High-Frequency Data, Frequency Domain Inference, and Volatility Forecasting

Volatility Forecasting with High Frequency Data

The daily volatility is typically unobserved but can be estimated using high frequent tick-by-tick data. In this paper, we study the problem of forecasting the unobserved volatility using past values of measured volatility. Specifically, we use daily estimates of volatility based on high frequency data, called realized variance, and construct the optimal linear forecast of future volatility. Ut...

Forecasting Volatility using High Frequency Data

Spot volatility estimation for high-frequency data

The availability of high-frequency intraday data allows us to accurately estimate stock volatility. This paper employs a bivariate diffusion to model the price and volatility of an asset and investigates kernel type estimators of spot volatility based on highfrequency return data. We establish both pointwise and global asymptotic distributions for the estimators. Jianqing Fan is Frederick Moore...

Forecasting the Return Distribution Using High-Frequency Volatility Measures

The aim of this paper is to forecast (out-of-sample) the distribution of financial returns based on realized volatility measures constructed from high-frequency returns. We adopt a semi-parametric model for the distribution by assuming that the return quantiles depend on the realized measures and evaluate the distribution, quantile and interval forecasts of the quantile model in comparison to a...

High Frequency Volatility

where B(t) is a standard Brownian motion. The volatility process σ(t) may be random or nonrandom, but it should be continuous. We observe one realization of X(t) for 0 ≤ t ≤ T , for example, one day of intraday tick data. Along with this, of course, will be one realization of σ(t). We assume that any drift term is negligible, which is generally adequate for high-frequency data. What can we tell...