Applied Financial Economics, 2002, 12, 193 ±202
Forecasting volatility in the New Zealand stock market
JUN YU Department of Economics, University of Auckland, Private Bag 92019, Auckland, New Zealand.
This study evaluates the performanc e of nine alternative models for predicting stock price volatility using daily New Zealand data. The competing models contain both simple models such as the random walk and smoothing models and complex models such as ARCH-type models and a stochastic volatility model. Four diVerent measures are used to evaluate the forecasting accuracy. The main results are the following: (1) the stochastic volatility model provides the best performance among all the candidates; (2) ARCH-type models can perform well or badly depending on the form chosen: the performance of the GARCH(3,2) model, the best model within the ARCH family, is sensitive to the choice of assessment measures; and (3) the regression and exponentially weighted moving average models do not perform well according to any assessment measure, in contrast to the results found in various markets.
I. INTRODUCTION Volatility in ®nancial markets has attracted growing attention by academics, policy makers and practitioners during the past two decades. First, volatility receives a great deal of concern from policy makers and ®nancial market participants because it can be used as a measurement of risk. Second, greater volatility in the stock, bond and foreign exchange markets raises important public policy issues about the stability of ®nancial markets and the impact of volatility on the economy. For example, Garner (1990) ®nds that the stock market crash in 1987 reduced consumer spending in the USA. Maskus (1990) ®nds that the volatility in foreign exchange markets has an impact on trade. Third, from a theoretical perspective, volatility plays a central role in the pricing of derivative securities. According to the Black±Scholes formula, for instance, the pricing of an European...