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step6_alternativeK.m
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%% This script generates the results in Table 6 in the manuscript
% Created 08 Jul 2021, 09:35 BST.
% Script last revised 21 Nov 2022
% @author: Arman Hassanniakalager GitHub: https://github.com/hkalager
% Common disclaimers apply. Subject to change at all time.
clear;
clc;
if ispc
act_fld=[pwd,'\'];
addpath([act_fld,'\Dataset_ETFS']);
addpath([act_fld,'\kfwe']);
elseif ismac
act_fld=[pwd,'/'];
addpath([act_fld,'/Dataset_ETFS']);
addpath([act_fld,'/kfwe']);
end
%tickerlistaa={'SPY','QQQ','SHV','LQD','GLD','USO'};
main_ticker={'SPY','QQQ','GLD','USO'};
loss_b_range=[-5,-2,0,1];
Benchmark={'GARCH','GJR-GARCH','HAR'};
cases_list={'One Piece','High Stress','Low Stress'};
freq=5; %mins
%oos_period_range_end=oos_period_range_test+oos_per-1;
try
Spec_data=load('RV_Pool_325_Spec_tbl.mat');
catch
record_mdlspec;
Spec_data=load('RV_Pool_325_Spec_tbl.mat');
end
Bsize=1000;
Bwindow=10;
%% Study period
oos_period_range_test=2014:2020;
%% Liang specification
Max_lambda=0.95;
N_bins=20;
gamma_range=.05:.05:.95;
%% FDR setting
fdrtarget=0.1;
rng(0);
%% Setting for RSW
gamma_rsw=.1;
%% The benchmark indexes
bench_ind=zeros(size(Benchmark));
family_class=Spec_data.Mdl_Class;
for s=1:numel(bench_ind)
sel_bench=Benchmark{s};
idx_bench=find(strcmp(family_class,sel_bench));
bench_ind(s)=idx_bench(1);
end
perf_table=table();
iter=0;
for IS_per=[66,126]
disp(['Calculations started for case of using past ',num2str(IS_per),' days ...']);
for t=1:numel(main_ticker)
flname=[act_fld,main_ticker{t},'_Pool_M',num2str(freq),...
'_OOS_2014_2020_',num2str(IS_per),'.mat'];
load(flname,'oosdate','oos_ser','TF1SMP','TF2SMP','tbl0');
oosdate_red=oosdate;
oosdate_red(weekday(oosdate_red)==1)=[];
disp(['Calculations started for ',main_ticker{t},' ...']);
oos_ser(oos_ser>.01)=.01;
oos_ser(oos_ser<1e-8)=1e-8;
oos_ser(isnan(oos_ser))=.01;
oos_ser_tested=oos_ser;
modelscount=size(oos_ser_tested,2);
oos_idx_range=1:size(oos_ser_tested,1);
target_idx=oos_idx_range+TF1SMP-1;
poolset_ser=oos_ser_tested(oos_idx_range,:);
target=tbl0{target_idx,'RVDaily'};
%sigma2=tbl0{TF1SMP+poolsetind(1)-1:TF1SMP+poolsetind(end)-1,4+voltyp};
indices=stationary_bootstrap((1:size(poolset_ser,1))',Bsize,Bwindow);
Perf=zeros(1,modelscount);
Perf_B=zeros(Bsize,modelscount);
for l=1:numel(loss_b_range)
tic;
iter=iter+1;
disp(['Case robust loss b = ',num2str(loss_b_range(l)),' ...']);
perf_table{iter,'Asset'}=main_ticker(t);
perf_table{iter,'K'}={IS_per};
perf_table{iter,'Robust_b'}=loss_b_range(l);
[Perf,loss_ser]=robust_loss_fn(poolset_ser,target,loss_b_range(l));
for b=1:Bsize
bsdata=poolset_ser(indices(:,b),:);
bstarget=target(indices(:,b),:);
Perf_B(b,:)=robust_loss_fn(bsdata,bstarget,loss_b_range(l));
end
% MCS test first
[INCLUDEDR] = mcs(loss_ser,fdrtarget,Bsize,Bwindow) ;
perf_table{iter,'MCS_included'}=numel(INCLUDEDR);
bucket_mcs=poolset_ser(:,INCLUDEDR);
bucket_mcs_mse=mean(abs(robust_loss_fn(bucket_mcs,target,0)),'omitnan');
perf_table{iter,'MCS_bucket_MSE'}=bucket_mcs_mse;
bucket_mcs_qlike=mean(abs(robust_loss_fn(bucket_mcs,target,-2)),'omitnan');
perf_table{iter,'MCS_bucket_QLIKE'}=bucket_mcs_qlike;
disp(['Number of included models by MCS is ', num2str(numel(INCLUDEDR))]);
for bi=1:numel(Benchmark)
Bench_Perf=Perf(bench_ind(bi));
Bench_Ser=poolset_ser(:,bench_ind(bi));
[~,maxind]=max(Perf);
Bench_Perf_B=Perf_B(:,bench_ind(bi));
pvalues=mypval(Bench_Perf-Perf',(Perf_B-Perf));
try
[pi_0hat,lambda]=est_pi0_disc(pvalues, N_bins,Max_lambda);
catch
pi_0hat=1;
end
%pi_0hat=max(pi_0hat,.5);
opt_gamma=gamma_finder(Bench_Perf-Perf',pvalues,gamma_range,pi_0hat);
[pi_aplushat, pi_aminushat] = compute_pi_ahat(pvalues, Bench_Perf-Perf', pi_0hat, opt_gamma);
[PORTFDR, FDRhat] = my_portfolio_FDR_mod(fdrtarget, Bench_Perf-Perf', pvalues, pi_0hat);
% The unlilely case where a benchmark has the best
% performance
PORTFDR=(FDRhat~=2).*PORTFDR;
lbl_column_fdr=['FDR_',Benchmark{bi}];
perf_table{iter,lbl_column_fdr}=sum(PORTFDR);
disp(['Number of significant models with FDR and benchmark ',...
Benchmark{bi},' is ', num2str(sum(PORTFDR))]);
if sum(PORTFDR)==0
PORTFDR(Benchmark{bi})=1;
end
bucket_fdr=poolset_ser(:,PORTFDR==1);
bench_MSE_lbl=[Benchmark{bi},'_MSE'];
bench_mse_val=abs(robust_loss_fn(Bench_Ser,target,0));
perf_table{iter,bench_MSE_lbl}=bench_mse_val;
bench_qlike_lbl=[Benchmark{bi},'_QLIKE'];
bench_qlike_val=abs(robust_loss_fn(Bench_Ser,target,-2));
perf_table{iter,bench_qlike_lbl}=bench_qlike_val;
lbl_column_fdr_bucket_MSE=['FDR_',Benchmark{bi},'_bucket_MSE'];
bucket_fdr_mse=mean(abs(robust_loss_fn(bucket_fdr,target,0)),'omitnan');
perf_table{iter,lbl_column_fdr_bucket_MSE}=bucket_fdr_mse;
lbl_column_fdr_bucket_QLIKE=['FDR_',Benchmark{bi},'_bucket_QLIKE'];
bucket_fdr_qlike=mean(abs(robust_loss_fn(bucket_fdr,target,-2)),'omitnan');
perf_table{iter,lbl_column_fdr_bucket_QLIKE}=bucket_fdr_qlike;
% RSW-FDP Test
k_rsw=1;
reject_set_rsw=kfwe(Bench_Perf-Perf,(Perf_B-Perf),k_rsw,fdrtarget,modelscount);
while numel(reject_set_rsw)>=(k_rsw/gamma_rsw-1)
k_rsw=k_rsw+1;
reject_set_rsw=kfwe(Bench_Perf-Perf,(Perf_B-Perf),k_rsw,fdrtarget,modelscount);
end
lbl_column_rsw=['KStepM_',Benchmark{bi}];
perf_table{iter,lbl_column_rsw}=numel(reject_set_rsw);
disp(['Number of significant models with kStepM and benchmark ',...
Benchmark{bi},' is ', num2str(numel(reject_set_rsw))]);
if numel(reject_set_rsw)==0
reject_set_rsw=bench_ind(bi);
end
bucket_rsw=poolset_ser(:,reject_set_rsw);
lbl_column_rsw_bucket_MSE=['KStepM_',Benchmark{bi},'_bucket_MSE'];
bucket_rsw_mse=mean(abs(robust_loss_fn(bucket_rsw,target,0)),'omitnan');
perf_table{iter,lbl_column_rsw_bucket_MSE}=bucket_rsw_mse;
lbl_column_rsw_bucket_QLIKE=['KStepM_',Benchmark{bi},'_bucket_QLIKE'];
bucket_rsw_qlike=mean(abs(robust_loss_fn(bucket_rsw,target,-2)),'omitnan');
perf_table{iter,lbl_column_rsw_bucket_QLIKE}=bucket_rsw_qlike;
end
toc;
end
end
end
fl_lbl=['Quantify_',num2str(oos_period_range_test(1)),...
'_',num2str(oos_period_range_test(end)),'_M',num2str(freq),...
'_multiK.csv'];
writetable(perf_table,fl_lbl);