Nonparametric Estimation of Conditional Expected Shortfall

We consider a nonparametric method to estimate conditional expected shortfalls, i.e. conditional expected losses knowing that losses are larger than a given loss quantile. We derive the asymptotic properties of kernal estimators of conditional expected shortfalls in the context of a stationary process satisfying strong mixing conditions. An empirical illustration is given for several stock index returns, namely CAC40, DAX30, S&P500, DJI, and Nikkei225.

MoreLess

Year of publication:	2004-05
Authors:	SCAILLET, Olivier
Institutions:	Swiss Finance Institute
Subject:	Nonparametric \| Kernel \| Time series \| Conditional VAR \| Conditional expected shortfall \| Risk management \| Loss severity distribution

freely available

Full text |

More access options

Check Google Scholar

In German libraries (KVK)

I need help

More details

Series:	FAME Research Paper Series.
Type of publication:	Book / Working Paper
Language:	English
Classification:	C14 - Semiparametric and Nonparametric Methods ; D81 - Criteria for Decision-Making under Risk and Uncertainty ; G10 - General Financial Markets. General ; G21 - Banks; Other Depository Institutions; Mortgages ; G22 - Insurance; Insurance Companies ; G28 - Government Policy and Regulation
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005248410