Detail práce

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

Autor: Mgr. Michal Svatoň
Rok: 2015 - letní
Vedoucí: doc. PhDr. Jozef Baruník Ph.D.
Konzultant:
Typ práce: Diplomová
Finance, finanční trhy a bankovnictví
Jazyk: Anglicky
Stránky: 76
Ocenění:
Odkaz: https://is.cuni.cz/webapps/zzp/detail/138369/
Abstrakt: 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.

02

Prosinec

Prosinec 2021
poútstčtsone
  12345
6789101112
13141516171819
20212223242526
2728293031  

Partneři

Deloitte

Sponzoři

CRIF
McKinsey
Patria Finance