Measuring and Modeling Risk Using High-Frequency Data

Measuring and modeling financial volatility is the key to derivative pricing, asset allocation and risk management. The recent availability of high-frequency data allows for refined methods in this field. In particular, more precise measures for the daily or lower frequency volatility can be obtained by summing over squared high-frequency returns. In turn, this so-called realized volatility can be used for more accurate model evaluation and description of the dynamic and distributional structure of volatility. Moreover, non-parametric measures of systematic risk are attainable, that can straightforwardly be used to model the commonly observed time-variation in the betas. The discussion of these new measures and methods is accompanied by an empirical illustration using high-frequency data of the IBM incorporation and of the DJIA index

Trading Dynamics in the Foreign Exchange Market: A Latent Factor Panel Intensity Approach

We develop a panel intensity framework for the analysis of complex trading activity datasets containing detailed information on individual trading actions in different securities for a set of investors. A feature of the model is the presence of a time-varying latent factor, which captures the influence of unobserved time effects and allows for correlation across individuals. We contribute to the literature on market microstructure and behavioral finance by providing new results on the disposition effect and on the manifestation of risk aversion on the high-frequency trading level. These novel insights are made possible by the joint characterization of not only the decision to close (exit) a position, usually considered in isolation in the literature, but also the decision to open (enter) a position, which together describe the trading process in its entirety. While the disposition effect is defined with respect to the willingness to realize profits/losses with respect to the performance of the position under consideration, we find that the performance of the total portfolio of positions is an additional factor influencing trading decisions that can reinforce or dampen the standard disposition effect. Moreover, the proposed methodology allows the investigation of the strength of these effects for different groups of investors ranging from small retail investors to professional and institutional investors