Romanian Equities: Copula and Extreme Value Theory for Modeling Market Risk

Background:

Before trying to bring more light to the definition and use of copula, I’ll start with the very basic statement that uncorrelatedness does not imply independence, while independence implies noncorrelation. This is very well explained by Mandelbrot in his book “The (mis)Behaviour of Markets” (with co-author Richard L. Hudson) – the key is in the distinction between size and direction of price movements and of course volatility clustering (large changes tend to be followed by large changes at ANY direction and small changes – followed by small changes at ANY direction. Note that here we do not specify the direction, but the size. And it is the size not direction that is important in analyzing co-movements. Correlation is not an adequate measure of dependence (a flaw of correlation is the normal distribution assumption; financial time series are not normally distributed – there are either too small or too large deviations from the average) and it is dependence that matters in risk management.

The formal definition of copula  “multivariate distribution function with uniformly distributed marginal” (Embrechts, Lindskog and McNeil, Modelling Dependence with Copulas and Applications to Risk Management) is a bit more technical and needs further clarification. The very basic of the copula is Sklar’s Thereom that  claims a copula can be derived from any joint distribution functions, and the opposite is true – namely any copula can be combined with any set of marginal distributions to result in a multivariate distribution function. The very heart of the copula is the separation of the marginal behavior and the dependence structure from the joint distribution.

There are many copulas – the most widely used are Gaussian and Student’s t, but there are also Archimedean type (Gumbel, Frank, Clayton).

Of course, as every model, copula has its limitations and in some cases can cause more troubles than the value-added from its use.

Extreme Value Theory approach was explained in the previous post. In this material, the EVT is based on calibrating Student’s t copula on standardized residuals from a autoregressive (mean)-GARCH (variance equation) model.  After that given the parameters of the Student’s t copula, jointly dependent stock returns are simulated by first simulating the corresponding dependent standardized residuals. The purpose of the whole exercise is to estimate Value-at-Risk (VaR) of the portfolio.  

Results:

Daily observations for the period Sept 3, 2012 – Sept 17, 2014 (511 daily returns for each company) of fourteen Romanian stocks are used (Fondul Proprietatea, OMV Petrom, Transgaz, Transelectrica, Banca Transilvania, BRD-GSG, Bucharest Stock Exchange, Biofarm, Antibiotice, SIF1, SIF2, SIF3, SIF4 and SIF5) are used. These stocks are combined in a hypothetical equally-weighted portfolio. The charts below present: (1) how extreme portfolio changes are during the analysed period; (2) portfolio performance.

The daily VaR at three levels of significance (1%, 5% and 10%) estimated under copula+EVT approach (together with max daily gain/loss), as well as VaR under multivariate normal distribution are reported below (10,000 daily simulations were run). Additionally, the individual stocks VaR and Expected Shortfalls at 5% level of significance are presented.

This publication is for information purposes only and should not be construed as a solicitation or an offer to buy or sell any securities.

Extreme Value-at-Risk of Bulgarian shares

Methodology: Extreme Value Theory (EVT) provides an alternative approach for the classical Value-at-Risk that is based on statistics as mean and standard deviation, as well as normal distribution assumption. Instead, EVT focuses not on the average numbers, but on extremes. And as such a background is the Generalized Extreme Value Distribution (GEV).

We collected daily close price series for a 2-year period: Sept 10, 2012 – Sept 10, 2014 (making 496 return series) for 7 stocks – Sopharma, Matlab, Advance Terrafund RETI, Fist Investment Bank, Chimimport, Eurohold and M+S Hydraulic. We then extract the worst 25 returns for each of the stocks, making 175 observations for the worst returns of all 7 stocks. Matlab’ gevfit function to the 175 worst returns is used to extract the three parameters - namely z, b, and a. Having the 3 parameters the approach for Extreme Value-at-Risk (EVaR) suggested by Quant at Risk is used, namely:

Data: The charts below present a hypothetical equally-weighted portfolio during the 2-year period. An interesting chart is the one showing the number of the standard deviations from the average, revealing how misleading could be the standard normal distribution assumption (we charted the absolute values of the standard deviations). There are too many extreme returns during the period that should not occur so often under the rules for normally distributed data. But they do, nonetheless and we should be prepared to such events.

1-day Extreme Value-at-Risk Results: We have a Fréchet distribution given the negative z as data was fit with negative signs (the daily losses). The resulted 1-day 95% confidence interval EVaR is -8.12%! This implies that among the 7 stocks at the specified significance level we should expect extreme loss of 8.12%. That’s really huge expected loss. But we have a 1-day 27.6% loss, the second and third worst losses are 17.2% and 15.2% respectively. So, it is a really period of extremes and normal distribution would hardly do a job here.

But how it compares with the VaR of the stocks we analyse? Below is a table of the results of VaR of the individual stocks based on two VaR approaches – one is based on empirical distribution and the second is based on normalised distribution (histogram):

This publication is for information purposes only and should not be construed as a solicitation or an offer to buy or sell any securities. 

Extreme acknowledgments are due to Pawel Lachowicz and his Quant at Risk!

Imports of Germany, France and Italy from Bulgaria, Romania and Poland

How does Bulgaria compare with Romania and Poland in terms of imports to key euro area economies like Germany, France and Italy? This gives an alternative view of the absolute level exports we all are used to read and analyse. But somehow important is also the question posed above. The figures of the imports are 12-month moving average after logarithmic transformations for the period Jan 2006-most recent numbers of 2014. The relative growth rates are presented in the charts below (source of the raw data are countries’ statistical institutes).

Romania Central Bank’s bank lending survey

The Central Bank of Romania published its regular report on bank lending survey on Q2 2014 figures and expectations of banks for Q3 2014 (http://bnro.ro/PublicationDocuments.aspx?icid=11324). The figures present not a bright picture on corporate loan demand for the third quarter of 2014, as provided by the chart below.

Year 2014 is expected to be another challenging year for the Romanian banks as the Central Bank initiated a significant balance sheet clean up, aimed at reducing NPL ratio in the Romanian banking system from 22.3% in Q1 2014 to 13.6%. The balance sheet clean-up has several directions: first is recording off-balance sheet of fully provisioned NPL (the painless step for the banks), the second refers to fully provisioning of loans overdue by more than 1 year and the third is related to a distinct treatment of loans granted to companies in insolvency via the recording off balance sheet up to 90% of the exposure (this is the most controversial measure, as banks claim they have higher recovery rates than 10%).