Practical Issues in Forecasting Volatility

By Ser-Huang Poon, Clive W. J. Granger. 2005

241 citations recorded by the source corpus.

A comparison is presented of 93 studies that conducted tests of volatility-forecasting methods on a wide range of financial asset returns. The survey found that option-implied volatility provides more accurate forecasts than time-series models. Among the time-series models, no model is a clear winner, although a possible ranking is as follows: historical volatility, generalized autoregressive conditional heteroscedasticity, and stochastic volatility. The survey produced some practical suggestions for volatility forecasting. Volatility forecasting plays an important role in investment, option pricing, and risk management. In this article, we summarize our review of 93 papers devoted to comparing the forecasting power of various volatility models reported in the past 20 years. The definition of volatility is taken to be standard deviation of returns. The assets studied in these 93 papers included stock indexes, stocks, exchange rates, and interest rates from both developed and emerging financial markets. The forecast horizon ranged from one hour to one year (with a few exceptions that extended the forecast horizon to 30 months and to five years). The review covers three main categories of time-series model—historical volatility, autoregressive conditional heteroscedasticity (ARCH), and stochastic volatility (SV)—and the method of deriving implied volatility from option prices. We introduce the four models, discuss some characteristics of financial market volatility, and describe the common objectives of volatility forecasting that have a direct impact on choice of volatility model and the criteria for evaluating forecasts. Using recent research, we provide some insights into the effect of outliers, make some suggestions as to how they might be handled, and provide some practical advice for volatility forecasters. We also offer a broad-based ranking of the four volatility-forecasting models. Financial market volatility is clearly forecastable. Research has shown that the forecasting power for stock index volatility is 50–58 percent for horizons of 1 to 20 trading days. The one-day-ahead forecasting record for exchange rates is 10–15 percent, and it is likely to increase by about threefold if ex post volatility is measured more accurately. The one-week-ahead and one-month-ahead records for short-term interest rates have been documented as, respectively, 8 percent and 24 percent. Based on the forecasting results reported in the studied papers, option-implied volatility dominates time-series models because the market option price fully incorporates current information and future volatility expectations. Between historical volatility and ARCH models, we found no clear winner, but they are both better than the stochastic volatility model. Despite the added flexibility and complexity of the SV model, we found no clear evidence that it provides superior volatility forecasts. Also, high-frequency data clearly provide more information and produce better volatility forecasts, particularly over short horizons. The conclusion that option-implied volatility forecasting provides the best forecast does not violate market efficiency because accurate volatility forecasting is not in conflict with underlying asset and option prices being correct. Options are not available for all assets, so using historical volatility must be considered. These models are not necessarily less sophisticated than ARCH models. For example, the realized-volatility model is classified as a historical volatility model. The important aspects of using historical models are (1) that actual volatility must be measured accurately and (2) that when high-frequency data are available, such information improves volatility estimation and forecasts.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/d74571aab0b644b51da9c9a78a046fbd3f1a1e29

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Citations in the corpus

Gaussian Process Regression Networks (2011, methodology): “This data is available from http: //www.ai-geostats.org/ .”
25 years of time series forecasting (2006, background): “…(including random walk, historical averages, ARFIMA, and various forms of exponential smoothing) and GARCH-type models (including ARCH and its various extensions), but both classes of models outperform the stochastic volatility model; see also Poon & Granger (2005) for an update on these findings.”
Copula Processes (2010, background): “GARCH [4], a generalized version of Engle’s ARCH, is arguably unsurpassed for predicting the volatility of returns on equity indices and currency exchanges [5, 6, 7].”
The effect of price tests on trader behavior and market quality: An analysis of Reg SHO ☆ (2008, background): “31 Poon and Granger (2005) note that implied volatility measures dominate time-series measures in terms of forecasting accuracy.”
A Practical Guide to Volatility Forecasting through Calm and Storm (2011, unknown): “An extensive survey of the literature's main findings is provided in Poon and Granger (2003, 2005).”