Low and high prices can improve volatility forecasts during periods of turmoil

In this study, we describe a modification of the GARCH model that we have formulated, where its parameters are estimated based on closing prices as well as on information related to daily minimum and maximum prices. In an empirical application, we show that the use of low and high prices in the derivation of the likelihood function of the GARCH model improved the volatility estimation and increased the accuracy of volatility forecasts based on this model during the period of turmoil, relative to using closing prices only. This analysis was performed for two stock indices from developed markets, i.e., S&P 500 and FTSE 100, and for two stock indices from emerging markets, i.e., the Polish WIG20 index and the Greek Athex Composite Share Price Index. The main result obtained in this study is robust to both the forecast evaluation criterion applied and the proxy used for the daily volatility.