1. Measuring Market Risk in EU New Member States Saša Žiković Faculty of Economics, University of Rijeka 13th Dubrovnik Economic Conference Croatian National Bank Dubrovnik, Croatia

2. Risk measurement in EU new member states  CEEC (Poland, Hungary, Czech Republic, Slovakia, Slovenia), Baltic states (Estonia, Lithuania, Latvia), Malta and Cyprus  Financial markets almost completely liberlised  Consolidation trends in financial industry  D omination of banks as financial intermediaries ( share in total f. assets > 80% - Slovakia > 90%)  High concetration in banking sector  L imited role of equity market s

3.  Lack of research  Lagging behind EU-15 in: - financial legislation, - market discipline, - insider trading, - disclosure of information, - knowledge of financial instruments, markets and risks Common features in the field of risk management

4.  Similar past - state-owned banks - extension of credit - government guidelines /existing banking relationships - government, other banks, companies provided support in distress - formal oversight, compliance with rules not risk mitigation - “moral hazard” - high costs of the system Common features in the field of risk management

5.  Present & future - risk management departments in banks - marking to market - fair value accounting (IAS 39) - quantification of risks - Value at Risk (VaR) models - Expected tail loss (ETL) models - Stress testing Common features in the field of risk management

6.  Knowledge: - huge differences within national economies - foreign banks better versed  Implementation of Basel standards - foreign banks - acquired internal models, pressure from headquarters - domestic banks - standardised approach - middle/small banks without risk management department Common features in the field of risk management

7.  Implementation of Basel standards - understaffed risk management departments even in larger banks - lack of knowledge and skilled employees - low % of banks use VaR to set trading limits - VaR not used for all risks; FX and equity risk - use of VaR for economic capital and capital requirements - medium term plans (desire, actual plan?) Common features in the field of risk management

8. Gross share of securities in assets of consolidated balance sheet of commercial banks in national economies, 2001- 2005 200514.5%23.6%28.3%14.4%28.0%6.9%2.9%5.4% YearPOLSLVKCZEHUNSLOESTLATLITHAvg 200116.8%27.0%27.8%19.3%28.1%15.8%N/A10.6%20.8% 200216.3%35.0%26.0%18.0%34.0%17.3%N/A11.8%22.6% 200316.4%36.4%26.4%19.0%34.2%9.4%4.9%10.0%19.6% 200416.2%32.5%26.6%16.3%28.9%8.0%3.9%7.2%17.4% 15.5% Avg16.0%30.9%27.0%17.4%30.6%11.5%3.9%9.0%19.2%

9. Gross share of securities in assets of consolidated balance sheet of commercial banks in national economies, 2001- 2005 200520,7%43,9%27,5% YearAustriaGermanyFranceAverage 200113,1%18,5%39,1%23,6% 200212,2%18,0%37,7%22,7% 200315,7%18,5%41,2%25,1% 200417,0%19,9%42,2%26,4% 17,9% Average15,2%19,1%40,8%25,1%

10.  Opposite trends between the EU new member states and EU old member states  Cleaning of banks’ balance sheets in EU new member states from: - state issued securities - sale of interests in the companies (collateral for bad debts)

11. Statistical properties of capital markets in EU new member states SBI20 BUX WIG20 PX50

12. Statistical properties of capital markets in EU new member states SKSM TALSERIGSEVILSE

13. Statistical properties of capital markets in EU new member states CYSMGENL MALTEX

14.  Boom in the markets due to: - catching up to EU standards - strong inflow of foreign direct and portfolio investments - securities trading at a discount compared to EU-15  CYSMGENL, WIG20 and MALTEX index diverge from strong positive trend

15. Statistical properties of capital markets in EU new member states Basic statistics for stock indexes from EU new member states, 1.1.2000 - 31.12.2005

16. Statistical properties of capital markets in EU new member states Normality tests for stock indexes from EU new member states, 1.1.2000 - 31.12.2005

17. Statistical properties of capital markets in EU new member states  All indexes have fat tails and asymmetry  Returns are not normaly distributed  Autocorrelation present in 7/10 indexes  Heteroskedasticity present in all indexes  Unsuitable for implementation of many VaR models

18. Estimated ARMA-GARCH parameters for stock indexes of EU new member states

19.  Basic GARCH (1,1) model was sufficient for all stock index except RIGSE (GJR-GARCH)  SBI20, VILSE, MALTEX and CYSMGENL - low persistence in volatility, very reactive  Stock indexes are far from integrated (contrary to EWMA volatility modelling - RiskMetrics model ) except CYSMGENL  Elementary assumptions of many VaR models are not satisfied  VaR models based on assumptions of normality and/or IID observations, will not perform satisfactory

20. Hybrid Historical simulation (HHS)  Captures time varying volatility, asymmetry and leptokurtosis  Nonparametric bootstrapping standardized residuals + Parametric GARCH volatility forecasting  Bootstrapping - leptokurtosis and asymmetry  GARCH - time varying volatility  Modification of recursive bootstrap procedure (Freedman, Peters, 1984) and volatility updating (Hull, White, 1998)

21. Hybrid Historical simulation (HHS) VaR at x% confidence level G(.; t;N) empirical cumulative distribution function Smooth density estimator (kernels)

22. Hybrid Historical simulation (HHS) Observation period Growth with the passing of time = more conservative estimates Arbitrary set the length = less conservative estimates

23. Data and VaR models  Returns collected from Bloomberg web site, period 01.01.2000 - 31.12.2005  1-day holding period VaR  95 and 99 % confidence level  Out-of-the-sample data sets = 500 latest observations from each index

24. Data and VaR models  Tested VaR models - Normal variance-covariance VaR, - RiskMetrics system, - Historical simulation 50, 100, 250 and 500 days, - BRW Historical simulation λ = 0.97 and 0.99, - GARCH RiskMetrics, - Hybrid Historical simulation

25. Results Number of VaR model failures, Kupiec and Christoffersen IND test, 95% confidence level

26. Results 95% cl  Majority of VaR models failed Kupiec test for at least one stock index  HHS, GARCH-RiskMetrics, BRW with λ = 0.97 and 0.99 passed the Kupiec test  All VaR models failed Christoffersen IND test  Best performers: HHS and GARCH-RiskMetrics  Worst performers: HS 50 and HS 100

27. Results Number of VaR model failures, Kupiec and Christoffersen IND test, 99% confidence level

28. Results 99% cl  Almost all VaR models perform very poorly  Only HHS model passed the Kupiec test for all the indexes  Majority of VaR models failed Christoffersen IND test  HHS and GARCH-RiskMetrics model passed the Christoffersen IND test  Best performers: HHS, HS 500 and BRW λ = 0.99  Worst performers: HS 50, HS 100, BRW λ = 0.97  RiskMetrics model failed 4/10 indexes

29. Results  Historical simulation, Normal VCV and RiskMetrics system do not capture the dynamics of data generating processes of stock indexes from EU new member states  HHS model ranked as the best performer for 6/10 indexes  Ranked 2. for remaining four indexes  GARCH-RiskMetrics model ranked as the best performer for 3/10 indexes.

30. Overall ranking scores of VaR models by backtesting performance at 99% cl

31. Qualitative characteristics of tested VaR models

32. Results  HHS model is the best performing tested VaR model across the stock indexes from EU new member states.  GARCH-RiskMetrics and HS 500 model placed high  Worst performers: HS with short observation periods  Popular VaR models (HS, Normal VCV and RiskMetrics system) placed low

33. CONCLUSION  All stock indexes from EU new member states are characterised by: - fat tails and asymmetry - autocorrelation and heteroskedasticity  Returns are not IID  Elementary assumption of many VaR models are not satisfied  Simpler VaR models cannot be trusted, at best, provide only unconditional coverage

34. CONCLUSION  ARMA-GARCH models successfully capture the dynamics of stock indexes from EU new member states  VaR models that assume constant volatility or take a more simplistic view of volatility fail  Extensions of VaR models (BRW and RiskMetrics) show improvement over the basic models  Modifying the RiskMetrics model with GARCH based volatility forecasting brings significant improvements to basic RiskMetrics model

35. CONCLUSION  HHS model is the best performing tested VaR model across the stock indexes from EU new member states.  VaR models that are commonly used in developed financial market are not suited for measuring market risk in EU new member states

36. CONCLUSION  Every VaR software package that a bank is thinking about implementing should be rigorously tested  National regulators have to take into consideration that simplistic, widely used VaR models are not well suited for developing financial markets.  Before allowance is given to banks on using internal VaR models national regulators should rigorously checks and analyse the backtesting performance as well as the theoretical framework of such models

37. Thank you for your attention