File Exchange

image thumbnail

Systemic Risk

version 1.6.8 (2.3 MB) by Tommaso Belluzzo
A MATLAB framework for systemic risk valuation and analysis.

35 Downloads

Updated 01 Sep 2019

GitHub view license on GitHub

# INTRODUCTION #

This script calculates and analyses the following returns-based systemic risk measures:

> CoVaR and Delta CoVaR (Conditional Value-at-Risk) proposed in Adrian & Brunnermeier (2008)
https://doi.org/10.2139/ssrn.1269446
> MES (Marginal Expected Shortfall) proposed in Acharya et al. (2010)
https://doi.org/10.2139/ssrn.1573171
> SRISK (Conditional Capital Shortfall Index) proposed in Brownlees & Engle (2010)
https://doi.org/10.2139/ssrn.1611229
> Connectedness Measures proposed in Billio et al. (2011)
https://doi.org/10.2139/ssrn.1963216
> Spillover Measures proposed in Diebold & Yilmaz (2014)
https://doi.org/10.1016/j.jeconom.2014.04.012

Some of the aforementioned models have been adjusted and improved according to the methodologies described in the V-Lab Documentation (https://vlab.stern.nyu.edu/docs), which represents a great hub for systemic risk measurement.

# USAGE #

1) Create a properly structured database (see the paragraph below).
2) Edit the "run.m" script following your needs.
3) Execute the "run.m" script.

# DATASET #

Datasets must be built following the structure of default ones included in every release of the framework (see "Datasets" folder). The main one ("Datasets\Example_Large.xlsx"), based on the US financial sector, defines the following entities over a period of time ranging from 2000 to 2014:

> Benchmark: S&P 500
> Financial Institutions (20):
=> Group 1: Insurance Companies (5)
==> American International Group Inc. (AIG)
==> The Allstate Corp. (ALL)
==> Berkshire Hathaway Inc. (BRK)
==> MetLife Inc. (MET)
==> Prudential Financial Inc. (PRU)
=> Group 2: Investment Banks (6)
==> Bank of America Corp. (BAC)
==> Citigroup Inc. (C)
==> The Goldman Sachs Group Inc. (GS)
==> J.P. Morgan Chase & Co. (JPM)
==> Lehman Brothers Holdings Inc. (LEH)
==> Morgan Stanley (MS)
=> Group 3: Commercial Banks (7)
==> American Express Co. (AXP)
==> Bank of New York Mellon Corp. (BK)
==> Capital One Financial Corp. (COF)
==> PNC Financial Services Inc. (PNC)
==> State Street Corp. (STT)
==> US Bancorp (USB)
==> Wells Fargo & Co. (WFC)
=> Group 4: Government-sponsored Enterprises (2)
==> Federal Home Loan Mortgage Corp / Freddie Mac (FMCC)
==> Federal National Mortgage Association / Fannie Mae (FNMA)
> State Variables (6):
=> RESI: the DJ US Select RESI as a proxy of real estate returns.
=> VIX: the implied volatility index.
=> TBR3M: the 3M treasury bill rate.
=> CRESPR: the change in the credit spread (the BAA corporate bond rate minus the 10Y treasury bond rate).
=> LIQSPR: the change in the liquidity spread (the 3M treasury bill rate minus the federal funds rate).
=> YIESPR: the change in the yield spread (the 10Y treasury bond rate minus the 3M treasury bond rate).

# NOTES #

> Financial time series must contain the benchmark index and at least 3 firms. They must be based on a daily frequency and contain enough observations to run consistent calculations (a minimum of 253 observations, which translates into a full business year plus an additional observation at the beginning). They must have been previously validated and preprocessed by: discarding illiquid series with too many zeroes (unless necessary), detecting and removing outliers, removing rows with NaNs or filling the gaps through interpolation.
> Returns must be expressed on a logarithmic scale, in accordance with all the systemic risk indicators.
> Market capitalizations and total liabilities must be expressed in the same currency. Following the SRISK methodology, the latter must be rolled forward by at least 3 months in order to simulate the difficulty of renegotiating debt in case of financial distress.
> Data concerning state variables and firm groups are optional, hence their respective sheets must be removed from the dataset if the related computations aren't necessary. Groups are based on key-value pairs where the Name field represents the group names and the Count field represents the number of firms to include in the group. The sum of the Count fields must be equal to the number of firms included in the dataset.
> While stochastic measures are very fast to compute, for huge datasets like Datasets\Example_Large.xlsx connectedness and spillover measures may take very long time to finish. The performance of computations may vary from machine to machine, depending on the CPU processing speed and the number of cores available for parallel computing.

Cite As

Tommaso Belluzzo (2019). Systemic Risk (https://www.github.com/TommasoBelluzzo/SystemicRisk), GitHub. Retrieved .

Comments and Ratings (57)

xudong liu

Download the the new toolboxs
thank you

yours xudong
English name: Daniel Tulip liu
China

This is due to a problem concerning the function "mfilename", which does not work well when the package is executed as a live script. A temporary solution would be to move all the m files in the subfolders at root level, where the "run.m" script is located. I'm working on a quick fix.

@Devon Leukes: Did you find a solution? I think I've the same error in the run.m script.

It states:

Undefined function 'parse_dataset' for input arguments of type 'char'.

Error in Untitled (line 31)
data = parse_dataset(fullfile(path_base,path_dset));

I simply copied Tomasso's newest code and then ran it.

If any others might have an idea, you're more than welcome!

The entire run.m:

warning('off','all');

close('all');
clearvars();
clc();
delete(allchild(0));

data = xlsread('Example.xlsx')

[path_base,~,~] = fileparts(mfilename('fullpath'));

if (~strcmpi(path_base(end),filesep()))
path_base = [path_base filesep()];
end

paths_base = genpath(path_base);
addpath(paths_base);

path_dset = strrep('Datasets\Example_Large.xlsx','\',filesep());

path_tpro = strrep('Templates\TemplatePRO.xlsx','\',filesep());
file_tpro = fullfile(path_base,path_tpro);
path_rpro = strrep('Results\ResultsPRO.xlsx','\',filesep());
file_rpro = fullfile(path_base,path_rpro);

path_tnet = strrep('Templates\TemplateNET.xlsx','\',filesep());
file_tnet = fullfile(path_base,path_tnet);
path_rnet = strrep('Results\ResultsNET.xlsx','\',filesep());
file_rnet = fullfile(path_base,path_rnet);

data = parse_dataset(fullfile(path_base,path_dset));

main_pro(data,file_tpro,file_rpro,0.95,0.40,0.08,true);
pause(2);
main_net(data,file_tnet,file_rnet,0.05,true,true);

save('data.mat','data');

rmpath(paths_base);

Hi George, sure it will.

Hi Tommaso,

Will this work with annual frequency in Total Liabilities? (I mean daily frequency but rolled forward by 12 months instead of 3)

Thank you,
George

Dear XuDong, I don't really remember how I came up with that small parfor in adjacency matrix calculations. At the time I was developing network measures, it probably seemed a good performance tweak after digging into community forums and reading the technical documentations, At present, with new MATLAB released meanwhile, it is probably not anymore the case. If you think that it is not optimizing the performance, or even worsening it, it should not be very difficult to remove it.

xudong liu

Dear Tommaso
I have some question for your code .Your program is very perfect.
two parfor loop question.
One: dcc_gjrgarch.m code line 68,start a parpool local work automatic;this i can understand
Two:main_net call the funcion file calculate_adjacency_matrix. code line 36 ,a parfor loop Starting parallel pool (cluster) automatic ,for this code i con`t understand it can start parpool cluster .Can you tell me the technology detail.
This is my last question,thanks for anser .
Thank you very much
Yours Xudong Chongqing,China

You should upload your dataset somewhere and share it with me. Because I cannot see what's going on with yours with just a stack trace.

teng

Dear Tommaso, I can run your dataset. But when using my dataset, I cannot get the results. I also get the following errors:
Error using calculate_covar (line 32)
The value 'svars' is invalid.The required input should be limited.

Error in main_pro>main_pro_internal (line 67)
[covar,dcovar] = calculate_covar(ret0_m,ret0_x,var_x,data.A,data.StVarsLag);

Error in main_pro (line 34)
main_pro_internal(ip_res.data,res,ip_res.k,ip_res.d,ip_res.l,ip_res.anl);

Error in covar (line 21)
main_pro(data,fullfile(path_base,path_rpro),0.95,0.40,0.08,true);

xudong liu

xudong liu

Thanks a lot

Dear XuDong, this script comes from my master of science thesis and it's written in italian language. The papers you're probably looking for are the following ones:
- CoVaR/ΔCoVaR (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1269446)
- MES (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1573171)
- SRISK (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1611229)
- Network Measures (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1963216)

xudong liu

Dear Tommaso ,we can run your toolbox ,can generate two excel file ,and graph.but i an my frend can not understand your result.can you give a link your work paper?so wo can understand your code calculate for . A Chines guy,Daniel tulip lew.my email: xudongliu520@vip.qq.com. thank you for your anser.

Dear George, dates should be in the format "dd/MM/yyyy". Probably, your local Windows settings are forcing Excel to use another format that the script can somehow handle but that produces errors in the long run. You could try to force your own date format in the parsing function or, alternatively, force Excel to use that format. I'm gonna fix this by allowing users to specify their own format, but this will be ready in no less than two days.

Thank you Tommaso. As I said, I'm using your dataset for now. I managed to partly solve it by formatting each A column as Date on excel but I still have the same issue with State Variables, even if I copy and paste the column A from Market Capitalization to State Variables. Without State Variables and Groups, I get the ResultsPro but not the ResultsNet and no plots. I also get the following errors:

Error using matlab.graphics.axis.Axes/set
While setting the 'XLim' property of Axes:
Value must be a 1x2 vector of numeric type in which the second element is larger than the first and may be Inf

Error in main_pro>plot_index (line 189)
set(sub_1,'XLim',[data.DatesNum(1) data.DatesNum(end)],'YLim',[(min(data.IdxRet) - 0.01) (max(data.IdxRet) + 0.01)]);

Error in main_pro_main_pro_internal (line 94)
plot_index(data);

Error in main_pro (line 34)
main_pro_internal(ip_res.data,res,ip_res.k,ip_res.d,ip_res.l,ip_res.anl);

Error in run (line 22)
main_pro(data,fullfile(path_base,path_rpro),0.95,0.40,0.08,true);

Dear George, as per script requirements, "market capitalizations must contain a supplementar observation at the beginning because a one-day lagged version is used in order to calculate weighted averages of probabilistic measures". That error is being thrown because your time series have either a totally mismatching time frame or because (more likely) the first date of the market capitalizations table is not less than the first date of the returns table. Also, a check on your current datetime format may detect another possible reason for this error to occur. Feel free to comment again if you are still experiencing problems.

Hi Tommaso, Thank you for this important contribution. I'm using Matlab R2015a and unfortunately I get the following errors when using your dataset:

Error using parse_dataset_parse_dataset_internal (line 106)
The 'Returns' table and the 'Market Capitalization' table observation dates are mismatching.

Error in parse_dataset (line 19)
data = parse_dataset_internal(res.file);

Error in run (line 20)
data = parse_dataset(fullfile(path_base,path_dset));

Dear Teng, the script has been created for performing the calculations using the maximum available data granularity. As far as I can remember, all the systemic risk indicators are based on daily frequencies. You tell me whether your purpose can be fulfilled and I answer you it can totally be. Try to run the script on a dataset with an increased frequency and see if it can finish smoothly... because it may require several adjustments.

teng

Dear Tommaso,thank you for your answer.Is it possible to modify the program to process the weekly financial time series? If so, how?

Dear Teng, the sign of value-at-risk models output is always likely to be negative since they represent losses occurring at -N standard deviations from the mean, where N is a value determined by the chosen confidence level. Of course, CoVaR makes no exception to this rule, the paper is correct. This being said, however, it's a common practice to reverse the sign of VaR results turning them into positive values (this is what my script does) because it's easier to compare them against other risk measures or metrics. There is also a "semantic" reason behind this behavior: when a loss of, let's say, 10$ occurs... you say you lost 10$, you don't say you lost -10$.

teng

Dear Tommaso, Why is CoVaR,DCoVaR positive but in the paper, Adrian & Brunnermeier (2009), it's negative?

xudong liu

Dear Belluzzo ,thank you for answer,now the program can run .

Dear Liu, did you perhaps changed the format of your dates in the Excel spreadsheet? Another possible cause of this issue is that you are using a different regional settings and therefore Excel products differently formatted dates when parsed. You may try to play a little bit with the "InputFormat" parameter until you find the one that is suitable for your needs.

xudong liu

错误使用 datetime (line 593)
无法使用 'dd/MM/yyyy' 格式解析日期/时间字符串。

出错 parse_dataset>parse_table (line 219)
res.Date = datetime(res.Date,'InputFormat','dd/MM/yyyy');

出错 parse_dataset>parse_dataset_internal (line 71)
rets = parse_table(file,1,'Returns');

出错 parse_dataset (line 19)
data = parse_dataset_internal(res.file);

出错 run (line 20)
data = parse_dataset(fullfile(path_base,path_dset));

Dear Jasper, sorry for the late reply. Yes, my algorithm uses the quantile regression and yes, it does make use of a GARCH method for the volatility computation. To be more specific, it uses a DCC-GJRGARCH. The code is open source and you can browse it on GitHub if necessary. Regards.

Jasper Lim

Hi Tommaso, does your code use quantile regression, or DCC multivariate GARCH to compute CoVaR? I believe that is the reason for my confusion as the quantile regression method proposed by Adrian and Brunnermeier requires state variables to generate time-varying delta-CoVaR. Thank you for your clarification.

@Jasper: I think we are misunderstanding each other. What do you mean when you say the measure is "time-varying"? Were you expecting to obtain a single Delta-CoVaR value when removing the state variables? State variables are just an instrument for improving the capability of the model to capture the time-varying risk profile of the economic contingency, nothing else. If you take a look at the "calculate_covar.m" script, you will see that state variables are not taken into account if they are omitted in the datased, but a time projection of the risk index is performed anyway.

@Michalis: hi and thanks for your feedback. I know the script is very computationally intensive (especially when network measures are being calculated) and I did my best, over the past years, to improve the performace. Halas, I didn't overcome the problem when dealing with big datasets. If you have any proposal, I'd be glad to hear them.

Salvo Tommasso, this briliant contribution, i wonder whether you have attempted to re-produce the analysis with a lot more counterparties or/and more state variables. I have attempted to enlarge the dataset. This certainly pushes the limits of computation and i wonder whether you thought of ways to proxy the equations and computations involved using different techniques? Crazie Mille.

Jasper Lim

Thank you Tommaso for your answers. I also realised that your code was able to generate time-varying delta-CoVaR even when I removed state variables from my dataset. May I know if this is a mistake? From my knowledge, only unconditional CoVaR and Delta-CoVaR can be computed without state variables according to the original CoVaR paper by Adrain and Brunnermeier.

Dear Jasper, the readme file was a little bit outdated. Actually, that statement is wrong: all you need is a benchmark and at least 3 firms as you can see at line 73 of "parse_dataset.m" in the current release. I fixed this small error, thanks for the feedback.

For what concerns the state variables, this is more of an econometric question. Actually, their purpose is to capture risk factors over time and provide a better estimate of the relation between the financial institutions you want to analyze. The more you provide, as long as they are meaningful in describing the economic/financial context, the better it is. Of course, an increase of their number increases the overall computation complexity and time.
The best suggestion I can give is to pick a number of state variables between 3 and 6, sticking on what other academic papers normally use.

Jasper Lim

Hi Tommaso, in the readme file it is stated that a minimum of 5 firms is required. May I enquire if that refers to 5 firms per group or a total of 5 firms in the dataset? Also, is there a minimum number of state variables required for the computations to be meaningful?

Hi Devon, after reviewing my code, I confirm everything works as expected at least on my machine. In order to run a meaningful test, I downloaded a brand new release package directly from GitHub (https://github.com/TommasoBelluzzo/SystemicRisk) and after launching the run script everything was correctly processed.

In order to help you out, I need more details about your current setup. Are you under Windows OS? Have you tried to evaluate the result of "fullfile(path_base,path_dset)" in run.m at line 20? Does the path points to a valid existing file?

Hi Devon, as far as I can see, the script can't find the dataset you are targeting. Let me check if something wrong happened with my last update. Otherwise, I'll assist you in debugging this issue.

Hi Tommaso, I've downloaded your code as is (no modifications) and running it in MATLAB R2017b. Running the run.m script I get the following errors:
-------------------------------------------------------------------------------------------------
Error using parse_dataset>parse_dataset_internal (line 26)
The dataset file does not exist.

Error in parse_dataset (line 19)
data = parse_dataset_internal(res.file);

Error in run (line 20)
data = parse_dataset(fullfile(path_base,path_dset));
------------------------------------------------------------------------------------------------------------------------------------
Can you please assist with debugging?
Many Thanks
Devon

Hi Yulin Li, thanks for your feedback. May you kindly provide a few examples of assets that report this behavior after being processed through GJR Garch? Do they belong to the default dataset? If not, may you upload your data somewhere and link it to me? Thanks!

Yulin Li

Hi Tommaso, thank you for the file. I've been working on some data using this code. I find that for some assets, the conditional asset volatility, which is the sqrt of s can be the same along time. s remains as the asset variance iteratively. I'm not familiar with gjr garch. I wonder what is the reason behind this. Please let me know your thoughts or information. Thanks a lot!

Dear Yufei Cao, the bandwidth for the MES kernel density was calculating using a simplified variant of the Scott's rule of thumb due to the fact the returns were already squeeze. Thanks to your feedback I decided to implement a standard version of it (signally, the same that can be found into Matlab built-in ksdensity function). The results have been only slightly affected by the modification and you can download the new version to try it out. For what concerns your second question, I verified the computation at it seems correct as per current implementation. Again, thanks for supporting this project.

Yufei Cao

Two questions about MES. In your function: calculate_mes_internal(), (1) how to select h? (2) computation f: it may be u-(c./s_m)) ./h not (c./s_m)-u?

Hi Mugdha. It seems that the dataset file you are trying to process cannot be found in the specified path. Put a breakpoint in run, line 12, and retrieve the value of "fullfile(path,'\exampledata\Example.xlsx')". See if the path is valid; if not, fix it and run the script again.

/////////////////////////////////////////////////////////////////////
Error using parse_dataset_parse_dataset_internal (line 26)
The dataset file does not exist.

Error in parse_dataset (line 19)
data = parse_dataset_internal(res.file);

Error in run (line 12)
data = parse_dataset(fullfile(path,'\exampledata\Example.xlsx'));
////////////////////////////////////////////////////////////////////////

I'm getting this error when I run the file run.m as per your instructions. Can you help you me understand and debug this error.
Thank you.

Hi Tommaso,

thanks for answering, the reference paper is the following:
"The systemic risk of European banks during the financial and sovereign debt crises". Journal of Banking & Finance, 63, 107-125 Black, L., Correa, R., Huang, X., & Zhou, H. (2016)."
You can find it here:
https://www.federalreserve.gov/pubs/ifdp/2013/1083/ifdp1083.pdf

The DIP by Black & Huang is not part of this framework, but during the development I stumbled upon it multiple times. I don't have a lot of time at present, but if you provide me a reference paper I can eventually check if this can be included in the package with a quick and dirty implementation.

Hi Tommaso, thanks for your help.
Do you have any practical framework on the "Distress insurance premium" by Black and Huang?

Thank you

EMB

Hi Nicu, I appreciate your help. Thank you very much. I should update my Matlab version to 2017 B since my version is 2017 A. Again, thank you for your help

@Eufrocinio, try with a 2017 version of Matlab (if possible, R2017b) and create an empty folder called 'Results'. It should work.

EMB

Hi Tommaso,

Thank you very much for sharing your code. However when i attempt to run the file (run.m), i encountered these error messages,

Error using parse_dataset>parse_dataset_internal (line 26)
The dataset file does not exist.

Error in parse_dataset (line 19)
data = parse_dataset_internal(res.file)

Error in run (line 12)
data = parse_dataset(fulfilepath(path,'\Datasets\Example.xlsx'))

I appreciate your time and effort. Thank you

regards,

Eufrocinio

Weidong Lin

I am using Matlab 2017b. This is my run.m file:
warning('off','all');
close('all');
clearvars();
clc();
[path,~,~] = fileparts('C:\Users\29943\Desktop\Systemic risk matlab\TommasoBelluzzo-SystemicRisk-9244888\run.m');
paths = genpath(path);
addpath(paths);
data = parse_dataset(fullfile(path, '\Datasets\Example.xlsx'));
main_pro(data,fullfile(path, '\Results\ResultsPRO.xlsx'),0.95,0.40,0.08,true);
pause(5);
main_net(data,fullfile(path, '\Results\ResultsNET.xlsx'),0.05,true,true);
save('data.mat','data');
rmpath(paths);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
And the errors are:
Error using matlab.graphics.interaction.internal.zoom/setAxesZoomConstraint (line 12)
Axes must be resident in the same figure as the mode

Error in matlab.graphics.interaction.internal.zoom/setAxesZoomMotion (line 5)
setAxesZoomConstraint(hThis,hAx,cons);

Error in gplotmatrix/gplotmatrixLabelCallback (line 454)
setAxesZoomMotion(hz1,ax2,'horizontal');

Error using main_pro>plot_correlations (line 275)
Error while evaluating Figure SizeChangedFcn.

Error using matlab.graphics.interaction.internal.zoom/setAxesZoomConstraint (line 12)
Axes must be resident in the same figure as the mode

Error in matlab.graphics.interaction.internal.zoom/setAxesZoomMotion (line 5)
setAxesZoomConstraint(hThis,hAx,cons);

Error in gplotmatrix/gplotmatrixLabelCallback (line 454)
setAxesZoomMotion(hz1,ax2,'horizontal');

Error using waitbar (line 113)
Error while evaluating Figure SizeChangedFcn.

Index exceeds matrix dimensions.

Error in gplotmatrix/gplotmatrixLabelCallback (line 465)
if ax(ii,1).YTick(1)- ax(ii,1).YLim(1) < rangeY*0.05 && ii~=rows

Error using waitbar (line 113)
Error while evaluating Figure SizeChangedFcn.

Index exceeds matrix dimensions.

Error in gplotmatrix/gplotmatrixLabelCallback (line 465)
if ax(ii,1).YTick(1)- ax(ii,1).YLim(1) < rangeY*0.05 && ii~=rows

Error using waitbar (line 113)
Error while evaluating Figure SizeChangedFcn.

Error using distcomp.remoteparfor/getCompleteIntervals (line 257)
Index exceeds matrix dimensions.

Error in calculate_adjacency_matrix>calculate_adjacency_matrix_internal (line 36)
parfor j = ij_seq

Error in calculate_adjacency_matrix (line 23)
adjm = calculate_adjacency_matrix_internal(ip_res.data,ip_res.sst,ip_res.rob);

Error in main_net>main_net_internal (line 71)
adjm = calculate_adjacency_matrix(win_i,data.SST,data.Rob);

Error in main_net (line 35)
main_net_internal(ip_res.data,res,ip_res.sst,ip_res.rob,ip_res.anl);

Error in run (line 16)
main_net(data,fullfile(path, '\Results\ResultsNET.xlsx'),0.05,true,true);

Weidong Lin

Hi Tommaso,
Many thanks for your codes. Could you please tell me what arguments do I need to input when I run the 'run.m' file? Especially for the function 'fileparts', 'parse_dataset', 'fullfile' in the 'run.m' file.

Many thanks!

YINING XU

Hi, Tommaso,

Thank you for sharing the files. I have encountered the same problem as Yao. When I run the 'run' file, I get the following errors:

Error in parse_dataset>parse_dataset_internal (line 73)
opts = detectImportOptions(file,'Sheet',1);

Error in parse_dataset (line 19)
data = parse_dataset_internal(res.file);

Error in run (line 12)
data = parse_dataset(fullfile(path,'\Datasets\Example.xlsx'));

I have put the file as you have suggested, but still there seems to be a problem. I would appreciate your help.
Thank you.
Nicu

Hi Yao, thanks for your feedback, but your log is not really meaningful since it doesn't show the exception being thrown by the script. Please, try to provide a full version of the error log so I can try to debug it. Thanks!

Yao qu

Hi Alessio. Currently, I am running your example in this package and it gives some warnings:

Error in parse_dataset>parse_dataset_internal (line 29)
[file_stat,file_shts,file_fmt] = xlsfinfo(file);

Error in parse_dataset (line 19)
data = parse_dataset_internal(res.file);

Error in run (line 12)
data = parse_dataset(fullfile(path,'\Datasets\Example.xlsx'));

Error in run (line 91)
evalin('caller', strcat(script, ';'));

I have tried several Matlab versions include 2014b, 2015b and 2017b but that problem still exist. I will be grateful if you are able to give me some help.

Yao.

Salve Alessio. Questo programma fa esattamente al caso suo. I dati in suo possesso devono però essere modificati in modo tale da risultare compatibili con quelli attesi in fase di input/lettura. Può trovare maggiori informazioni in merito nella pagina principale del progetto ( https://github.com/TommasoBelluzzo/SystemicRisk ) e se qualcosa non fosse chiaro, può ricontattarmi qui (o là) per vedere di risolvere il problema.

P.S. = in fase di descrizione metodologica, un riferimento al mio progetto, sebbene non obbligatorio, sarebbe assai gradito.

Saluti, T.B.

Salve. Sto scrivendo la tesi e dovrei calcolare ΔCoVaR ed SRISK. Vorrei sapere se scaricando il pacchetto che trovo in questa pagina (e con i dati necessari) è possibile calcolarle. Attendo risposta. Grazie.

A.T.

Updates

1.6.8

Improved description.

1.6.7

Improved description.

1.6.6

New features.

1.6.5

Minor fixes and improvements.

1.6.4

Minor fixes and improvements.

1.6.3

Minor fixes and improvements.

1.6.2

Minor fixes and improvements.

1.6.1

Minor fixes and improvements.

1.6.0

Major refactoring, performance improvements, minor improvements and fixes.

1.5.0

Major refactoring, performance improvements, minor improvements and fixes.

1.4.2

Improved description.

1.4.1

Improved description.

1.4.0

Major refactoring, performance improvements, minor improvements and fixes.

1.3.1

Minor fixes and improvements.

1.3.0

Improved description.

1.2.9

Minor fixes and improvements.

1.2.8

Minor fixes and improvements.

1.2.7

Minor fixes and improvements.

1.2.6

Minor fixes and improvements.

1.2.5

Minor fixes and improvements.

1.2.4

Minor fixes and improvements.

1.2.3

Minor fixes and improvements.

1.2.2

Minor fixes and improvements.

1.2.1

Minor fixes and improvements.

1.2.0

Minor fixes and improvements.

1.1.9

Improved description.

1.1.8

Minor fixes and improvements.

1.1.7

Improved description.

1.1.6

Minor fixes and improvements.

1.1.5

Minor fixes and improvements.

1.1.4

Updated details concerning compatibility & requirements.

1.1.3

Minor fixes and improvements.

1.1.2

Minor fixes and improvements.

1.1.1

Project website.

1.1.0

Target release.

1.0.9

Improved tags.

1.0.8

Improved tags.

1.0.7

Improved tags.

1.0.6

Improved description.

1.0.5

Improved description.

1.0.4

Improved description.

1.0.3

Added screenshot.

1.0.2

Minor fixes and improvements.

1.0.1

Added details concerning compatibility & requirements.

1.0.0.0

Description

1.0.0.0

Description

MATLAB Release Compatibility
Created with R2014b
Compatible with R2014b to R2019a
Platform Compatibility
Windows macOS Linux