The sum of squared intraday returns provides an unbiased and almost error-free measure of ex-post volatility. In this paper we develop a nonlinear Autoregressive Fractionally Integrated Moving Average (ARFIMA) model for realized volatility, which accommodates level shifts, day-of-the-week...

This paper investigates the merits of high-frequency intraday data when forming minimum variance portfolios and minimum tracking error portfolios with daily rebalancing from the individual constituents of the S&P 100 index. We focus on the issue of determining the optimal sampling frequency,...

Quadratic optimization for asset portfolios often leads to error maximization, with optimizers zooming in on large errors in the predicted inputs, that is, expected returns and risks. The consequence in most cases is a poor real-time performance. In this paper we show how to improve real-time...

The volatility information content of stock options for individual firms is measured using option prices for 149 U.S. firms and the S&P 100 index. ARCH and regression models are used to compare volatility forecasts defined by historical stock returns, at-the-money implied volatilities and...

We investigate the association of various firm-specific and market-wide factors with the riskneutral skewness (RNS) implied by the prices of individual stock options. Our analysis covers 149 U.S. firms over a four-year period. Our choice of firms is based on adequate liquidity and trading...