Value-at-risk forecasting via dynamic asymmetric exponential power distributions

Research output: Contribution to journalArticlepeer-review

Abstract

In the value-at-risk (VaR) literature, many existing works assume that the noise distribution is the same over time. To take into account the potential time-varying dynamics of stock returns, we propose a dynamic asymmetric exponential distribution-based framework. The new method includes a time-varying shape parameter to control the dynamic shape of the distribution, a time-varying probability parameter to control the dynamic proportion of positive returns, and a time-varying scale parameter to control the dynamic volatility. We combine the generalized method of moments and the exponentially weighted moving average (EWMA) approach to derive specifications for these time-varying parameters. Empirical applications demonstrate the superior performance of the proposed method when compared with various GARCH and EWMA approaches without time variation in the innovations.

Original languageEnglish (US)
JournalJournal of Forecasting
DOIs
StateAccepted/In press - 2020

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • Computer Science Applications
  • Strategy and Management
  • Statistics, Probability and Uncertainty
  • Management Science and Operations Research

Fingerprint

Dive into the research topics of 'Value-at-risk forecasting via dynamic asymmetric exponential power distributions'. Together they form a unique fingerprint.

Cite this