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Forecasting REIT volatility with high-frequency data: a comparison of alternative methods

Jian Zhou

Applied Economics, 2017, vol. 49, issue 26, 2590-2605

Abstract: Volatility is a crucial input for many financial applications, including asset allocation, risk management and option pricing. Over the last two decades the use of high-frequency data has greatly advanced the research on volatility modelling. This article makes the first attempt in the real estate literature to employ intraday data for volatility forecasting. We examine a wide range of commonly used methods and apply them to several major global REIT markets. Our findings suggest that the group of reduced form methods deliver the most accurate one-step-ahead forecast for daily REIT volatility. They outperform their GARCH-model-based counterparts and two methods using low-frequency data. We also show that exploiting intraday information through GARCH does not necessarily yield incremental precision for forecasting REIT volatility. Our results are relatively robust to the choice of realized measure of volatility and the length of evaluation period.

Date: 2017
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Handle: RePEc:taf:applec:v:49:y:2017:i:26:p:2590-2605