High-Frequency and Model-Free Volatility Estimators

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...

Using High-Frequency Data to Model Volatility Dynamics

he covariance matrix of asset returns is important for a wide range of individuals.1 Academics use estimates of the covariance matrix to test asset-pricing theories. Portfolio managers use the covariance matrix in designing tracking strategies where the return on their portfolio is designed to closely follow the return on a benchmark portfolio. Risk managers use the matrix to construct measures...

Bridge Homogeneous Volatility Estimators

Private Information and High-Frequency Stochastic Volatility

We study the effect of privately informed traders on measured price changes and trades in asset markets. In the model exogenous news is captured by signals that informed agents receive. Agents trade anonymously through a market specialist, who does not receive a signal. We show that the entry and exit of informed traders following the arrival of news accounts for high-frequency serial correlati...

Model-free vs. Model-based Volatility Prediction∗

The well-known ARCH/GARCH models for financial time series have been criticized of late for their poor performance in volatility prediction, i.e., prediction of squared returns.1 Focusing on three representative data series, namely a foreign exchange series (Yen vs. Dollar), a stock index series (the S&P500 index), and a stock price series (IBM), the case is made that financial returns may not ...