Market Risk and Volatility Weighted Historical Simulation after Basel III

Laurent, Jean‐Paul; Firouzi, Hassan Omidi

doi:10.2139/ssrn.3094463

Cited by 2 publications

(1 citation statement)

References 32 publications

(49 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At each past date t, one can compute a virtual return-the opposite of a clean (or hypothetical) P&L-that would occur if day t positions were exactly those of the current date. Even if each bank uses its own internal VaR model, most financial institutions compute VaR through filtered or simple historical simulations on plain or hypothetical (virtual) returns (see Laurent and Omidi Firouzi, 2017). This is the aim of the present paper to study the asymptotic properties of such VaR evaluation methods.…”

Section: Introductionmentioning

confidence: 99%

Virtual Historical Simulation for estimating the conditional VaR of large portfolios

Francq

Zakoïan

2020

Journal of Econometrics

View full text Add to dashboard Cite

In order to estimate the conditional risk of a portfolio's return, two strategies can be advocated. A multivariate strategy requires estimating a dynamic model for the vector of risk factors, which is often challenging, when at all possible, for large portfolios. A univariate approach based on a dynamic model for the portfolio's return seems more attractive. However, when the combination of the individual returns is time varying, the portfolio's return series is typically non stationary which may invalidate statistical inference. An alternative approach consists in reconstituting a "virtual portfolio", whose returns are built using the current composition of the portfolio and for which a stationary dynamic model can be estimated. This paper establishes the asymptotic properties of this method, that we call Virtual Historical Simulation.Numerical illustrations on simulated and real data are provided.JEL Classification: C13, C22 and C58.

show abstract

Section: Introductionmentioning

confidence: 99%