This paper proposes intraday High Frequency Risk (HFR) measures for market risk in the case of irregularly spaced high-frequency data. In this context, we distinguish three concepts of value-at-risk (VaR): the total VaR, the marginal (or per-time-unit) VaR, and the instantaneous VaR. Since the...

This paper proposes a new simple test of market risk models validation or Value at Risk (VaR) accuracy. The test exploits the idea that the sequence of VaR violations verifies the properties of a white noise. More precisely, we use the Multivariate Portmanteau statistic of Hosking [1980] to...

Dans cet article, nous proposons une démarche originale visant à évaluer la capacité des tests usuels de backtesting à discriminer différentes prévisions de Value at Risk (VaR) ne fournissant pas la même évaluation ex-ante du risque. Nos résultats montrent que, pour un même actif, ces...

This paper proposes a new duration-based backtesting procedure for VaR forecasts. The GMM test framework proposed by Bontemps (2006) to test for the distributional assumption (i.e. the geometric distribution) is applied to the case of the VaR forecasts validity. Using simple J-statistic based on...

The objective of this paper is to propose a market risk measure defined in price event time and a suitable backtesting procedure for irregularly spaced data. Firstly, we combine Autoregressive Conditional Duration models for price movements and a non parametric quantile estimation to derive a...

This paper proposes a new test of Value at Risk (VaR) validation. Our test exploits the idea that the sequence of VaR violations (Hit function) - taking value 1-α, if there is a violation, and -α otherwise - for a nominal coverage rate α verifies the properties of a martingale difference if...

In the context of credit scoring, ensemble methods based on decision trees, suchas the random forest method, provide better classi cation performance than standardlogistic regression models. However, logistic regression remains the benchmark in thecredit risk industry mainly because the lack of...