Kilic, Ekrem (2006): Violation duration as a better way of VaR model evaluation : evidence from Turkish market portfolio.
Download (161Kb) | Preview
Financial crisis those we have been experienced during last two decades encouraged the efforts of both academicians and the market participants to develop clear representations of the risk exposure of a �nancial institute. As a useful tool for measuring market risk of a portfolio, Value-at-Risk has emerged as the standard. However, there are several alternative Value-at-Risk implementations which may pro- duce signi�cantly di¤erent Value-at-Risk forecasts. Thus, evaluation of Value-at-Risk forecasts is as crucial as VaR itself. In this paper I will use the methodology which has described by Christoffersen and Pelletier and I extended the methodology to create duration based analogous of unconditional coverage, conditional coverage and inde- pendence tests. I evaluated 14 Value-at-Risk implementation by using a Turkish Market portfolio which contain foreing currency, stock and bonds.
|Item Type:||MPRA Paper|
|Institution:||Finecus Financial Software and Consultancy|
|Original Title:||Violation duration as a better way of VaR model evaluation : evidence from Turkish market portfolio|
|Keywords:||Value-at-Risk; model evaluation; conditional cover- age; duration based coverage testing|
|Subjects:||C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation, Validation, and Selection
G - Financial Economics > G1 - General Financial Markets > G11 - Portfolio Choice; Investment Decisions
|Depositing User:||Ekrem Kilic|
|Date Deposited:||06. Nov 2007|
|Last Modified:||12. Feb 2013 17:50|
 Bollerslev, T., (1990), �Modelling the Coherence in Short-Run Nominal Exchange Rates: A Multivariate Generalized ARCH Model�, Review of Economics and Statistics,No.72, pp.498-505.  Boudoukh, J., M. Richardson , and R. Whitelaw, (1998). "The best of both worlds". Risk, May, pp. 64-67.  Burden, R.L., J.D. Faires, (2005). Numerical Analysis. Eigth Edition, Chp. 4, pp.196-203, Thomson Higher Education, Belmont, CA,USA  Butler, J.S. and B. Schachter, (1998). "Estimating Value-at-Risk with a Precision Measure by Combining Kernel Estimation with Historical Simulation". Review of Derivatives Research, No.1, pp.371-390.  Christo¤ersen, P., J. Hahn and A.Inoue, (2001). "Testing, Comparing, and Combining Value-at-Risk Measures". Center for Financial Insti- tutions Working Papers 99-44, Wharton School Center for Financial Institutions, University of Pennsylvania.  Christo¤ersen, P., D. Pelletier, (2004). "Backtesting Value-at-Risk: A Duration-Based Approach". Journal of Financial Econometrics, Vol.2, No.1, pp.84-108, Oxford University Press  Christo¤ersen, P., (1998)."Evaluating Interval Forecasts". International Economic Review No.39, pp.841-862.  Christo¤ersen, P., (2003). Elements of Financial Risk Management.San Diego, Academic Press.  Danielsson, J., C. G. de Vries, (1998). "Beyond the Sample: Extreme Quantile and Probability Estimation," FMG Discussion Papers dp298, Financial Markets Group  Danielsson, J., C. G. de Vries, (1998). "Value-at-Risk and Extreme Re- turns," Tinbergen Institute Discussion Papers 98-017/2, Tinbergen In- stitute  Engle, R., (2002). "Dynamic Conditional Correlation : A Simple Class of Multivariate GARCH". Journal of Business and Economic Statistics, No.20, pp.339-350. 22  Glasserman, P., P. Heidelberger, P. Shahabuddin, (2000). "E¢ cient Monte Carlo Methods for Value-at-Risk". Risk Management Report 2000.  Glasserman, P., (2004). Monte Carlo Methods in Financial Engineering. New York, Springer-Verlag  Hull J. and White, (1998). "Incorporating Volatility Updating Into the Historical Simulation Method for Value-at-Risk".Journal of Derivatives, Vol. 6, No. 1, (Fall 1998), pp. 5-19  Jorion, P., (2001). Value at Risk: The New Benchmark for Controlling Market Risk. 2 edn, McGraw-Hill, New York.  J. P. Morgan, (1996). RiskMetrics Technical Manual. Fourth Edition. J. P. Morgan  J. P. Morgan, (1999). Risk Management: A Practical Guide. J. P. Mor- gan  Kilic, E., (2004). "Forecasting Volatility of Turkish Markets: A Com- parison of Thin and Thick Models".Econometrics 0510007, Economics Working Paper Archive EconWPA.  Kupiec,P.H., (1995). "Techniques for verifying the accuracy of risk mea- surement models". Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System  Lopez, J.A., (1998). "Methods For Evaluating Value-at-Risk Estimates". Research Paper No.9802, Federal Reserve Bank of New York.  Mandira, S., S. Thomas, and A.Shah, (2003). "Selection of Value-at- Risk Models". Journal of Forecasting, No.22, pp.337-358, John Wiley & Sons, Ltd.  Pagan, A., A. Ullah, (1999). Nonparametric Econometrics. First Edition, Chp.1, pp.71-77, Cambridge University Press  Rosenblatt, M., (1956). "Remarks on Some Nonparametric Esti- mates of a Density Function". Annals of Mathematical Statistics, Vol.27,N.3,pp.:832-837.