Estimation of Jump-Tails: Theory and Applications

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The recent turmoil in financial markets has highlighted the importance of obtaining a better understanding of the likely occurrence of rare disasters, or so-called "tail" events, and their transmission across different assets and markets. The main theme of the proposed research activities relates to the reliable econometric estimation of such events based high-frequency intraday financial data.Intellectual Merit: The availability of high-frequency intraday asset prices has spurred a large and rapidly growing literature concerned with the analysis of this potentially rich new data source. This project aims to further expand on our ability to extract useful information about important economic phenomena from such data through the development of new econometric procedures and empirical applications thereof. Specifically, combining the insights from traditional Extreme Value Theory (EVT), developed in the context of actuary science under the assumption of independent occurrence of rare events, with so-called realized variation measures, designed to account for empirically realistic volatility clustering in financial markets, the new econometric estimators that we seek to develop as part of the proposal hold the promise of delivering much more accurate estimates for extreme "tail" events than any currently available procedures.Broader Impact: The proposed research program has a number of important broadly defined implications for the theoretical and empirical analyzes of economic and financial data, and should be of interests to theoretically oriented econometricians, as well as applied macroeconomists and financial researchers and regulators alike with an interest in the estimation of "tail" events. In particular, the most important and difficult to manage financial market risks are invariably associated with rare events. Hence, the ability to more accurately measure and possibly forecast the "tails," holds the promise of improved risk management procedures that are better geared toward controlling large risks, leaving aside the smaller "continuous" price moves. By enhancing our understanding of the types of economic "news" that induce large price moves, or "jumps," in financial asset prices, the empirical implementations of the new procedures will also help shed new light on the fundamental linkages between asset markets and the real economy. The lack of investor confidence and fear of "tail" events are often singled out as one of the main culprits behind the massive losses in market values in the advent of the Fall 2008 financial crises, and the idea that rare disasters may help explain apparent mis-pricing has spurred a rapidly growing recent literature. The key arguments put forth in that literature typically hinge on probabilities of severe events that exceed those materialized in-sample, or probabilities calibrated to reflect an unusually broad set of assets and/or countries. Instead, our proposed econometric procedures hold the promise of reliable estimating the likely occurrence of "tail" events in a given market based on actually observed high-frequency data, without resorting to "peso" type explanations.The research results will be disseminated broadly at seminars and conferences. The project will also seek to integrate research and education by involving both graduate and undergraduate students in the proposed research activities. All of the research results, including new computer programs and databases, will be made easily available through the web.