +Output: Table1.png, Table2.png, Table3.png, Table4.png + +Data files: +deltaSD.mat, deltaSD_step3.mat + +Computer system: Processor: Intel(R) Xeon(R) CPU W3530 @ 2.80GHz Installed memory: 8.00 GB Software: MATLAB R2013b diff --git a/RunSMC_est[1].m b/RunSMC_est[1].m new file mode 100644 index 0000000..dd70aac --- /dev/null +++ b/RunSMC_est[1].m @@ -0,0 +1,239 @@ +% % Parameter estimation by the density-tempered expanding-data +% sequential Monte Carlo +% Paper: Estimating Distance-to-Default with a Sector-Specific +% Liability Adjustment via Sequential Monte Carlo (Draft: August 12, 2016) + +clear all +randn('state',1); +rand('state',1); +p = parpool(4); % open parralete computing: Maximun 4 workers in Matlab R2013b +addpath('Subfunction') + +SimuFlag = 0; % 0: real data/ 1: simulation data +yearI = 2014; % [2009 2014] +SecCode = 20018; % [20018 20082 20051 20055] + +load(['Input\Y' num2str(yearI) 'G' num2str(SecCode,'%06d') '_c.mat']) +Fname_org = fieldnames(YearData); +Nfiled_org = length(Fname_org); + +% The random selected 40 firms in paper +if SecCode ==2 0051 || SecCode == 20055 + load(['Input\RandFirm_' num2str(SecCode) '.mat'],'FirmCode') + [Irand] = ismember(YearData.FirmCode,FirmCode); + for Iname=1:Nfiled_org + YearData.(Fname_org{Iname})=YearData.(Fname_org{Iname})(:,Irand); + end +end + +% Check data with Missing and sorting correspondingly +SunMiss = sum(isnan(YearData.Equity)); +[Mseq, Miss_sort] = sort(SunMiss); +% delete firms with 250 time series missing data +NaNInd = find(Mseq==250); +StoreDelete= YearData.FirmCode(1,Miss_sort(NaNInd)); +for Iname=1:Nfiled_org + Y.(Fname_org{Iname}) = YearData.(Fname_org{Iname})(:,Miss_sort); + Y.(Fname_org{Iname})(:,NaNInd) = []; +end +[Ydays, NtotFirm] = size( Y.Equity); + +SectorN = size( Y.Equity,2); +% SMCsettingbyFirm: Initilization parameters setting +[ ModelSetting,int_ComParam, int_Param, smcsettings ] = SMCsettingbyFirm( SectorN ); + +% Nrep: # of simulations --for Excel output +Nrep = 1; +ElapsT = zeros(1,Nrep,'double'); +OE = 2; +n0 = 0; + +% Step1. Initialization sampling: Common variables +for i=smcsettings.Com_Idx +theta.(smcsettings.Fname{i}) = InitSim_theta(smcsettings.Nsim, ... + int_ComParam.m, int_ComParam.s.^(1/2), int_ComParam.dist, int_ComParam.bound); +% density of initialization sampler for common parmameter + [ lnpdf_int(:,1)] = oglikelihood_density( theta.(smcsettings.Fname{i}),... + int_ComParam.m, diag(int_ComParam.s),1,0); +end + +Int_mean = zeros(ModelSetting.Ni); +Int_var = zeros(ModelSetting.Ni); +Nf = 0; + +for CountG = 1:smcsettings.Gnum + + begT = tic; + + % add Firms from Nf to Ns + Nf = smcsettings.MoveBlocks(CountG+1).moveindex(1)-1; % # of firms Finish SMC + Ns = smcsettings.MoveBlocks(CountG+1).moveindex(end); + disp(['add company to ' num2str( Ns )]) + Y_exp = ExtractData( Y, 1, Ns, ModelSetting.N ); + + % Step1. Initialization sampling: Individual variables + if CountG == 1 + + Int_data = zeros(smcsettings.Nsim, ModelSetting.Ni, Ns); + % only sample first Group partameters + for Gi = smcsettings.MoveBlocks(CountG+1).moveindex + Int_data(:,:,Gi) = InitSim_theta(smcsettings.Nsim, int_Param(Gi).m' ,... + (int_Param(Gi).s).^(1/2)', int_Param(Gi).dist, int_Param(Gi).bound ); + % log likelihood of initialization sampler + [ lnpdf_int(:,Gi+1)] = loglikelihood_density(... + Int_data(:,:,Gi),int_Param(Gi).m', diag(int_Param(Gi).s), 1,1); + end + % Initial theta enviroment + for i=1:ModelSetting.Ni + Iname = smcsettings.Ind_Idx(i); + theta.(smcsettings.Fname{Iname}) = ... + squeeze(Int_data(:,i,smcsettings.MoveBlocks(CountG+1).moveindex )); + end + + else % Step5. Adding more firms + Int_mean = zeros(ModelSetting.Ni,Nf); + Int_var = zeros(ModelSetting.Ni,Nf); + + for i=1:ModelSetting.Ni + Iname = smcsettings.Ind_Idx(i); + Int_mean(i,:) = mean( theta.(smcsettings.Fname{Iname})(:,1:Nf)) ; + Int_var(i,:) = var( theta.(smcsettings.Fname{Iname})(:,1:Nf) ) ; + Int_data(:,i,:) = squeeze( theta.(smcsettings.Fname{Iname}) ); + % Theta(i) regress on delta + for Gi=2:Nf + TmpReg = ols(theta.(smcsettings.Fname{Iname})(:,Gi), [ones(smcsettings.Nsim,1)... + theta.(smcsettings.Fname{smcsettings.Com_Idx})]); + int_Param(Gi).Coeff(i,:)= TmpReg.beta; + end + end + + % Re-Initialization: Resample Common Variable : + for i= smcsettings.Com_Idx + % Mix density funciton + % I_s ~ N(m,s) + int_ComParam.m(1,2) = mean(theta.(smcsettings.Fname{i}),1); + int_ComParam.s(1,2) = var(theta.(smcsettings.Fname{i}) ); + + IndM = random('bino',1,1-smcsettings.MixW,[smcsettings.Nsim ,1])==1; + % ( smcsettings.MixW )% generate from CountG result + theta.(smcsettings.Fname{i})(IndM==0) = InitSim_theta(sum(IndM==0), ... + int_ComParam.m(2), int_ComParam.s(2)^(1/2), int_ComParam.dist,... + int_ComParam.bound); + % ( 1-smcsettings.MixW )% generate from Initial setting I_0 + theta.(smcsettings.Fname{i})(IndM) = InitSim_theta(sum(IndM), ... + int_ComParam.m(1), int_ComParam.s(1)^(1/2), int_ComParam.dist,... + int_ComParam.bound); + + [ lnpdf_int(:,i),int_ComParam.tc] = loglikelihood_density(... + theta.(smcsettings.Fname{i}),int_ComParam.m,... + int_ComParam.s , 1,1, smcsettings.MixW, []); + + end + % adding firms + Int_data = cat(3,Int_data,zeros(smcsettings.Nsim, ModelSetting.Ni, Ns-Nf) ); + + for Gi=1:Ns + % Re-Initialization: Resample Individual variables + if Gi<=Nf + if Gi==1 % (1) Initialization + int_Param(Gi).m = Int_mean(:,Gi)'; + Mean_Fi = repmat(int_Param(Gi).m,smcsettings.Nsim,1); + int_Param(Gi).s = cov(bsxfun(@minus,Int_data(:,:,Gi),Mean_Fi)); + % Resample Initial theta from Trucated MultiNormal + % (beta1>0) + Int_data(:,:,Gi) = SimProposalMN2(Mean_Fi,int_Param(Gi).s,[], 1,1,... + int_Param(Gi).bound(1),int_Param(Gi).bound(2)); + else % (2:Nf) Initialization + Mean_Fi =[ones(smcsettings.Nsim,1) theta.(smcsettings.Fname{smcsettings.Com_Idx})]... + *int_Param(Gi).Coeff'; + int_Param(Gi).m = mean(Mean_Fi); + int_Param(Gi).s = cov(bsxfun(@minus,Int_data(:,:,Gi),Mean_Fi )); %* ModelSetting.adjK; + % Resample Initial theta from MultiNormal + Int_data(:,:,Gi) = SimProposalMN2(Mean_Fi,int_Param(Gi).s,[], 1,0,[],[]); + end + [ lnpdf_int(:,Gi+1)] = loglikelihood_density(... + Int_data(:,:,Gi),int_Param(Gi).m', int_Param(Gi).s, 0,1); + else % Expanding data (Nf+1:Ns) Initialization + + % Generate Initial theta from iid Normal + Int_data(:,:,Gi) = InitSim_theta(smcsettings.Nsim, int_Param(Gi).m' ,... + int_Param(Gi).s'.^(1/2), int_Param(Gi).dist, int_Param(Gi).bound ); + + % log likelihood of initialization sampler + [ lnpdf_int(:,Gi+1)] = loglikelihood_density(... + Int_data(:,:,Gi),int_Param(Gi).m', diag(int_Param(Gi).s), 1,1); + end + end + + for i=1:ModelSetting.Ni + Iname = smcsettings.Ind_Idx(i); + theta.(smcsettings.Fname{Iname}) = squeeze(Int_data(:,i,:)); + end + + end + + % evaluate log likelihood at initial parameters: smcsettings.fun + %%%% initial likelihood + [lnCL_addD] = feval(smcsettings.fun, theta, Y_exp,1 ); + + gamma=0; + lnw = zeros(smcsettings.Nsim,1); + t=1; + reslt(CountG).runtime(t)=toc(begT); + + while gamma < 1 + + begT = tic; + %%%%%% SMC step %%%%%%% + [reslt(CountG).ESS(t),reslt(CountG).normfac(t),reslt(CountG).AcceptRate(t,:),... + theta,lnCL_addD,lnw,lnpdf_int,gamma,smcsettings] = SMC_Step_MixW(theta,... + lnCL_addD,lnw,lnpdf_int,int_ComParam, int_Param,gamma,... + Y_exp,smcsettings,CountG); + + Xeven = theta; + + for Iname = 1:smcsettings.nameN + if ismember(Iname,smcsettings.Com_Idx); + reslt(CountG).(smcsettings.Fname{Iname})(t,:)=... + [mean(Xeven.(smcsettings.Fname{Iname})) min(Xeven.(smcsettings.Fname{Iname}))... + max(Xeven.(smcsettings.Fname{Iname})) prctile(Xeven.(smcsettings.Fname{Iname}),5) ... + prctile(Xeven.(smcsettings.Fname{Iname}),95) ... + mean(Xeven.(smcsettings.Fname{Iname})) - std(Xeven.(smcsettings.Fname{Iname}))*1.96 ... + mean(Xeven.(smcsettings.Fname{Iname})) + std(Xeven.(smcsettings.Fname{Iname}))*1.96]; + else + % Uncommon parameters + reslt(CountG).(smcsettings.Fname{Iname}).m(t,:)=... + [mean(Xeven.(smcsettings.Fname{Iname})) ... + min(Xeven.(smcsettings.Fname{Iname})) ... + max(Xeven.(smcsettings.Fname{Iname})) ]; + reslt(CountG).(smcsettings.Fname{Iname}).prc(t,:)=... + [prctile(Xeven.(smcsettings.Fname{Iname}),5) prctile(Xeven.(smcsettings.Fname{Iname}),95)]; + reslt(CountG).(smcsettings.Fname{Iname}).ci(t,:)=... + [mean(Xeven.(smcsettings.Fname{Iname})) - std(Xeven.(smcsettings.Fname{Iname}))*1.96 ... + mean(Xeven.(smcsettings.Fname{Iname})) + std(Xeven.(smcsettings.Fname{Iname}))*1.96]; + end + end + + reslt(CountG).runtime(t+1) = toc(begT); + + t=t+1; + disp([ 'gamma steps:' num2str(t-1)]); + end + + %store full-sample posterior + reslt(CountG).Xeven=Xeven; + + bridgeN(1,CountG) = t-1; + + outExcel=[reslt(CountG).weight(t-1,:);reshape([reslt(CountG).mu.m(t-1,:) reslt(CountG).mu.prc(t-1,:) reslt(CountG).mu.ci(t-1,:)],Ns,7);... + reshape([reslt(CountG).beta.m(t-1,:) reslt(CountG).beta.prc(t-1,:) reslt(CountG).beta.ci(t-1,:)],Ns,7);... + reshape(exp([reslt(CountG).nu.m(t-1,:) reslt(CountG).nu.prc(t-1,:) reslt(CountG).nu.ci(t-1,:)]),Ns,7)]; + xlswrite(['Examples\OutY' num2str(yearI) '_Sec' num2str(SecCode) '.xls'],outExcel,1,sprintf('B%d',OE)) + xlswrite(['Examples\OutY' num2str(yearI) '_Sec' num2str(SecCode) '.xls'],reslt(CountG).runtime,1,sprintf('M%d',CountG+1)) + OE = OE+size(outExcel,1)+1; + +end +save(['Examples\Y' num2str(yearI) '_Sec' num2str(SecCode) '.mat']) +disp(['Firms without any data available: ' num2str(StoreDelete)]) + +delete(p) diff --git a/Table1[1].png b/Table1[1].png new file mode 100644 index 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zePeaKkV`~y)b>&P@Q`nXR#_`+UU>7O31tf!u3V1!ypnng_A|>SngtmC$1!PXHNg~T&iMd9J{WA3H%m&XVrWyFhToqd5AR<0%7oQhD3oX7} z5QK952Pz56cSjPP9p6a?i8?GC{tBj5#%M33c--sIw$%H*F3Of9a{KGxS4FMJ{kE3jBfqE1KI!6RT=-k@cDm#_4f$;KWU7A u57WPg>Ax^r{|MoDq)#-j-5s8}d-!{V Date: Fri, 9 Sep 2016 11:27:39 +0800 Subject: [PATCH 04/21] Delete Metainfo[1].txt --- Metainfo[1].txt | 38 -------------------------------------- 1 file changed, 38 deletions(-) delete mode 100644 Metainfo[1].txt diff --git a/Metainfo[1].txt b/Metainfo[1].txt deleted file mode 100644 index e5a4861..0000000 --- a/Metainfo[1].txt +++ /dev/null @@ -1,38 +0,0 @@ - -Name of QuantLet: DTD_SMC - -Published in: Applied Quantitative Finance (3rd Edition) - -Description: 'Shows Distance-to-Default (DTD)s using sequential Monte Carlo (SMC) method introduced in the paper. -There are two code files. One is RunSMC_est.m, and the other is TableGenerate.m. In RunSMC_est.m, data from "Input" folder are loaded and all other functions are stored in "subfunciton" folder. The parameters for the initialization sampler are set in funciton SMCsettingbyFirm.m. Then, we sample 1024 particles using the initialization sampler and proceed with the function SMC_Step_MixW.m, which conducts the reweighting and resampling steps. After the resampling step, a function MoveSet_MixW.m runs Metropolis-Hastings (MH) moves. At this stage, the acceptant rate of each block will be displayed in the Command Window. The SMC_Step_MixW.m function will be repeatedly run until it finishesthe likelihood density-tempering for the first 5 firms. Then, it will go back to RunSMC_est.m file. 5 firms are added each time, and re-initialization followed by calling SMC_Step_MixW.m will be conducted again and again until reaching N firms. The following files in "Examples" folder are 12 firms from Airline sector in 2014 and 40 random selected firms from Insurance sector in 2009. OutY2014_Sec20018.xlsx/ Y2014_Sec20018.mat (Time required to estimate: 0.73 hours) OutY2009_Sec20055.xlsx/ Y2009_Sec20055.mat (Time required to estimate: 13.5 hours) -In TableGenerate.m file, Table 1 to Table 4 can be generated from the "Output" data.' - -Keywords: Distance-to-Default, credit risk model, sequential Monte Carlo method, density-tempered, expanding-data - -Author: Jin-Chuan Duan and Christine Wei-Ting Wang - -Submitted: - -Input: -Y2009G020018.mat, Y2014G020018.mat, airlines in 2009 and 2014, Y2009G020051.mat, Y2014G020051.mat, banks in 2009 and 2014, Y2009G020055.mat, Y2014G020055.mat, insurance in 2009 and 2014, Y2014G020082.mat, Y2009G020082.mat, engineering&construction in 2009 and 2014. -- Date: 250 daily observations for each firm up to the end of the year -- FirmCode: classified into 76 industry groups by Bloomberg Industry Classification System (BICS) -- Equity: market capitalization -- ShortD: short term debt -- LongD: long term debt -- otherL: other liability -- TotalA: total asset -- Rf: interest rate - -Output: -RandFirm_20051.mat: 40 Banks selected in paper. RandFirm_20055.mat: 40 Insurances selected in paper. -Y2009_Sec20018.mat, Y2014_Sec20018.mat, Y2009_Sec20082.mat, Y2014_Sec20082.mat, Y2009_Sec20051.mat, Y2014_Sec20051.mat, Y2009_Sec20055.mat, Y2014_Sec20055.mat OutY2009_Sec20018.xlsx, OutY2009_Sec20051.xlsx, OutY2009_Sec20055.xlsx, OutY2009_Sec20082.xlsx, OutY2014_Sec20018.xlsx, OutY2014_Sec20051.xlsx, OutY2014_Sec20055.xlsx, OutY2014_Sec20082.xlsx -Table1.png, Table2.png, Table3.png, Table4.png - -Data files: -deltaSD.mat, deltaSD_step3.mat - -Computer system: -Processor: Intel(R) Xeon(R) -CPU W3530 @ 2.80GHz -Installed memory: 8.00 GB Software: MATLAB R2013b From f0d09c3909b38b0e511af4461a6ec65b3c2bfe94 Mon Sep 17 00:00:00 2001 From: "Christine W. Wang" Date: Fri, 9 Sep 2016 11:27:47 +0800 Subject: [PATCH 05/21] Delete README[1].md --- README[1].md | 34 ---------------------------------- 1 file changed, 34 deletions(-) delete mode 100644 README[1].md diff --git a/README[1].md b/README[1].md deleted file mode 100644 index fb74f1c..0000000 --- a/README[1].md +++ /dev/null @@ -1,34 +0,0 @@ - -Name of QuantLet: DTD_SMC - -Published in: Applied Quantitative Finance third Edition - -Description: Shows Distance-to-Default (DTD)s using sequential Monte Carlo (SMC) method introduced in the paper. -There are two code files. One is RunSMC_est.m, and the other is TableGenerate.m. In RunSMC_est.m, data from "Input" folder are loaded and all other functions are stored in "subfunciton" folder. The parameters for the initialization sampler are set in funciton SMCsettingbyFirm.m. Then, we sample 1024 particles using the initialization sampler and proceed with the function SMC_Step_MixW.m, which conducts the reweighting and resampling steps. After the resampling step, a function MoveSet_MixW.m runs Metropolis-Hastings (MH) moves. At this stage, the acceptant rate of each block will be displayed in the Command Window. The SMC_Step_MixW.m function will be repeatedly run until it finishesthe likelihood density-tempering for the first 5 firms. Then, it will go back to RunSMC_est.m file. 5 firms are added each time, and re-initialization followed by calling SMC_Step_MixW.m will be conducted again and again until reaching N firms. The following files in "Examples" folder are 12 firms from Airline sector in 2014 and 40 random selected firms from Insurance sector in 2009. OutY2014_Sec20018.xlsx/ Y2014_Sec20018.mat (Time required to estimate: 0.73 hours) OutY2009_Sec20055.xlsx/ Y2009_Sec20055.mat (Time required to estimate: 13.5 hours) -In TableGenerate.m file, Table 1 to Table 4 can be generated from the "Output" data. - -Keywords: Distance-to-Default, credit risk model, sequential Monte Carlo method, density-tempered, expanding-data - -Author: Jin-Chuan Duan and Christine Wei-Ting Wang - -Submitted: - -Input: -Y2009G020018.mat, Y2014G020018.mat, airlines in 2009 and 2014, Y2009G020051.mat, Y2014G020051.mat, banks in 2009 and 2014, Y2009G020055.mat, Y2014G020055.mat, insurance in 2009 and 2014, Y2014G020082.mat, Y2009G020082.mat, engineering&construction in 2009 and 2014. -• Date: 250 daily observations for each firm up to the end of the year -• FirmCode: classified into 76 industry groups by Bloomberg Industry Classification System (BICS) -• Equity: market capitalization -• ShortD: short term debt -• LongD: long term debt -• otherL: other liability -• TotalA: total asset -• Rf: interest rate -RandFirm_20051.mat: 40 Banks selected in paper. RandFirm_20055.mat: 40 Insurances selected in paper. -Y2009_Sec20018.mat, Y2014_Sec20018.mat, Y2009_Sec20082.mat, Y2014_Sec20082.mat, Y2009_Sec20051.mat, Y2014_Sec20051.mat, Y2009_Sec20055.mat, Y2014_Sec20055.mat OutY2009_Sec20018.xlsx, OutY2009_Sec20051.xlsx, OutY2009_Sec20055.xlsx, OutY2009_Sec20082.xlsx, OutY2014_Sec20018.xlsx, OutY2014_Sec20051.xlsx, OutY2014_Sec20055.xlsx, OutY2014_Sec20082.xlsx - -Output: Table1.png, Table2.png, Table3.png, Table4.png - -Data files: -deltaSD.mat, deltaSD_step3.mat - -Computer system: Processor: Intel(R) Xeon(R) CPU W3530 @ 2.80GHz Installed memory: 8.00 GB Software: MATLAB R2013b From 7421be7cadee4e1f360eca459a6f9fdd07a4750c Mon Sep 17 00:00:00 2001 From: "Christine W. Wang" Date: Fri, 9 Sep 2016 11:28:01 +0800 Subject: [PATCH 06/21] Delete RunSMC_est[1].m --- RunSMC_est[1].m | 239 ------------------------------------------------ 1 file changed, 239 deletions(-) delete mode 100644 RunSMC_est[1].m diff --git a/RunSMC_est[1].m b/RunSMC_est[1].m deleted file mode 100644 index dd70aac..0000000 --- a/RunSMC_est[1].m +++ /dev/null @@ -1,239 +0,0 @@ -% % Parameter estimation by the density-tempered expanding-data -% sequential Monte Carlo -% Paper: Estimating Distance-to-Default with a Sector-Specific -% Liability Adjustment via Sequential Monte Carlo (Draft: August 12, 2016) - -clear all -randn('state',1); -rand('state',1); -p = parpool(4); % open parralete computing: Maximun 4 workers in Matlab R2013b -addpath('Subfunction') - -SimuFlag = 0; % 0: real data/ 1: simulation data -yearI = 2014; % [2009 2014] -SecCode = 20018; % [20018 20082 20051 20055] - -load(['Input\Y' num2str(yearI) 'G' num2str(SecCode,'%06d') '_c.mat']) -Fname_org = fieldnames(YearData); -Nfiled_org = length(Fname_org); - -% The random selected 40 firms in paper -if SecCode ==2 0051 || SecCode == 20055 - load(['Input\RandFirm_' num2str(SecCode) '.mat'],'FirmCode') - [Irand] = ismember(YearData.FirmCode,FirmCode); - for Iname=1:Nfiled_org - YearData.(Fname_org{Iname})=YearData.(Fname_org{Iname})(:,Irand); - end -end - -% Check data with Missing and sorting correspondingly -SunMiss = sum(isnan(YearData.Equity)); -[Mseq, Miss_sort] = sort(SunMiss); -% delete firms with 250 time series missing data -NaNInd = find(Mseq==250); -StoreDelete= YearData.FirmCode(1,Miss_sort(NaNInd)); -for Iname=1:Nfiled_org - Y.(Fname_org{Iname}) = YearData.(Fname_org{Iname})(:,Miss_sort); - Y.(Fname_org{Iname})(:,NaNInd) = []; -end -[Ydays, NtotFirm] = size( Y.Equity); - -SectorN = size( Y.Equity,2); -% SMCsettingbyFirm: Initilization parameters setting -[ ModelSetting,int_ComParam, int_Param, smcsettings ] = SMCsettingbyFirm( SectorN ); - -% Nrep: # of simulations --for Excel output -Nrep = 1; -ElapsT = zeros(1,Nrep,'double'); -OE = 2; -n0 = 0; - -% Step1. Initialization sampling: Common variables -for i=smcsettings.Com_Idx -theta.(smcsettings.Fname{i}) = InitSim_theta(smcsettings.Nsim, ... - int_ComParam.m, int_ComParam.s.^(1/2), int_ComParam.dist, int_ComParam.bound); -% density of initialization sampler for common parmameter - [ lnpdf_int(:,1)] = oglikelihood_density( theta.(smcsettings.Fname{i}),... - int_ComParam.m, diag(int_ComParam.s),1,0); -end - -Int_mean = zeros(ModelSetting.Ni); -Int_var = zeros(ModelSetting.Ni); -Nf = 0; - -for CountG = 1:smcsettings.Gnum - - begT = tic; - - % add Firms from Nf to Ns - Nf = smcsettings.MoveBlocks(CountG+1).moveindex(1)-1; % # of firms Finish SMC - Ns = smcsettings.MoveBlocks(CountG+1).moveindex(end); - disp(['add company to ' num2str( Ns )]) - Y_exp = ExtractData( Y, 1, Ns, ModelSetting.N ); - - % Step1. Initialization sampling: Individual variables - if CountG == 1 - - Int_data = zeros(smcsettings.Nsim, ModelSetting.Ni, Ns); - % only sample first Group partameters - for Gi = smcsettings.MoveBlocks(CountG+1).moveindex - Int_data(:,:,Gi) = InitSim_theta(smcsettings.Nsim, int_Param(Gi).m' ,... - (int_Param(Gi).s).^(1/2)', int_Param(Gi).dist, int_Param(Gi).bound ); - % log likelihood of initialization sampler - [ lnpdf_int(:,Gi+1)] = loglikelihood_density(... - Int_data(:,:,Gi),int_Param(Gi).m', diag(int_Param(Gi).s), 1,1); - end - % Initial theta enviroment - for i=1:ModelSetting.Ni - Iname = smcsettings.Ind_Idx(i); - theta.(smcsettings.Fname{Iname}) = ... - squeeze(Int_data(:,i,smcsettings.MoveBlocks(CountG+1).moveindex )); - end - - else % Step5. Adding more firms - Int_mean = zeros(ModelSetting.Ni,Nf); - Int_var = zeros(ModelSetting.Ni,Nf); - - for i=1:ModelSetting.Ni - Iname = smcsettings.Ind_Idx(i); - Int_mean(i,:) = mean( theta.(smcsettings.Fname{Iname})(:,1:Nf)) ; - Int_var(i,:) = var( theta.(smcsettings.Fname{Iname})(:,1:Nf) ) ; - Int_data(:,i,:) = squeeze( theta.(smcsettings.Fname{Iname}) ); - % Theta(i) regress on delta - for Gi=2:Nf - TmpReg = ols(theta.(smcsettings.Fname{Iname})(:,Gi), [ones(smcsettings.Nsim,1)... - theta.(smcsettings.Fname{smcsettings.Com_Idx})]); - int_Param(Gi).Coeff(i,:)= TmpReg.beta; - end - end - - % Re-Initialization: Resample Common Variable : - for i= smcsettings.Com_Idx - % Mix density funciton - % I_s ~ N(m,s) - int_ComParam.m(1,2) = mean(theta.(smcsettings.Fname{i}),1); - int_ComParam.s(1,2) = var(theta.(smcsettings.Fname{i}) ); - - IndM = random('bino',1,1-smcsettings.MixW,[smcsettings.Nsim ,1])==1; - % ( smcsettings.MixW )% generate from CountG result - theta.(smcsettings.Fname{i})(IndM==0) = InitSim_theta(sum(IndM==0), ... - int_ComParam.m(2), int_ComParam.s(2)^(1/2), int_ComParam.dist,... - int_ComParam.bound); - % ( 1-smcsettings.MixW )% generate from Initial setting I_0 - theta.(smcsettings.Fname{i})(IndM) = InitSim_theta(sum(IndM), ... - int_ComParam.m(1), int_ComParam.s(1)^(1/2), int_ComParam.dist,... - int_ComParam.bound); - - [ lnpdf_int(:,i),int_ComParam.tc] = loglikelihood_density(... - theta.(smcsettings.Fname{i}),int_ComParam.m,... - int_ComParam.s , 1,1, smcsettings.MixW, []); - - end - % adding firms - Int_data = cat(3,Int_data,zeros(smcsettings.Nsim, ModelSetting.Ni, Ns-Nf) ); - - for Gi=1:Ns - % Re-Initialization: Resample Individual variables - if Gi<=Nf - if Gi==1 % (1) Initialization - int_Param(Gi).m = Int_mean(:,Gi)'; - Mean_Fi = repmat(int_Param(Gi).m,smcsettings.Nsim,1); - int_Param(Gi).s = cov(bsxfun(@minus,Int_data(:,:,Gi),Mean_Fi)); - % Resample Initial theta from Trucated MultiNormal - % (beta1>0) - Int_data(:,:,Gi) = SimProposalMN2(Mean_Fi,int_Param(Gi).s,[], 1,1,... - int_Param(Gi).bound(1),int_Param(Gi).bound(2)); - else % (2:Nf) Initialization - Mean_Fi =[ones(smcsettings.Nsim,1) theta.(smcsettings.Fname{smcsettings.Com_Idx})]... - *int_Param(Gi).Coeff'; - int_Param(Gi).m = mean(Mean_Fi); - int_Param(Gi).s = cov(bsxfun(@minus,Int_data(:,:,Gi),Mean_Fi )); %* ModelSetting.adjK; - % Resample Initial theta from MultiNormal - Int_data(:,:,Gi) = SimProposalMN2(Mean_Fi,int_Param(Gi).s,[], 1,0,[],[]); - end - [ lnpdf_int(:,Gi+1)] = loglikelihood_density(... - Int_data(:,:,Gi),int_Param(Gi).m', int_Param(Gi).s, 0,1); - else % Expanding data (Nf+1:Ns) Initialization - - % Generate Initial theta from iid Normal - Int_data(:,:,Gi) = InitSim_theta(smcsettings.Nsim, int_Param(Gi).m' ,... - int_Param(Gi).s'.^(1/2), int_Param(Gi).dist, int_Param(Gi).bound ); - - % log likelihood of initialization sampler - [ lnpdf_int(:,Gi+1)] = loglikelihood_density(... - Int_data(:,:,Gi),int_Param(Gi).m', diag(int_Param(Gi).s), 1,1); - end - end - - for i=1:ModelSetting.Ni - Iname = smcsettings.Ind_Idx(i); - theta.(smcsettings.Fname{Iname}) = squeeze(Int_data(:,i,:)); - end - - end - - % evaluate log likelihood at initial parameters: smcsettings.fun - %%%% initial likelihood - [lnCL_addD] = feval(smcsettings.fun, theta, Y_exp,1 ); - - gamma=0; - lnw = zeros(smcsettings.Nsim,1); - t=1; - reslt(CountG).runtime(t)=toc(begT); - - while gamma < 1 - - begT = tic; - %%%%%% SMC step %%%%%%% - [reslt(CountG).ESS(t),reslt(CountG).normfac(t),reslt(CountG).AcceptRate(t,:),... - theta,lnCL_addD,lnw,lnpdf_int,gamma,smcsettings] = SMC_Step_MixW(theta,... - lnCL_addD,lnw,lnpdf_int,int_ComParam, int_Param,gamma,... - Y_exp,smcsettings,CountG); - - Xeven = theta; - - for Iname = 1:smcsettings.nameN - if ismember(Iname,smcsettings.Com_Idx); - reslt(CountG).(smcsettings.Fname{Iname})(t,:)=... - [mean(Xeven.(smcsettings.Fname{Iname})) min(Xeven.(smcsettings.Fname{Iname}))... - max(Xeven.(smcsettings.Fname{Iname})) prctile(Xeven.(smcsettings.Fname{Iname}),5) ... - prctile(Xeven.(smcsettings.Fname{Iname}),95) ... - mean(Xeven.(smcsettings.Fname{Iname})) - std(Xeven.(smcsettings.Fname{Iname}))*1.96 ... - mean(Xeven.(smcsettings.Fname{Iname})) + std(Xeven.(smcsettings.Fname{Iname}))*1.96]; - else - % Uncommon parameters - reslt(CountG).(smcsettings.Fname{Iname}).m(t,:)=... - [mean(Xeven.(smcsettings.Fname{Iname})) ... - min(Xeven.(smcsettings.Fname{Iname})) ... - max(Xeven.(smcsettings.Fname{Iname})) ]; - reslt(CountG).(smcsettings.Fname{Iname}).prc(t,:)=... - [prctile(Xeven.(smcsettings.Fname{Iname}),5) prctile(Xeven.(smcsettings.Fname{Iname}),95)]; - reslt(CountG).(smcsettings.Fname{Iname}).ci(t,:)=... - [mean(Xeven.(smcsettings.Fname{Iname})) - std(Xeven.(smcsettings.Fname{Iname}))*1.96 ... - mean(Xeven.(smcsettings.Fname{Iname})) + std(Xeven.(smcsettings.Fname{Iname}))*1.96]; - end - end - - reslt(CountG).runtime(t+1) = toc(begT); - - t=t+1; - disp([ 'gamma steps:' num2str(t-1)]); - end - - %store full-sample posterior - reslt(CountG).Xeven=Xeven; - - bridgeN(1,CountG) = t-1; - - outExcel=[reslt(CountG).weight(t-1,:);reshape([reslt(CountG).mu.m(t-1,:) reslt(CountG).mu.prc(t-1,:) reslt(CountG).mu.ci(t-1,:)],Ns,7);... - reshape([reslt(CountG).beta.m(t-1,:) reslt(CountG).beta.prc(t-1,:) reslt(CountG).beta.ci(t-1,:)],Ns,7);... - reshape(exp([reslt(CountG).nu.m(t-1,:) reslt(CountG).nu.prc(t-1,:) reslt(CountG).nu.ci(t-1,:)]),Ns,7)]; - xlswrite(['Examples\OutY' num2str(yearI) '_Sec' num2str(SecCode) '.xls'],outExcel,1,sprintf('B%d',OE)) - xlswrite(['Examples\OutY' num2str(yearI) '_Sec' num2str(SecCode) '.xls'],reslt(CountG).runtime,1,sprintf('M%d',CountG+1)) - OE = OE+size(outExcel,1)+1; - -end -save(['Examples\Y' num2str(yearI) '_Sec' num2str(SecCode) '.mat']) -disp(['Firms without any data available: ' num2str(StoreDelete)]) - -delete(p) From d275d8792926b38c79548c40519da05df4f86ed1 Mon Sep 17 00:00:00 2001 From: "Christine W. 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deletions(-) delete mode 100644 TableGenerate[1].m diff --git a/TableGenerate[1].m b/TableGenerate[1].m deleted file mode 100644 index 9b55f1f..0000000 --- a/TableGenerate[1].m +++ /dev/null @@ -1,124 +0,0 @@ -% To generate Table 1 to Table 4 of the paper -% "Estimating Distance-to-Default with a Sector-Specific -% Liability Adjustment via Sequential Monte Carlo" -% Section 4 Empirical implementation -clear all -OutTable2=zeros(7,8); - -for k=1:8 -switch k - case 1 - Year=2009; - IndustryCode=20018; - case 2 - Year=2014; - IndustryCode=20018; - case 3 - Year=2009; - IndustryCode=20082; - case 4 - Year=2014; - IndustryCode=20082; - case 5 - Year=2009; - IndustryCode=20051; - case 6 - Year=2014; - IndustryCode=20051; - case 7 - Year=2009; - IndustryCode=20055; - case 8 - Year=2014; - IndustryCode=20055; -end - -% loading raw data -load(['Input\Y' num2str(Year) 'G' num2str(IndustryCode,'%06d') '_c.mat']) -load(['Output\Y' num2str(Year) '_Sec' num2str(IndustryCode,'%05d') '.mat'], 'Y','theta','outExcel') %SMC result -% financial statement according to our critiera: -% 22 days (1 month) consuctive data available -Fname_org = fieldnames(YearData); -Nfiled_org = length(Fname_org); - -%% Table 1 -% Fname_org={'Date','FirmCode', 'Equity', 'ShortD','LongD', 'otherL','TotalA','Rf'}'; -OutTable1(1,k)=size(YearData.FirmCode,2); -for i=3:Nfiled_org-1 -OutTable1(i-1,k)=nanmean(YearData.(Fname_org{i})(250,:)); -end -TotalL=YearData.(Fname_org{4})+YearData.(Fname_org{5})+YearData.(Fname_org{6}); -OutTable1(7,k)= nanmean(TotalL(250,:)); -Dratio=YearData.otherL./TotalL; -OutTable1(8,k)= nanmean(Dratio(250,:)); - -%% Table 2 -% summary statistic of CRI data / -% CRI data critiera: -% more than 10 months and 250 daily data available within 24 months - -% estimation result from DTD MLE step1 -load('CRI_data\deltaSD.mat', 'fulldata') -indY = find(floor(fulldata(:,2)/100)==(Year*100+12)); - Ydata=fulldata(indY,:); % year end data - [im, ic] = ismember(Ydata(:,1),Y.FirmCode); - -tmpY=Ydata(im,:); -[ia, iR]=ismember(Y.FirmCode,tmpY); -CRIparam{4,k}=tmpY(iR(ia),:); -OutTable2(3,k) = nanmean(CRIparam{4,k}(:,7) ); - -% estimation result from DTD MLE step2 -load('CRI_data\deltaSD_step3.mat', 'fulldata') - -indY = find(floor(fulldata(:,2)/100)==(Year*100+12)); - Ydata=fulldata(indY,:); % year end data - [im, ic] = ismember(Ydata(:,1),Y.FirmCode); - -CRIparam{1,k}=IndustryCode; -CRIparam{2,k}=Year; -tmpY=Ydata(im,:); -[ia, iR]=ismember(Y.FirmCode,tmpY); -CRIparam{3,k}=tmpY(iR(ia),:); -% delta :CRIparam{3,k}(:,7) -- Table 2 -% mu :CRIparam{3,k}(:,5) -- Table 3 -% sigma :CRIparam{3,k}(:,6) -- Table 3 -OutTable2(4,k) = nanmean(CRIparam{3,k}(:,7) ); - -% estimation result from SMC method -weight = outExcel(1,1); -OutTable2(5:7,k) = [weight; prctile(theta.weight,2.5); prctile(theta.weight,97.5)]; -Nq=size(Y.FirmCode,2); -MissR=sum(sum(isnan(Y.Equity)))/(250*Nq); -OutTable2(1:2,k) = [Nq; MissR]; - -OutTable3_a(1:2,k)=[nanmean(CRIparam{3,k}(:,5))*250;nanmean(CRIparam{3,k}(:,6))*sqrt(250)]; - -% Table 3_b: Firm-specific parameters for four industry sectors in 2009 and 2014 -sigma=sqrt(outExcel(Nq+2:2*Nq+1,1).^2+outExcel(2*Nq+2:3*Nq+1,1).^2); -OutTable3_b(1:16,k)= [nanmean(outExcel(2:Nq+1,1)); nanmedian(outExcel(2:Nq+1,1)) ;nanmin(outExcel(2:Nq+1,1) ) ;nanmax(outExcel(2:Nq+1,1) ); - nanmean(outExcel(Nq+2:2*Nq+1,1)); nanmedian(outExcel(Nq+2:2*Nq+1,1)) ;nanmin(outExcel(Nq+2:2*Nq+1,1)) ;nanmax(outExcel(Nq+2:2*Nq+1,1)); - nanmean(outExcel(2*Nq+2:3*Nq+1,1)); nanmedian(outExcel(2*Nq+2:3*Nq+1,1));nanmin(outExcel(2*Nq+2:3*Nq+1,1));nanmax(outExcel(2*Nq+2:3*Nq+1,1)); - nanmean(sigma); nanmedian(sigma) ;nanmin(sigma ) ;nanmax(sigma )]; - CV2= diag( outExcel(2*Nq+2:3*Nq+1,1).^2 )+ outExcel(Nq+2:2*Nq+1,1)*outExcel(Nq+2:2*Nq+1,1)'; - CR=corrcov(CV2); - tmpCR=reshape(tril(CR,-1),1,Nq*Nq); - dataCR=tmpCR(tmpCR~=0); -OutTable3_b(17:20,k)=[ mean(dataCR);median(dataCR);min(dataCR);max(dataCR)]; - -% Table 4: DTD comparison for four industry sectors in 2009 and 2014. -addpath('Subfunction') - -tdays=250; -liability_valid = Y.ShortD + 0.5 * Y.LongD + weight * Y.otherL; -[evalue ] = impvalue_annual(Y.Equity(:,1:Nq), liability_valid(:,1:Nq), Y.Rf(:,1:Nq)/(100), sigma', 250/tdays, []); -% mu = reslt(end).mu.m(end,1:Nq)-sig.* sig /2; -DTD=((log(evalue(end,1:Nq)./liability_valid(end,1:Nq)))./(sigma'))'; -DTDtmp=DTD(ia); %to consistance with CRI database -DTDnanI=~isnan( DTDtmp); - % compare CRI and SMC -OutTable4(1:10,k)= [nanmean(CRIparam{3,k}(:,3)); nanmedian(CRIparam{3,k}(:,3)) ;nanmin(CRIparam{3,k}(:,3) ) ;nanmax(CRIparam{3,k}(:,3) );... -nanmean(DTD); nanmedian(DTD) ;nanmin(DTD ) ;nanmax(DTD );... -corr( CRIparam{3,k}(DTDnanI,3) , DTDtmp(DTDnanI),'type','Kendall');corr(CRIparam{3,k}(DTDnanI,3),DTDtmp(DTDnanI),'type','Pearson')]; - -end From bdabced9fd4745ddab470708c5e82f9a1f648641 Mon Sep 17 00:00:00 2001 From: "Christine W. 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z8(*O3l~lf_b9Dle!}tYh?!LL&il1;(CM~vrfzD>+C#Tww>s!%r^XJ#`CT>F*U%I zACX-(O>}92&{IqdEFSH?(+g@Bz`8)qvMKb?p$0Y2dP3ljCcyhx?Xb(~p?#%3amTz?vrxFNdaj=E_~Ve@vg%V=PzZ6rRk(c6ZvED|o@XqtOYODzZn|&M4_> zyu3~8+nWN#b@%qi{7asdz*O(m^Etb$tW>g2k3}Kil|M1M(8~d*OqR@OD zjklUlC&wdacyDrWcO6r+ch!fILXwaz`ymff}cp_BUrUtyt4}bq-R@OTcwH} z{FTC*RgzrlPUpvYttcqxaJv2U=xDX0d4snmpICJE3n@H*e$R|3;S0~^HrIfpnxel>+wwx-b4h{aeTPljf&l85&5F%GnEa28t zybAr&^*bQ1xxUg`CQ?<#BzChFh#q3e%bj>Beg&Tuh{)AL7VX1U;HOhP!s2Pe6Ev-iSA+-71i_@IAFn|xHs%zT?Q01S- zw&`61SM=H3>?Yuj(-qiQg-_U{sZ*x2Q-}CBGk__&)H~i$PURe46=c}VZbl@ZQtzg%H8%M7=!4&i*CWzXQEkE4xG#?!DxIx!5rFfac;9&?U-&oJolxS+!Za`99rl9d79;(Ez`$a^`l>l8yW%2ZiIZhm~t#2## zcBx5{^XWkH3MAmweq)KQu>URAbD&C%0Q78&)EAMfYwi(24r zNF_K8fYW$Fj08!ZrZye4$c?h@oumoKHoYQEM69GBWOf`3Qbt&TNz(r738=$uu(s!A z%*6}e0U^)Eo_LMndLp-Fn{Y}x9kqrZ4nyU>kX^VC5O3C8%0$O_WVFD2|zg7JG?`ZN17w)M30?%Z0|2X_Ee4CVJ}I{)~v|K$Vz|Kb1nrT+i# Date: Fri, 9 Sep 2016 11:31:05 +0800 Subject: [PATCH 12/21] Create XFG_DTD_SMC --- XFG_DTD_SMC | 1 + 1 file changed, 1 insertion(+) create mode 100644 XFG_DTD_SMC diff --git a/XFG_DTD_SMC b/XFG_DTD_SMC new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/XFG_DTD_SMC @@ -0,0 +1 @@ + From 10b1c7c53ea092e0128de11d2bb65cb8d7d1a650 Mon Sep 17 00:00:00 2001 From: "Christine W. Wang" Date: Fri, 9 Sep 2016 11:31:16 +0800 Subject: [PATCH 13/21] Delete XFG_DTD_SMC --- XFG_DTD_SMC | 1 - 1 file changed, 1 deletion(-) delete mode 100644 XFG_DTD_SMC diff --git a/XFG_DTD_SMC b/XFG_DTD_SMC deleted file mode 100644 index 8b13789..0000000 --- a/XFG_DTD_SMC +++ /dev/null @@ -1 +0,0 @@ - From 6ab6bb57e633b2eb6c10012f81217de3ff42649c Mon Sep 17 00:00:00 2001 From: "Christine W. Wang" Date: Fri, 9 Sep 2016 11:35:23 +0800 Subject: [PATCH 14/21] Create README-edit --- XFG_DTD_SMC/README-edit | 37 +++++++++++++++++++++++++++++++++++++ 1 file changed, 37 insertions(+) create mode 100644 XFG_DTD_SMC/README-edit diff --git a/XFG_DTD_SMC/README-edit b/XFG_DTD_SMC/README-edit new file mode 100644 index 0000000..ec199d5 --- /dev/null +++ b/XFG_DTD_SMC/README-edit @@ -0,0 +1,37 @@ +Name of QuantLet: DTD_SMC + +Published in: Applied Quantitative Finance (3rd Edition) + +Description: 'Shows Distance-to-Default (DTD)s using sequential Monte Carlo (SMC) method introduced in the paper. +There are two code files. One is RunSMC_est.m, and the other is TableGenerate.m. In RunSMC_est.m, data from "Input" folder are loaded and all other functions are stored in "subfunciton" folder. The parameters for the initialization sampler are set in funciton SMCsettingbyFirm.m. Then, we sample 1024 particles using the initialization sampler and proceed with the function SMC_Step_MixW.m, which conducts the reweighting and resampling steps. After the resampling step, a function MoveSet_MixW.m runs Metropolis-Hastings (MH) moves. At this stage, the acceptant rate of each block will be displayed in the Command Window. The SMC_Step_MixW.m function will be repeatedly run until it finishesthe likelihood density-tempering for the first 5 firms. Then, it will go back to RunSMC_est.m file. 5 firms are added each time, and re-initialization followed by calling SMC_Step_MixW.m will be conducted again and again until reaching N firms. The following files in "Examples" folder are 12 firms from Airline sector in 2014 and 40 random selected firms from Insurance sector in 2009. OutY2014_Sec20018.xlsx/ Y2014_Sec20018.mat (Time required to estimate: 0.73 hours) OutY2009_Sec20055.xlsx/ Y2009_Sec20055.mat (Time required to estimate: 13.5 hours) +In TableGenerate.m file, Table 1 to Table 4 can be generated from the "Output" data.' + +Keywords: Distance-to-Default, credit risk model, sequential Monte Carlo method, density-tempered, expanding-data + +Author: Jin-Chuan Duan and Christine Wei-Ting Wang + +Submitted: + +Input: +Y2009G020018.mat, Y2014G020018.mat, airlines in 2009 and 2014, Y2009G020051.mat, Y2014G020051.mat, banks in 2009 and 2014, Y2009G020055.mat, Y2014G020055.mat, insurance in 2009 and 2014, Y2014G020082.mat, Y2009G020082.mat, engineering&construction in 2009 and 2014. +- Date: 250 daily observations for each firm up to the end of the year +- FirmCode: classified into 76 industry groups by Bloomberg Industry Classification System (BICS) +- Equity: market capitalization +- ShortD: short term debt +- LongD: long term debt +- otherL: other liability +- TotalA: total asset +- Rf: interest rate + +Output: +RandFirm_20051.mat: 40 Banks selected in paper. RandFirm_20055.mat: 40 Insurances selected in paper. +Y2009_Sec20018.mat, Y2014_Sec20018.mat, Y2009_Sec20082.mat, Y2014_Sec20082.mat, Y2009_Sec20051.mat, Y2014_Sec20051.mat, Y2009_Sec20055.mat, Y2014_Sec20055.mat OutY2009_Sec20018.xlsx, OutY2009_Sec20051.xlsx, OutY2009_Sec20055.xlsx, OutY2009_Sec20082.xlsx, OutY2014_Sec20018.xlsx, OutY2014_Sec20051.xlsx, OutY2014_Sec20055.xlsx, OutY2014_Sec20082.xlsx +Table1.png, Table2.png, Table3.png, Table4.png + +Data files: +deltaSD.mat, deltaSD_step3.mat + +Computer system: +Processor: Intel(R) Xeon(R) +CPU W3530 @ 2.80GHz +Installed memory: 8.00 GB Software: MATLAB R2013b From 2f7488efda45e10d1f220d6635c98eea2117438e Mon Sep 17 00:00:00 2001 From: "Christine W. Wang" Date: Fri, 9 Sep 2016 11:35:47 +0800 Subject: [PATCH 15/21] Delete README-edit --- XFG_DTD_SMC/README-edit | 37 ------------------------------------- 1 file changed, 37 deletions(-) delete mode 100644 XFG_DTD_SMC/README-edit diff --git a/XFG_DTD_SMC/README-edit b/XFG_DTD_SMC/README-edit deleted file mode 100644 index ec199d5..0000000 --- a/XFG_DTD_SMC/README-edit +++ /dev/null @@ -1,37 +0,0 @@ -Name of QuantLet: DTD_SMC - -Published in: Applied Quantitative Finance (3rd Edition) - -Description: 'Shows Distance-to-Default (DTD)s using sequential Monte Carlo (SMC) method introduced in the paper. -There are two code files. One is RunSMC_est.m, and the other is TableGenerate.m. In RunSMC_est.m, data from "Input" folder are loaded and all other functions are stored in "subfunciton" folder. The parameters for the initialization sampler are set in funciton SMCsettingbyFirm.m. Then, we sample 1024 particles using the initialization sampler and proceed with the function SMC_Step_MixW.m, which conducts the reweighting and resampling steps. After the resampling step, a function MoveSet_MixW.m runs Metropolis-Hastings (MH) moves. At this stage, the acceptant rate of each block will be displayed in the Command Window. The SMC_Step_MixW.m function will be repeatedly run until it finishesthe likelihood density-tempering for the first 5 firms. Then, it will go back to RunSMC_est.m file. 5 firms are added each time, and re-initialization followed by calling SMC_Step_MixW.m will be conducted again and again until reaching N firms. The following files in "Examples" folder are 12 firms from Airline sector in 2014 and 40 random selected firms from Insurance sector in 2009. OutY2014_Sec20018.xlsx/ Y2014_Sec20018.mat (Time required to estimate: 0.73 hours) OutY2009_Sec20055.xlsx/ Y2009_Sec20055.mat (Time required to estimate: 13.5 hours) -In TableGenerate.m file, Table 1 to Table 4 can be generated from the "Output" data.' - -Keywords: Distance-to-Default, credit risk model, sequential Monte Carlo method, density-tempered, expanding-data - -Author: Jin-Chuan Duan and Christine Wei-Ting Wang - -Submitted: - -Input: -Y2009G020018.mat, Y2014G020018.mat, airlines in 2009 and 2014, Y2009G020051.mat, Y2014G020051.mat, banks in 2009 and 2014, Y2009G020055.mat, Y2014G020055.mat, insurance in 2009 and 2014, Y2014G020082.mat, Y2009G020082.mat, engineering&construction in 2009 and 2014. -- Date: 250 daily observations for each firm up to the end of the year -- FirmCode: classified into 76 industry groups by Bloomberg Industry Classification System (BICS) -- Equity: market capitalization -- ShortD: short term debt -- LongD: long term debt -- otherL: other liability -- TotalA: total asset -- Rf: interest rate - -Output: -RandFirm_20051.mat: 40 Banks selected in paper. RandFirm_20055.mat: 40 Insurances selected in paper. -Y2009_Sec20018.mat, Y2014_Sec20018.mat, Y2009_Sec20082.mat, Y2014_Sec20082.mat, Y2009_Sec20051.mat, Y2014_Sec20051.mat, Y2009_Sec20055.mat, Y2014_Sec20055.mat OutY2009_Sec20018.xlsx, OutY2009_Sec20051.xlsx, OutY2009_Sec20055.xlsx, OutY2009_Sec20082.xlsx, OutY2014_Sec20018.xlsx, OutY2014_Sec20051.xlsx, OutY2014_Sec20055.xlsx, OutY2014_Sec20082.xlsx -Table1.png, Table2.png, Table3.png, Table4.png - -Data files: -deltaSD.mat, deltaSD_step3.mat - -Computer system: -Processor: Intel(R) Xeon(R) -CPU W3530 @ 2.80GHz -Installed memory: 8.00 GB Software: MATLAB R2013b From fc0686852de2de869417826aec2feacc3f107053 Mon Sep 17 00:00:00 2001 From: "Christine W. Wang" Date: Fri, 9 Sep 2016 11:37:14 +0800 Subject: [PATCH 16/21] Create Metainfo.txt --- XFG_DTD_SMC/Metainfo.txt | 37 +++++++++++++++++++++++++++++++++++++ 1 file changed, 37 insertions(+) create mode 100644 XFG_DTD_SMC/Metainfo.txt diff --git a/XFG_DTD_SMC/Metainfo.txt b/XFG_DTD_SMC/Metainfo.txt new file mode 100644 index 0000000..ec199d5 --- /dev/null +++ b/XFG_DTD_SMC/Metainfo.txt @@ -0,0 +1,37 @@ +Name of QuantLet: DTD_SMC + +Published in: Applied Quantitative Finance (3rd Edition) + +Description: 'Shows Distance-to-Default (DTD)s using sequential Monte Carlo (SMC) method introduced in the paper. +There are two code files. One is RunSMC_est.m, and the other is TableGenerate.m. In RunSMC_est.m, data from "Input" folder are loaded and all other functions are stored in "subfunciton" folder. The parameters for the initialization sampler are set in funciton SMCsettingbyFirm.m. Then, we sample 1024 particles using the initialization sampler and proceed with the function SMC_Step_MixW.m, which conducts the reweighting and resampling steps. After the resampling step, a function MoveSet_MixW.m runs Metropolis-Hastings (MH) moves. At this stage, the acceptant rate of each block will be displayed in the Command Window. The SMC_Step_MixW.m function will be repeatedly run until it finishesthe likelihood density-tempering for the first 5 firms. Then, it will go back to RunSMC_est.m file. 5 firms are added each time, and re-initialization followed by calling SMC_Step_MixW.m will be conducted again and again until reaching N firms. The following files in "Examples" folder are 12 firms from Airline sector in 2014 and 40 random selected firms from Insurance sector in 2009. OutY2014_Sec20018.xlsx/ Y2014_Sec20018.mat (Time required to estimate: 0.73 hours) OutY2009_Sec20055.xlsx/ Y2009_Sec20055.mat (Time required to estimate: 13.5 hours) +In TableGenerate.m file, Table 1 to Table 4 can be generated from the "Output" data.' + +Keywords: Distance-to-Default, credit risk model, sequential Monte Carlo method, density-tempered, expanding-data + +Author: Jin-Chuan Duan and Christine Wei-Ting Wang + +Submitted: + +Input: +Y2009G020018.mat, Y2014G020018.mat, airlines in 2009 and 2014, Y2009G020051.mat, Y2014G020051.mat, banks in 2009 and 2014, Y2009G020055.mat, Y2014G020055.mat, insurance in 2009 and 2014, Y2014G020082.mat, Y2009G020082.mat, engineering&construction in 2009 and 2014. +- Date: 250 daily observations for each firm up to the end of the year +- FirmCode: classified into 76 industry groups by Bloomberg Industry Classification System (BICS) +- Equity: market capitalization +- ShortD: short term debt +- LongD: long term debt +- otherL: other liability +- TotalA: total asset +- Rf: interest rate + +Output: +RandFirm_20051.mat: 40 Banks selected in paper. RandFirm_20055.mat: 40 Insurances selected in paper. +Y2009_Sec20018.mat, Y2014_Sec20018.mat, Y2009_Sec20082.mat, Y2014_Sec20082.mat, Y2009_Sec20051.mat, Y2014_Sec20051.mat, Y2009_Sec20055.mat, Y2014_Sec20055.mat OutY2009_Sec20018.xlsx, OutY2009_Sec20051.xlsx, OutY2009_Sec20055.xlsx, OutY2009_Sec20082.xlsx, OutY2014_Sec20018.xlsx, OutY2014_Sec20051.xlsx, OutY2014_Sec20055.xlsx, OutY2014_Sec20082.xlsx +Table1.png, Table2.png, Table3.png, Table4.png + +Data files: +deltaSD.mat, deltaSD_step3.mat + +Computer system: +Processor: Intel(R) Xeon(R) +CPU W3530 @ 2.80GHz +Installed memory: 8.00 GB Software: MATLAB R2013b From 3def37e40406c5bc1fa9dd836558fc5d6985eeee Mon Sep 17 00:00:00 2001 From: "Christine W. Wang" Date: Fri, 9 Sep 2016 11:38:00 +0800 Subject: [PATCH 17/21] Add files via upload --- XFG_DTD_SMC/README[1].md | 34 ++++++++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) create mode 100644 XFG_DTD_SMC/README[1].md diff --git a/XFG_DTD_SMC/README[1].md b/XFG_DTD_SMC/README[1].md new file mode 100644 index 0000000..fb74f1c --- /dev/null +++ b/XFG_DTD_SMC/README[1].md @@ -0,0 +1,34 @@ + +Name of QuantLet: DTD_SMC + +Published in: Applied Quantitative Finance third Edition + +Description: Shows Distance-to-Default (DTD)s using sequential Monte Carlo (SMC) method introduced in the paper. +There are two code files. One is RunSMC_est.m, and the other is TableGenerate.m. In RunSMC_est.m, data from "Input" folder are loaded and all other functions are stored in "subfunciton" folder. The parameters for the initialization sampler are set in funciton SMCsettingbyFirm.m. Then, we sample 1024 particles using the initialization sampler and proceed with the function SMC_Step_MixW.m, which conducts the reweighting and resampling steps. After the resampling step, a function MoveSet_MixW.m runs Metropolis-Hastings (MH) moves. At this stage, the acceptant rate of each block will be displayed in the Command Window. The SMC_Step_MixW.m function will be repeatedly run until it finishesthe likelihood density-tempering for the first 5 firms. Then, it will go back to RunSMC_est.m file. 5 firms are added each time, and re-initialization followed by calling SMC_Step_MixW.m will be conducted again and again until reaching N firms. The following files in "Examples" folder are 12 firms from Airline sector in 2014 and 40 random selected firms from Insurance sector in 2009. OutY2014_Sec20018.xlsx/ Y2014_Sec20018.mat (Time required to estimate: 0.73 hours) OutY2009_Sec20055.xlsx/ Y2009_Sec20055.mat (Time required to estimate: 13.5 hours) +In TableGenerate.m file, Table 1 to Table 4 can be generated from the "Output" data. + +Keywords: Distance-to-Default, credit risk model, sequential Monte Carlo method, density-tempered, expanding-data + +Author: Jin-Chuan Duan and Christine Wei-Ting Wang + +Submitted: + +Input: +Y2009G020018.mat, Y2014G020018.mat, airlines in 2009 and 2014, Y2009G020051.mat, Y2014G020051.mat, banks in 2009 and 2014, Y2009G020055.mat, Y2014G020055.mat, insurance in 2009 and 2014, Y2014G020082.mat, Y2009G020082.mat, engineering&construction in 2009 and 2014. +• Date: 250 daily observations for each firm up to the end of the year +• FirmCode: classified into 76 industry groups by Bloomberg Industry Classification System (BICS) +• Equity: market capitalization +• ShortD: short term debt +• LongD: long term debt +• otherL: other liability +• TotalA: total asset +• Rf: interest rate +RandFirm_20051.mat: 40 Banks selected in paper. RandFirm_20055.mat: 40 Insurances selected in paper. +Y2009_Sec20018.mat, Y2014_Sec20018.mat, Y2009_Sec20082.mat, Y2014_Sec20082.mat, Y2009_Sec20051.mat, Y2014_Sec20051.mat, Y2009_Sec20055.mat, Y2014_Sec20055.mat OutY2009_Sec20018.xlsx, OutY2009_Sec20051.xlsx, OutY2009_Sec20055.xlsx, OutY2009_Sec20082.xlsx, OutY2014_Sec20018.xlsx, OutY2014_Sec20051.xlsx, OutY2014_Sec20055.xlsx, OutY2014_Sec20082.xlsx + +Output: Table1.png, Table2.png, Table3.png, Table4.png + +Data files: +deltaSD.mat, deltaSD_step3.mat + +Computer system: Processor: Intel(R) Xeon(R) CPU W3530 @ 2.80GHz Installed memory: 8.00 GB Software: MATLAB R2013b From 6a5249253e7fb5cef60483fc82799f9c17946710 Mon Sep 17 00:00:00 2001 From: "Christine W. Wang" Date: Fri, 9 Sep 2016 11:38:48 +0800 Subject: [PATCH 18/21] Delete README[1].md --- XFG_DTD_SMC/README[1].md | 34 ---------------------------------- 1 file changed, 34 deletions(-) delete mode 100644 XFG_DTD_SMC/README[1].md diff --git a/XFG_DTD_SMC/README[1].md b/XFG_DTD_SMC/README[1].md deleted file mode 100644 index fb74f1c..0000000 --- a/XFG_DTD_SMC/README[1].md +++ /dev/null @@ -1,34 +0,0 @@ - -Name of QuantLet: DTD_SMC - -Published in: Applied Quantitative Finance third Edition - -Description: Shows Distance-to-Default (DTD)s using sequential Monte Carlo (SMC) method introduced in the paper. -There are two code files. One is RunSMC_est.m, and the other is TableGenerate.m. In RunSMC_est.m, data from "Input" folder are loaded and all other functions are stored in "subfunciton" folder. The parameters for the initialization sampler are set in funciton SMCsettingbyFirm.m. Then, we sample 1024 particles using the initialization sampler and proceed with the function SMC_Step_MixW.m, which conducts the reweighting and resampling steps. After the resampling step, a function MoveSet_MixW.m runs Metropolis-Hastings (MH) moves. At this stage, the acceptant rate of each block will be displayed in the Command Window. The SMC_Step_MixW.m function will be repeatedly run until it finishesthe likelihood density-tempering for the first 5 firms. Then, it will go back to RunSMC_est.m file. 5 firms are added each time, and re-initialization followed by calling SMC_Step_MixW.m will be conducted again and again until reaching N firms. The following files in "Examples" folder are 12 firms from Airline sector in 2014 and 40 random selected firms from Insurance sector in 2009. OutY2014_Sec20018.xlsx/ Y2014_Sec20018.mat (Time required to estimate: 0.73 hours) OutY2009_Sec20055.xlsx/ Y2009_Sec20055.mat (Time required to estimate: 13.5 hours) -In TableGenerate.m file, Table 1 to Table 4 can be generated from the "Output" data. - -Keywords: Distance-to-Default, credit risk model, sequential Monte Carlo method, density-tempered, expanding-data - -Author: Jin-Chuan Duan and Christine Wei-Ting Wang - -Submitted: - -Input: -Y2009G020018.mat, Y2014G020018.mat, airlines in 2009 and 2014, Y2009G020051.mat, Y2014G020051.mat, banks in 2009 and 2014, Y2009G020055.mat, Y2014G020055.mat, insurance in 2009 and 2014, Y2014G020082.mat, Y2009G020082.mat, engineering&construction in 2009 and 2014. -• Date: 250 daily observations for each firm up to the end of the year -• FirmCode: classified into 76 industry groups by Bloomberg Industry Classification System (BICS) -• Equity: market capitalization -• ShortD: short term debt -• LongD: long term debt -• otherL: other liability -• TotalA: total asset -• Rf: interest rate -RandFirm_20051.mat: 40 Banks selected in paper. RandFirm_20055.mat: 40 Insurances selected in paper. -Y2009_Sec20018.mat, Y2014_Sec20018.mat, Y2009_Sec20082.mat, Y2014_Sec20082.mat, Y2009_Sec20051.mat, Y2014_Sec20051.mat, Y2009_Sec20055.mat, Y2014_Sec20055.mat OutY2009_Sec20018.xlsx, OutY2009_Sec20051.xlsx, OutY2009_Sec20055.xlsx, OutY2009_Sec20082.xlsx, OutY2014_Sec20018.xlsx, OutY2014_Sec20051.xlsx, OutY2014_Sec20055.xlsx, OutY2014_Sec20082.xlsx - -Output: Table1.png, Table2.png, Table3.png, Table4.png - -Data files: -deltaSD.mat, deltaSD_step3.mat - -Computer system: Processor: Intel(R) Xeon(R) CPU W3530 @ 2.80GHz Installed memory: 8.00 GB Software: MATLAB R2013b From c4e19352287396e78aa38a175f5a8cc30ea3e654 Mon Sep 17 00:00:00 2001 From: "Christine W. Wang" Date: Fri, 9 Sep 2016 11:52:25 +0800 Subject: [PATCH 19/21] Add files via upload --- XFG_DTD_SMC/README[1].md | 34 +++++ XFG_DTD_SMC/RunSMC_est[1].m | 239 +++++++++++++++++++++++++++++++++ XFG_DTD_SMC/Table1[1].png | Bin 0 -> 75904 bytes XFG_DTD_SMC/Table2[1].png | Bin 0 -> 84210 bytes XFG_DTD_SMC/Table3[1].png | Bin 0 -> 158509 bytes XFG_DTD_SMC/Table4[1].png | Bin 0 -> 92365 bytes XFG_DTD_SMC/TableGenerate[1].m | 124 +++++++++++++++++ 7 files changed, 397 insertions(+) create mode 100644 XFG_DTD_SMC/README[1].md create mode 100644 XFG_DTD_SMC/RunSMC_est[1].m create mode 100644 XFG_DTD_SMC/Table1[1].png create mode 100644 XFG_DTD_SMC/Table2[1].png create mode 100644 XFG_DTD_SMC/Table3[1].png create mode 100644 XFG_DTD_SMC/Table4[1].png create mode 100644 XFG_DTD_SMC/TableGenerate[1].m diff --git a/XFG_DTD_SMC/README[1].md b/XFG_DTD_SMC/README[1].md new file mode 100644 index 0000000..fb74f1c --- /dev/null +++ b/XFG_DTD_SMC/README[1].md @@ -0,0 +1,34 @@ + +Name of QuantLet: DTD_SMC + +Published in: Applied Quantitative Finance third Edition + +Description: Shows Distance-to-Default (DTD)s using sequential Monte Carlo (SMC) method introduced in the paper. +There are two code files. One is RunSMC_est.m, and the other is TableGenerate.m. In RunSMC_est.m, data from "Input" folder are loaded and all other functions are stored in "subfunciton" folder. The parameters for the initialization sampler are set in funciton SMCsettingbyFirm.m. Then, we sample 1024 particles using the initialization sampler and proceed with the function SMC_Step_MixW.m, which conducts the reweighting and resampling steps. After the resampling step, a function MoveSet_MixW.m runs Metropolis-Hastings (MH) moves. At this stage, the acceptant rate of each block will be displayed in the Command Window. The SMC_Step_MixW.m function will be repeatedly run until it finishesthe likelihood density-tempering for the first 5 firms. Then, it will go back to RunSMC_est.m file. 5 firms are added each time, and re-initialization followed by calling SMC_Step_MixW.m will be conducted again and again until reaching N firms. The following files in "Examples" folder are 12 firms from Airline sector in 2014 and 40 random selected firms from Insurance sector in 2009. OutY2014_Sec20018.xlsx/ Y2014_Sec20018.mat (Time required to estimate: 0.73 hours) OutY2009_Sec20055.xlsx/ Y2009_Sec20055.mat (Time required to estimate: 13.5 hours) +In TableGenerate.m file, Table 1 to Table 4 can be generated from the "Output" data. + +Keywords: Distance-to-Default, credit risk model, sequential Monte Carlo method, density-tempered, expanding-data + +Author: Jin-Chuan Duan and Christine Wei-Ting Wang + +Submitted: + +Input: +Y2009G020018.mat, Y2014G020018.mat, airlines in 2009 and 2014, Y2009G020051.mat, Y2014G020051.mat, banks in 2009 and 2014, Y2009G020055.mat, Y2014G020055.mat, insurance in 2009 and 2014, Y2014G020082.mat, Y2009G020082.mat, engineering&construction in 2009 and 2014. +• Date: 250 daily observations for each firm up to the end of the year +• FirmCode: classified into 76 industry groups by Bloomberg Industry Classification System (BICS) +• Equity: market capitalization +• ShortD: short term debt +• LongD: long term debt +• otherL: other liability +• TotalA: total asset +• Rf: interest rate +RandFirm_20051.mat: 40 Banks selected in paper. RandFirm_20055.mat: 40 Insurances selected in paper. +Y2009_Sec20018.mat, Y2014_Sec20018.mat, Y2009_Sec20082.mat, Y2014_Sec20082.mat, Y2009_Sec20051.mat, Y2014_Sec20051.mat, Y2009_Sec20055.mat, Y2014_Sec20055.mat OutY2009_Sec20018.xlsx, OutY2009_Sec20051.xlsx, OutY2009_Sec20055.xlsx, OutY2009_Sec20082.xlsx, OutY2014_Sec20018.xlsx, OutY2014_Sec20051.xlsx, OutY2014_Sec20055.xlsx, OutY2014_Sec20082.xlsx + +Output: Table1.png, Table2.png, Table3.png, Table4.png + +Data files: +deltaSD.mat, deltaSD_step3.mat + +Computer system: Processor: Intel(R) Xeon(R) CPU W3530 @ 2.80GHz Installed memory: 8.00 GB Software: MATLAB R2013b diff --git a/XFG_DTD_SMC/RunSMC_est[1].m b/XFG_DTD_SMC/RunSMC_est[1].m new file mode 100644 index 0000000..dd70aac --- /dev/null +++ b/XFG_DTD_SMC/RunSMC_est[1].m @@ -0,0 +1,239 @@ +% % Parameter estimation by the density-tempered expanding-data +% sequential Monte Carlo +% Paper: Estimating Distance-to-Default with a Sector-Specific +% Liability Adjustment via Sequential Monte Carlo (Draft: August 12, 2016) + +clear all +randn('state',1); +rand('state',1); +p = parpool(4); % open parralete computing: Maximun 4 workers in Matlab R2013b +addpath('Subfunction') + +SimuFlag = 0; % 0: real data/ 1: simulation data +yearI = 2014; % [2009 2014] +SecCode = 20018; % [20018 20082 20051 20055] + +load(['Input\Y' num2str(yearI) 'G' num2str(SecCode,'%06d') '_c.mat']) +Fname_org = fieldnames(YearData); +Nfiled_org = length(Fname_org); + +% The random selected 40 firms in paper +if SecCode ==2 0051 || SecCode == 20055 + load(['Input\RandFirm_' num2str(SecCode) '.mat'],'FirmCode') + [Irand] = ismember(YearData.FirmCode,FirmCode); + for Iname=1:Nfiled_org + YearData.(Fname_org{Iname})=YearData.(Fname_org{Iname})(:,Irand); + end +end + +% Check data with Missing and sorting correspondingly +SunMiss = sum(isnan(YearData.Equity)); +[Mseq, Miss_sort] = sort(SunMiss); +% delete firms with 250 time series missing data +NaNInd = find(Mseq==250); +StoreDelete= YearData.FirmCode(1,Miss_sort(NaNInd)); +for Iname=1:Nfiled_org + Y.(Fname_org{Iname}) = YearData.(Fname_org{Iname})(:,Miss_sort); + Y.(Fname_org{Iname})(:,NaNInd) = []; +end +[Ydays, NtotFirm] = size( Y.Equity); + +SectorN = size( Y.Equity,2); +% SMCsettingbyFirm: Initilization parameters setting +[ ModelSetting,int_ComParam, int_Param, smcsettings ] = SMCsettingbyFirm( SectorN ); + +% Nrep: # of simulations --for Excel output +Nrep = 1; +ElapsT = zeros(1,Nrep,'double'); +OE = 2; +n0 = 0; + +% Step1. Initialization sampling: Common variables +for i=smcsettings.Com_Idx +theta.(smcsettings.Fname{i}) = InitSim_theta(smcsettings.Nsim, ... + int_ComParam.m, int_ComParam.s.^(1/2), int_ComParam.dist, int_ComParam.bound); +% density of initialization sampler for common parmameter + [ lnpdf_int(:,1)] = oglikelihood_density( theta.(smcsettings.Fname{i}),... + int_ComParam.m, diag(int_ComParam.s),1,0); +end + +Int_mean = zeros(ModelSetting.Ni); +Int_var = zeros(ModelSetting.Ni); +Nf = 0; + +for CountG = 1:smcsettings.Gnum + + begT = tic; + + % add Firms from Nf to Ns + Nf = smcsettings.MoveBlocks(CountG+1).moveindex(1)-1; % # of firms Finish SMC + Ns = smcsettings.MoveBlocks(CountG+1).moveindex(end); + disp(['add company to ' num2str( Ns )]) + Y_exp = ExtractData( Y, 1, Ns, ModelSetting.N ); + + % Step1. Initialization sampling: Individual variables + if CountG == 1 + + Int_data = zeros(smcsettings.Nsim, ModelSetting.Ni, Ns); + % only sample first Group partameters + for Gi = smcsettings.MoveBlocks(CountG+1).moveindex + Int_data(:,:,Gi) = InitSim_theta(smcsettings.Nsim, int_Param(Gi).m' ,... + (int_Param(Gi).s).^(1/2)', int_Param(Gi).dist, int_Param(Gi).bound ); + % log likelihood of initialization sampler + [ lnpdf_int(:,Gi+1)] = loglikelihood_density(... + Int_data(:,:,Gi),int_Param(Gi).m', diag(int_Param(Gi).s), 1,1); + end + % Initial theta enviroment + for i=1:ModelSetting.Ni + Iname = smcsettings.Ind_Idx(i); + theta.(smcsettings.Fname{Iname}) = ... + squeeze(Int_data(:,i,smcsettings.MoveBlocks(CountG+1).moveindex )); + end + + else % Step5. Adding more firms + Int_mean = zeros(ModelSetting.Ni,Nf); + Int_var = zeros(ModelSetting.Ni,Nf); + + for i=1:ModelSetting.Ni + Iname = smcsettings.Ind_Idx(i); + Int_mean(i,:) = mean( theta.(smcsettings.Fname{Iname})(:,1:Nf)) ; + Int_var(i,:) = var( theta.(smcsettings.Fname{Iname})(:,1:Nf) ) ; + Int_data(:,i,:) = squeeze( theta.(smcsettings.Fname{Iname}) ); + % Theta(i) regress on delta + for Gi=2:Nf + TmpReg = ols(theta.(smcsettings.Fname{Iname})(:,Gi), [ones(smcsettings.Nsim,1)... + theta.(smcsettings.Fname{smcsettings.Com_Idx})]); + int_Param(Gi).Coeff(i,:)= TmpReg.beta; + end + end + + % Re-Initialization: Resample Common Variable : + for i= smcsettings.Com_Idx + % Mix density funciton + % I_s ~ N(m,s) + int_ComParam.m(1,2) = mean(theta.(smcsettings.Fname{i}),1); + int_ComParam.s(1,2) = var(theta.(smcsettings.Fname{i}) ); + + IndM = random('bino',1,1-smcsettings.MixW,[smcsettings.Nsim ,1])==1; + % ( smcsettings.MixW )% generate from CountG result + theta.(smcsettings.Fname{i})(IndM==0) = InitSim_theta(sum(IndM==0), ... + int_ComParam.m(2), int_ComParam.s(2)^(1/2), int_ComParam.dist,... + int_ComParam.bound); + % ( 1-smcsettings.MixW )% generate from Initial setting I_0 + theta.(smcsettings.Fname{i})(IndM) = InitSim_theta(sum(IndM), ... + int_ComParam.m(1), int_ComParam.s(1)^(1/2), int_ComParam.dist,... + int_ComParam.bound); + + [ lnpdf_int(:,i),int_ComParam.tc] = loglikelihood_density(... + theta.(smcsettings.Fname{i}),int_ComParam.m,... + int_ComParam.s , 1,1, smcsettings.MixW, []); + + end + % adding firms + Int_data = cat(3,Int_data,zeros(smcsettings.Nsim, ModelSetting.Ni, Ns-Nf) ); + + for Gi=1:Ns + % Re-Initialization: Resample Individual variables + if Gi<=Nf + if Gi==1 % (1) Initialization + int_Param(Gi).m = Int_mean(:,Gi)'; + Mean_Fi = repmat(int_Param(Gi).m,smcsettings.Nsim,1); + int_Param(Gi).s = cov(bsxfun(@minus,Int_data(:,:,Gi),Mean_Fi)); + % Resample Initial theta from Trucated MultiNormal + % (beta1>0) + Int_data(:,:,Gi) = SimProposalMN2(Mean_Fi,int_Param(Gi).s,[], 1,1,... + int_Param(Gi).bound(1),int_Param(Gi).bound(2)); + else % (2:Nf) Initialization + Mean_Fi =[ones(smcsettings.Nsim,1) theta.(smcsettings.Fname{smcsettings.Com_Idx})]... + *int_Param(Gi).Coeff'; + int_Param(Gi).m = mean(Mean_Fi); + int_Param(Gi).s = cov(bsxfun(@minus,Int_data(:,:,Gi),Mean_Fi )); %* ModelSetting.adjK; + % Resample Initial theta from MultiNormal + Int_data(:,:,Gi) = SimProposalMN2(Mean_Fi,int_Param(Gi).s,[], 1,0,[],[]); + end + [ lnpdf_int(:,Gi+1)] = loglikelihood_density(... + Int_data(:,:,Gi),int_Param(Gi).m', int_Param(Gi).s, 0,1); + else % Expanding data (Nf+1:Ns) Initialization + + % Generate Initial theta from iid Normal + Int_data(:,:,Gi) = InitSim_theta(smcsettings.Nsim, int_Param(Gi).m' ,... + int_Param(Gi).s'.^(1/2), int_Param(Gi).dist, int_Param(Gi).bound ); + + % log likelihood of initialization sampler + [ lnpdf_int(:,Gi+1)] = loglikelihood_density(... + Int_data(:,:,Gi),int_Param(Gi).m', diag(int_Param(Gi).s), 1,1); + end + end + + for i=1:ModelSetting.Ni + Iname = smcsettings.Ind_Idx(i); + theta.(smcsettings.Fname{Iname}) = squeeze(Int_data(:,i,:)); + end + + end + + % evaluate log likelihood at initial parameters: smcsettings.fun + %%%% initial likelihood + [lnCL_addD] = feval(smcsettings.fun, theta, Y_exp,1 ); + + gamma=0; + lnw = zeros(smcsettings.Nsim,1); + t=1; + reslt(CountG).runtime(t)=toc(begT); + + while gamma < 1 + + begT = tic; + %%%%%% SMC step %%%%%%% + [reslt(CountG).ESS(t),reslt(CountG).normfac(t),reslt(CountG).AcceptRate(t,:),... + theta,lnCL_addD,lnw,lnpdf_int,gamma,smcsettings] = SMC_Step_MixW(theta,... + lnCL_addD,lnw,lnpdf_int,int_ComParam, int_Param,gamma,... + Y_exp,smcsettings,CountG); + + Xeven = theta; + + for Iname = 1:smcsettings.nameN + if ismember(Iname,smcsettings.Com_Idx); + reslt(CountG).(smcsettings.Fname{Iname})(t,:)=... + [mean(Xeven.(smcsettings.Fname{Iname})) min(Xeven.(smcsettings.Fname{Iname}))... + max(Xeven.(smcsettings.Fname{Iname})) prctile(Xeven.(smcsettings.Fname{Iname}),5) ... + prctile(Xeven.(smcsettings.Fname{Iname}),95) ... + mean(Xeven.(smcsettings.Fname{Iname})) - std(Xeven.(smcsettings.Fname{Iname}))*1.96 ... + mean(Xeven.(smcsettings.Fname{Iname})) + std(Xeven.(smcsettings.Fname{Iname}))*1.96]; + else + % Uncommon parameters + reslt(CountG).(smcsettings.Fname{Iname}).m(t,:)=... + [mean(Xeven.(smcsettings.Fname{Iname})) ... + min(Xeven.(smcsettings.Fname{Iname})) ... + max(Xeven.(smcsettings.Fname{Iname})) ]; + reslt(CountG).(smcsettings.Fname{Iname}).prc(t,:)=... + [prctile(Xeven.(smcsettings.Fname{Iname}),5) prctile(Xeven.(smcsettings.Fname{Iname}),95)]; + reslt(CountG).(smcsettings.Fname{Iname}).ci(t,:)=... + [mean(Xeven.(smcsettings.Fname{Iname})) - std(Xeven.(smcsettings.Fname{Iname}))*1.96 ... + mean(Xeven.(smcsettings.Fname{Iname})) + std(Xeven.(smcsettings.Fname{Iname}))*1.96]; + end + end + + reslt(CountG).runtime(t+1) = toc(begT); + + t=t+1; + disp([ 'gamma steps:' num2str(t-1)]); + end + + %store full-sample posterior + reslt(CountG).Xeven=Xeven; + + bridgeN(1,CountG) = t-1; + + outExcel=[reslt(CountG).weight(t-1,:);reshape([reslt(CountG).mu.m(t-1,:) reslt(CountG).mu.prc(t-1,:) reslt(CountG).mu.ci(t-1,:)],Ns,7);... + reshape([reslt(CountG).beta.m(t-1,:) reslt(CountG).beta.prc(t-1,:) reslt(CountG).beta.ci(t-1,:)],Ns,7);... + reshape(exp([reslt(CountG).nu.m(t-1,:) reslt(CountG).nu.prc(t-1,:) reslt(CountG).nu.ci(t-1,:)]),Ns,7)]; + xlswrite(['Examples\OutY' num2str(yearI) '_Sec' num2str(SecCode) '.xls'],outExcel,1,sprintf('B%d',OE)) + xlswrite(['Examples\OutY' num2str(yearI) '_Sec' num2str(SecCode) '.xls'],reslt(CountG).runtime,1,sprintf('M%d',CountG+1)) + OE = OE+size(outExcel,1)+1; + +end +save(['Examples\Y' num2str(yearI) '_Sec' num2str(SecCode) '.mat']) +disp(['Firms without any data available: ' num2str(StoreDelete)]) + +delete(p) diff --git a/XFG_DTD_SMC/Table1[1].png b/XFG_DTD_SMC/Table1[1].png new file mode 100644 index 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zePeaKkV`~y)b>&P@Q`nXR#_`+UU>7O31tf!u3V1!ypnng_A|>SngtmC$1!PXHNg~T&iMd9J{WA3H%m&XVrWyFhToqd5AR<0%7oQhD3oX7} z5QK952Pz56cSjPP9p6a?i8?GC{tBj5#%M33c--sIw$%H*F3Of9a{KGxS4FMJ{kE3jBfqE1KI!6RT=-k@cDm#_4f$;KWU7A u57WPg>Ax^r{|MoDq)#-j-5s8}d-!{V Date: Fri, 9 Sep 2016 11:54:16 +0800 Subject: [PATCH 20/21] Create CRI_data --- XFG_DTD_SMC/CRI_data | 1 + 1 file changed, 1 insertion(+) create mode 100644 XFG_DTD_SMC/CRI_data diff --git a/XFG_DTD_SMC/CRI_data b/XFG_DTD_SMC/CRI_data new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/XFG_DTD_SMC/CRI_data @@ -0,0 +1 @@ + From 1e1507812f221b352e956706fc119245251beab7 Mon Sep 17 00:00:00 2001 From: "Christine W. Wang" Date: Fri, 9 Sep 2016 11:54:32 +0800 Subject: [PATCH 21/21] Delete CRI_data --- XFG_DTD_SMC/CRI_data | 1 - 1 file changed, 1 deletion(-) delete mode 100644 XFG_DTD_SMC/CRI_data diff --git a/XFG_DTD_SMC/CRI_data b/XFG_DTD_SMC/CRI_data deleted file mode 100644 index 8b13789..0000000 --- a/XFG_DTD_SMC/CRI_data +++ /dev/null @@ -1 +0,0 @@ -