Forecasting volatility in the New Zealand stock market

By Jun Yu. 2002

169 citations recorded by the source corpus.

This study evaluates the performance 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 different 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.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/5ccd4ded984e4b5eb86b8829e7c2ae80b89f5441

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Citations in the corpus

Deep Learning-Based Corporate Performance Prediction Model Considering Technical Capability (2017, methodology): “Then, we estimated the prediction performance of the four models using the RMSE, as in related studies [43].”
Forecasting Volatility in Financial Markets: A Review (2003, unknown): “Yu (2002) ranks SV top for forecasting New Zealand stock market volatility, but the margin is very small, partly because the evaluation is based on variance and not standard deviation.”
Modeling energy price dynamics: GARCH versus stochastic volatility (2016, result): “This result is in line with the finding in the finance literature that stochastic volatility models often fit financial returns better (see, e.g., Kim, Shepherd, and Chib, 1998; Yu, 2002).”
Stochastic volatility forecasting and risk management (2005, methodology): “Yu (2002) uses the stochastic volatility model to forecast daily stock market volatility for New Zealand.”
Multivariate Stochastic Volatility Models: Bayesian Estimation and Model Comparison (2006, methodology): “Superior performance of univariate SV models over ARCH-type models is documented in Danielsson (1994) and Kim et al. (1998) in terms of in-sample fitting, and in Yu (2002) in terms of out-of-sample forecasting.”
MACROECONOMIC VARIABLES AND STOCK MARKET INTERACTIONS: NEW ZEALAND EVIDENCE (2006, unknown): “While, in most developed countries such as US and Japan, it is considered as a criminal offence (Yu, 2002).”
Estimation methods for stochastic volatility models: a survey (2004, methodology): “Knight and Yu (2002) establishes the strong consistency and asymptotic normality for the ECF estimators with a general weight function.”