Risk Estimation of Karachi Stock Exchange via Conditional Autoregressive Value-at-Risk by Regression Quantiles

F. Iqbal

Abstract


This paper examines the market risk of Karachi Stock Exchange (KSE) by employing the Conditional Autoregressive Value-at-Risk by Regression Quantiles (CAViaR) model. The CAViaR model interprets the Value-at-Risk (VaR) as the quantile of future portfolio values conditional on current information and directly compute this quantile instead of inverting the distribution of returns. An asymmetric conditional heteroscedastic specification for CAViaR is proposed and applied along with four commonly used CAViaR specifications for the one-day-ahead VaR estimation of KSE for the period 1998 2010. The in-sample and out-of-sample predictive performance of alternative CAViaR specifications are compared and evaluated. The proposed model that accounts for asymmetry of risk is found to produce better and reliable estimates for VaR of KSE.


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References


Basel Committee on Banking Supervision.. Amendment to the Capital Accord to incorporate market risks, 1996.

P. Jorion, Value at Risk: The New Benchmark for Managing Financial Risk, 3rd edn. New York: McGraw-Hill, 2007.

K. Kuester, S. Mittinik and M. Paolella, Value-at-risk prediction: A comparison of alternative strategies, J. Financial Econometrics, vol. 4, pp. 5389, 2006.

P. Abad, S. Benito and C. Lopez, A comprehensive review of Value at Risk methodologies, The Spanish Review of Financial Economics, vol. 12, pp. 1532, 2014.

R.F. Engle and S. Manganelli, CAViaR: Conditional autoregressive value at risk by regression quantiles, J. Business and Economic Statistics, vol. 22, pp. 367-381, 2004.

R. Koenker and G. Bassett, Regression quantiles, Econometrica, vol. 46, pp. 3350, 1978.

R. Koenker, Quantile Regression. Econometric Society Monographs, New York: Cambridge University Press, 2005.

Y. Bao, T. Lee and B. Saltoglu, Evaluating predictive performance of value-at-risk models in emerging markets: A reality check, Journal of Forecasting, vol. 25, pp. 101128, 2006.

P.L.H. Yu, W.K. Li and S. Jin, On Some Models for Value-At-Risk, Econometric Reviews, vol. 2, pp. 622641, 2010.

J. Iqbal, S. Azher, and A. Ijaz, Predictive ability of Value-at-Risk methods: evidence from the Karachi Stock Exchange-100 Index, MPRA Paper 01/2010, University Library of Munich, Germany, 2010.

A. Qayyum and F. Nawaz, Measuring Financial Risk using Extreme Value Theory: evidence from Pakistan, MPRA Working Paper 29288, University Library of Munich, Germany, 2011.

F. Nawaz, and M. Afzal, Value at risk: Evidence from Pakistan Stock Exchange, African Journal of Business Management, vol. 5, no. 17, pp. 74747480, 2011.

F. Iqbal / The Nucleus 53, No. 2 (2016) 128-133

M. Mahmud and N. Mirza, Volatility and dynamics in an emerging economy: Case of Karachi Stock Exchange, Ekonomska Istraivanja, vol. 24, no. 4, pp. 5164, 2011.

A. Haque and K. Naeem, Forecasting volatility and Value-at-Risk of Karachi Stock Exchange 100 Index: Comparing distribution-type and symmetry-type models, European Online Journal of Natural and Social Sciences, vol. 3, no. 2, pp. 208219, 2014.

L. Glosten, R. Jagannathan and D. Runkle, On the relation between the expected value and the volatility on the nominal

excess return on stocks, Journal of Finance, vol. 48, no. 5, pp. 17791801, 1993.

C.W.S. Chen, R. Gerlach, B.B.K. Hwang and M. McAleer, Forecasting Value-at-Risk using nonlinear regression quantile and the intra-day range, International Journal of Forecasting, vol. 28, pp. 557574, 2012.


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