Forecasting Financial Volatility Evidence from Chinese Stock market

Forecasting Financial Volatility Evidence from Chinese Stock market

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FORECASTING FINANCIAL VOLATILITY: EVIDENCE FROM CHINESE STOCK MARKET by HONGYU PAN and ZHICHAO ZHANG WORKING PAPER IN ECONOMICS AND FINANCE No. 06/02 FEBRUARY 2006 School of Economics, Finance and Business University of Durham 23-26 Old Elvet Durham DH1 3HY UK
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1 Forecasting Financial Volatility: Evidence from Chinese Stock Market Hongyu Pan* School of International Trade and Economics University of International Business and Economics China Zhichao Zhang Durham Business School University of Durham UK Abstract Volatility models and their forecasts are of interest to many types of economic agents, especially for financial risk management. Since 1982 when Engle proposed the Autoregressive Conditionally Heteroscedastic (ARCH) model, there have emerged numerous models for forecasting volatility. Given the vast number of models available, agents must decide which one to use. This paper explores a number of linear and GARCH-type models for predicting the daily volatility of two equity indices in the Chinese stock market. Under the framework of three distributional assumptions, the forecasts are evaluated using traditional metrics and by how they perform in a modern risk management setting-Value at Risk. We find that the relative accuracies of various methods are sensitive to the measure used to evaluate them. However, the worst performing method for forecasting the one-day-ahead volatility in the Shanghai and Shenzhen index is the random walk model. Keywords: G ARCH model, Volatility Forecasting, Value at Risk, Back-testing *. This author wishes to thank the 211-Project Office of UIBE for the funding support.
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2 I. Introduction Over the past 20 years, volatility models and their forecasts have been the focus of both academic researchers and practitioners. This is chiefly because is volatility is used as a measurement of risk. Volatility is one of the most important aspects of financial market developments, providing an important input for portfolio management, option pricing and market regulation (Poon and Granger, 2003). An investor’s choice of portfolio is intended to maximize his expected return subject to a risk constraint, or to minimize his risk subject to a return constraint. A good forecast of an asset’s price volatility provides a starting point for the assessment of investment risk. To price an option, we need to know the volatility of the underlying asset. According to the Black-Scholes formula, volatility is the only parameter that needs to be estimated. In recent years, the tremendous growth of trading activity and the trading losses of well known financial institutions have led financial regulators and supervisory committees to quantify the risk. One of the most popular methods to do this is the Value at Risk (VaR) approach. From an empirical point of view, the computation of the VaR requires the computation of the quantile of the distribution of the returns of the portfolio. With probability α , the returns will be lower than the VaR. Under
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This note was uploaded on 02/28/2010 for the course ECO 211 taught by Professor Gilo during the Spring '10 term at Young Harris.

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Forecasting Financial Volatility Evidence from Chinese Stock market

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