Daily Volatility Forecasts: Reassessing the Performance of GARCH Models

David Gordon McMillan, A Speight

    Research output: Contribution to journalArticlepeer-review

    35 Citations (Scopus)

    Abstract

    Volatility plays a key role in asset and portfolio management and derivatives pricing. As such, accurate measures and good forecasts of volatility are crucial for the implementation and evaluation of asset and derivative pricing models in addition to trading and hedging strategies. However, whilst GARCH models are able to capture the observed clustering effect in asset price volatility in-sample, they appear to provide relatively poor out-of-sample forecasts. Recent research has suggested that this relative failure of GARCH models arises not from a failure of the model but a failure to specify correctly the 'true volatility' measure against which forecasting performance is measured. It is argued that the standard approach of using ex post daily squared returns as the measure of 'true volatility' includes a large noisy component. An alternative measure for 'true volatility' has therefore been suggested, based upon the cumulative squared returns from intra-day data. This paper implements that technique and reports that, in a dataset of 17 daily exchange rate series, the GARCH model outperforms smoothing and moving average techniques which have been previously identified as providing superior volatility forecasts. Copyright (C) 2004 John Wiley Sons, Ltd.

    Original languageEnglish
    Pages (from-to)449-460
    Number of pages12
    JournalJournal of Forecasting
    Volume23
    Issue number6
    DOIs
    Publication statusPublished - Sept 2004

    Keywords

    • volatility forecasts
    • GARCH
    • intra-day data
    • EXCHANGE-RATE VOLATILITY
    • AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICITY
    • MARKET
    • BEHAVIOR
    • RETURNS
    • PRICES

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