A GARCH APPROACH TO VaR CALCULATION IN FINANCIAL MARKET
Nurfadhlina Abdul Halim, Endang Soeryana, Alit Kartiwa
Abstract
Value at Risk (VaR) has already becomes a standard measurement that must be carried out by financial institution for both internal interest and regulatory. VaR is defined as the value that portfolio will loss with a certain probability value and over a certain time horizon (usually one or ten days). In this paper we examine of VaR calculation when the volatility is not constant using generalized autoregressive conditional heteroscedastic (GARCH) model. We illustrate the method to real data from Indonesian financial market that is the stock of PT. Indosat Tbk.
Keywords
Value at Risk; Risk Management; GARCH model; skewness, kurtosis; Quantile
Alexader, C. (Editor). (1999). Risk Management and Analysis. Volume 1 : Measuring and Modelling Financial Risk. New York : John Wiley & Sons Inc.
Dowd, K. (2002). An Introduction to Market Risk Measurement. United State American : John Wiley & Sons Inc.
Engle, R.F. & Manganelli, S. (2002). CAViaR : Conditional Autoregressive Value at Risk by regression Quantiles.
Gourieroux, C. (1997). ARCH Models and Financial Applications. New York : Springer
Jorion, P. (2004). Bank Trading Risk and Systemic Risk. Third draft : December 2004.
Khindanova, I.N. & Rachev, S.T. (none). Value at Risk : Recent Advances. University of California, Santa Barbara and University of Karlsruhe, Germany.
Shi-Jie Deng. (2004). Heavy-tailed GARCH Models : Pricing and Risk Management Applications in Power Market. IMA Control & Pricing in Communication & Power Networks, 7-13 Mar. 2004.
Tsay, R.S. (2005). Analysis of Financial Time Series. Second Edition. Hoboken, New Jersey : John Wiley & Sons, Inc.