Abstract
One of the goals of financial risk management is the accurate calculation of the magnitudes and probabilities of large potential losses due to extreme events such as stock market crashes, currency crises, trading scandals, or large bond defaults. In statistical terms, these magnitudes and probabilities are high quantiles and tail probabilities of the probability distribution of losses. The importance of risk management in finance cannot be overstated. The catastrophes of October 17, 1987, Long-Term Capital Management, Barings PLC, Metallgesellschaft, Orange County and Daiwa clearly illustrate the losses that can occur as the result of extreme market movements1. The objective of extreme value analysis in finance is to quantify the probabilistic behavior of unusually large losses and to develop tools for managing extreme risks.
See Jorian (2001) for a detailed description of these financial disasters.
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(2006). Modeling Extreme Values. In: Modeling Financial Time Series with S-PLUS®. Springer, New York, NY. https://doi.org/10.1007/978-0-387-32348-0_5
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