Work detail

Application of quantile autoregressive models in minimum Value at Risk and Conditional Value at Risk hedging

Author: Mgr. Michal Svatoň
Year: 2015 - summer
Leaders: doc. PhDr. Jozef Baruník Ph.D.
Consultants:
Work type: Finance, Financial Markets and Banking
Masters
Language: English
Pages: 76
Awards and prizes:
Link: https://is.cuni.cz/webapps/zzp/detail/138369/
Abstract: Futures contracts represent a suitable instrument for hedging. One consequence
of their standardized nature is the presence of basis risk. In order
to mitigate it an agent might aim to minimize Value at Risk or Expected
Shortfall. Among numerous approaches to their modelling, CAViaR models
which build upon quantile regression are appealing due to the limited set
of assumptions and decent empirical performance. We propose alternative
specifications for CAViaR model - power and exponential CAViaR, and an
alternative, flexible way of computing Expected Shortfall within CAViaR
framework - Implied Expectile Level. Empirical analysis suggests that exponential
CAViaR yields competitive results both in Value at Risk and Expected
Shortfall modelling and in subsequent Value at Risk and Expected
Shortfall hedging.

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