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Volatility forecasting: evidence from a fractional integrated asymmetric power ARCH skewed-t model

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  • Stavros Degiannakis

Abstract

Predicting the one-step-ahead volatility is of great importance in measuring and managing investment risk more accurately. Taking into consideration the main characteristics of the conditional volatility of asset returns, an asymmetric Autoregressive Conditional Heteroscedasticity (ARCH) model is estimated. The model is extended to also capture (i) the skewness and excess kurtosis that the asset returns exhibit, and (ii) the fractional integration of the conditional variance. The model, which takes into consideration both the fractional integration of the conditional variance as well as the skewed and leptokurtic conditional distribution of innovations, produces the most accurate one-day-ahead volatility forecasts. The study recommends to portfolio managers and traders that extended ARCH models generate more accurate volatility forecasts of stock returns.

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  • Stavros Degiannakis, 2004. "Volatility forecasting: evidence from a fractional integrated asymmetric power ARCH skewed-t model," Applied Financial Economics, Taylor & Francis Journals, vol. 14(18), pages 1333-1342.
    • Handle: RePEc:taf:apfiec:v:14:y:2004:i:18:p:1333-1342
    DOI: 10.1080/0960310042000285794
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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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