8. Applications in Value-at-Risk, Expected Shortfall and Options Pricing

  1. Evdokia Xekalaki and
  2. Stavros Degiannakis

Published Online: 31 MAR 2010

DOI: 10.1002/9780470688014.ch8

ARCH Models for Financial Applications

ARCH Models for Financial Applications

How to Cite

Xekalaki, E. and Degiannakis, S. (2010) Applications in Value-at-Risk, Expected Shortfall and Options Pricing, in ARCH Models for Financial Applications, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470688014.ch8

Author Information

  1. Department of Statistics Athens University of Economics and Business, Greece

Publication History

  1. Published Online: 31 MAR 2010
  2. Published Print: 16 APR 2010

ISBN Information

Print ISBN: 9780470066300

Online ISBN: 9780470688014

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Keywords:

  • ARCH volatility forecasts;
  • shortfall forecasting;
  • Value-At-Risk (VaR)

Summary

Value-at-risk (VaR) is the main tool for reporting to bank regulators the risk that financial institutions face. It is a statistic of the dispersion of a distribution and, for a given portfolio, refers to the worst outcome likely to occur over a predetermined period and a given confidence level. The common way to measure the performance of volatility forecasting models is by assessing their ability to predict future volatility. Another way to judge the forecasting accuracy is to construct a measure of the usefulness of the forecasts. The ARCH process assumes a discrete time framework with time-varying variance for the rate of returns. However, at least for near-the-money trading, the computation of options prices by plugging accurate volatility estimators into the BS formula is a widely used strategy among market participants.

Controlled Vocabulary Terms

autoregressive conditional heteroskedasticity; Forecast accuracy