Evaluation Of Alternative Volatility Forecasting Methods

Evaluation Of Alternative Volatility Forecasting Methods

Alternative Volatility Forecasting Methods, For many financial market applications, including option pricing and investment decisions, volatility forecasting is crucial. Therefore, the research of volatility forecasting has been an active area of study since the past years. In recent years, the emergence of many financial time series methods for volatility forecasting has proved the importance of understanding the nature of volatility in any financial instruments.

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Often, people will think ‘price’ is used as an indicator of the stock market performance. Due to the non-stationary nature of price series of the stock market, most researchers actually transformed series of ‘price change (return)’ or ‘absolute price changes (absolute return)’ in their studies. There is a difference between the term ‘return’ and the term ‘volatility’. The term ‘volatility’ is used as a crude measure of the total risk of financial assets. Actually, volatility is the standard deviation or the variance of returns whereas ‘return’ is merely the changes of prices…………

An increasingly commonly adopted tool for the measurement of the risk exposure associated with a particular portfolio of assets known as ‘Value at Risk’ (VaR) involves calculation of the expected losses that might result from changes in the market prices of particular securities (Jorion, 2001; Bessis, 2002). Thus, the VaR of a particular portfolio is defined as the maximum loss on a portfolio occurring within a specified time and with a given (small) probability.

Under this approach, the validity of a bank’s internally modeled VaR is ‘backtested’ by comparing actual daily trading gains or losses with the estimated VaR and noting the number of ‘exceptions’ occurring, in the sense of days when the VaR estimate was insufficient to cover actual trading losses, with concerns naturally arising where such exceptions frequently occur, and that can result in a range of penalties for the financial institution concerned (Saunders & Cornett, 2003).

A crucial parameter in the implementation of parametric VaR calculation methods is an estimate of the volatility parameter that describes the asset or portfolio, or more accurately a forecast of that volatility where the simplifying assumption of constancy is relaxed and time-varying volatility is acknowledged. While it has long been recognized that returns volatility exhibits ‘clustering,’ such that large (small) returns follow large (small) returns of random sign (Mandelbrot, 1963; Fama, 1965), it is only following the introduction of the generalized autoregressive conditional heteroskedasticity (GARCH) model (Engle, 1982; Bollerslev, 1986) that financial economists have modeled and forecast these temporal dependencies using econometric techniques, and a variety of adaptations of the basic GARCH framework are now widely used in modeling time-varying volatility. In particular, the significance of asymmetric effects in stock index returns…………….