Variance-ratio Statistics and High-frequency Data: Testing for Changes in Intraday Volatility Patterns

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...

Spurious Jump Detection and Intraday Changes in Volatility

We investigate the properties of several nonparametric tests for jumps in financial markets. We derive a theoretical property of these tests not observed in any of the previous literature: when they are applied to finitely sampled data, they are generally biased toward finding too many jumps. This results from bias in finite-sample estimation of several important test components. The severity o...

Forecasting Volatility using High Frequency Data

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...

9944 Time Transformations , Intraday Data and Volatility Models

In this paper, we focus on the trade and quote data for the IBM stock traded at the NYSE. We present two di erent frameworks for analyzing this dataset. First, using regularly sampled observations, we characterize the intraday volatility of the mid-point of the bid-ask quotes by estimating GARCH and EGARCH models, with intraday seasonality being accounted for. We also highlight the impact of ch...