0000000000744994
AUTHOR
Xingyi Li
The term structure of volatility predictability
Volatility forecasting is crucial for portfolio management, risk management, and pricing of derivative securities. Still, little is known about the accuracy of volatility forecasts and the horizon of volatility predictability. This paper aims to fill these gaps in the literature. We begin this paper by introducing the notions of the spot and forward predicted volatilities and propose to describe the term structure of volatility predictability by the spot and forward forecast accuracy curves. Then we perform a comprehensive study on the term structure of volatility predictability in the stock and foreign exchange markets. Our results quantify the volatility forecast accuracy across horizons …
Forecasting Stock Market Volatility: The Gains from Using Intraday Data
There is evidence that volatility forecasting models that use intraday data produce superior forecast accuracy as compared with that delivered by the models that use daily data. However, this evidence is still sparse and incomplete in the stock markets. This paper extends previous studies on forecasting stock market volatility in several important directions and comprehensively assesses the gains in forecast accuracy provided by intraday data. First, we use an extensive set of intraday data on 28 single stocks and 23 stock market indices. Second, in our study we use forecast horizons ranging from 1 day to 6 months. Third, we compare forecasting abilities of several competing models. We find…
The Limits to Volatility Predictability: Quantifying Forecast Accuracy Across Horizons
Volatility forecasting is crucial for portfolio management, risk management, and pricing of derivative securities. Still, little is known about how far ahead one can forecast volatility. First, in this paper we introduce the notions of the spot and forward predicted volatilities and propose to describe the term structure of volatility predictability by the spot and forward forecast accuracy curves. Then, by employing a few popular time-series volatility models, we perform a comprehensive empirical study on the horizon of volatility predictability. Our results suggest that, whereas the spot volatility can be predicted over horizons that extend to 35 weeks, the horizon of the forward volatili…
Stock Volatility Predictability in Bull and Bear Markets
Recent literature on stock return predictability suggests that it varies substantially across economic states being strongest during bad economic times. In line with this evidence, we document that stock volatility predictability is also state dependent. In particular, using a large data set of high-frequency data on individual stocks and a few popular time-series volatility models, in this paper we comprehensively examine how volatility forecastability varies across bull and bear states of the stock market. We find that the volatility forecast horizon is substantially longer when the market is in a bear state than when it is in a bull state. In addition, the volatility forecast accuracy is…