Realizing the extremes: Estimation of tail-risk measures from a high-frequency perspective

Bee, Marco; Dupuis, Debbie J.; Trapin, Luca

doi:10.1016/j.jempfin.2016.01.006

Cited by 37 publications

(13 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This mixed volatility–EVT approach outperforms several GARCH specifications in terms of VaR forecast. Bee, Dupuis, and Trapin () extend this approach to several HF‐based volatility models and find that using realized measures provides better VaR forecasts than McNeil and Frey (). A pure EVT approach appears in Chavez‐Demoulin, Davison, and McNeil () and Chavez‐Demoulin, Embrechts, and Sardy (), where the POT model is extended to a dynamic framework.…”

Section: A Taxonomy Of Var Methodologiesmentioning

confidence: 99%

Realized extreme quantile: A joint model for conditional quantiles and measures of volatility with EVT refinements

Bee

Dupuis

Trapin

2018

J of Applied Econometrics

Self Cite

View full text Add to dashboard Cite

SummaryWe propose a new framework exploiting realized measures of volatility to estimate and forecast extreme quantiles. Our realized extreme quantile (REQ) combines quantile regression with extreme value theory and uses a measurement equation that relates the realized measure to the latent conditional quantile.Model estimation is performed by quasi maximum likelihood, and a simulation experiment validates this estimator in finite samples. An extensive empirical analysis shows that high-frequency measures are particularly informative of the dynamic quantiles. Finally, an out-of-sample forecast analysis of quantile-based risk measures confirms the merit of the REQ.

show abstract

Section: A Taxonomy Of Var Methodologiesmentioning

confidence: 99%

Realized extreme quantile: A joint model for conditional quantiles and measures of volatility with EVT refinements

Bee

Dupuis

Trapin

2018

J of Applied Econometrics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Volatility-EVT. This class of models proposes a two step procedure that pre-whitens the returns with a model for the volatility and then applies a model based on EVT to the tails of the estimated residuals (Bee et al 2016;McNeil and Frey 2000). 2.…”

Section: Var αmentioning

confidence: 99%

“…After prewhitening the returns with a GARCH model (Bollerslev 1986); Laurini and Tawn (2008) noted that the scaled residuals still present some dependence in the extremes, and proposed a declustering procedure to account for this effect. Following recent trends in financial econometrics, Bee et al (2016) propose extending the information set F t−1 to include intra-day observations and use volatility models based on high-frequency (HF) data to pre-whiten the returns.…”

Section: Volatility-evtmentioning

confidence: 99%

“…Building on this literature, several HF-based volatility models for the returns and the realized volatility have been proposed to improve model fitting (Engle and Gallo 2006;Hansen et al 2012;Shephard and Sheppard 2010). Bee et al (2016) suggest to extend the framework of McNeil and Frey (2000) using this new class of models to capture the volatility dynamic and applying the POT method to the tails of the residuals.…”

Section: Volatility-evtmentioning

confidence: 99%

See 1 more Smart Citation

Estimating and Forecasting Conditional Risk Measures with Extreme Value Theory: A Review

Bee

Trapin

2018

Risks

Self Cite

View full text Add to dashboard Cite

One of the key components of financial risk management is risk measurement. This typically requires modeling, estimating and forecasting tail-related quantities of the asset returns' conditional distribution. Recent advances in the financial econometrics literature have developed several models based on Extreme Value Theory (EVT) to carry out these tasks. The purpose of this paper is to review these methods.

show abstract

“…Most authors preferred to select a threshold as a fixed quantile of the data set, instead of determining a threshold value at each step, especially when they use a moving window of observation to find out-of-sample VaR estimates. McNeil and Frey (2000) [11], Karmakar and Shukla (2015) [19], Bee et al (2016) [20], Totić and Božović (2016) [21], Li (2017) [22], Fernandez (2003) [12], Jadhav and Ramanathan (2009) [13] and Huang et al (2017) [23] chose the 90th quantile of the loss distribution as a threshold. In contrast, a less conservative, but fixed threshold was used by Gençay and Selçuk (2004) [14], Cifter (2011) [24], Soltane et al (2012) [25].…”

Section: Introductionmentioning

confidence: 99%

Value at Risk Estimation Using the GARCH-EVT Approach with Optimal Tail Selection

Echaust

Just

2020

Mathematics

View full text Add to dashboard Cite

A conditional Extreme Value Theory (GARCH-EVT) approach is a two-stage hybrid method that combines a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) filter with the Extreme Value Theory (EVT). The approach requires pre-specification of a threshold separating distribution tails from its middle part. The appropriate choice of a threshold level is a demanding task. In this paper we use four different optimal tail selection algorithms, i.e., the path stability method, the automated Eye-Ball method, the minimization of asymptotic mean squared error method and the distance metric method with a mean absolute penalty function, to estimate out-of-sample Value at Risk (VaR) forecasts and compare them to the fixed threshold approach. Unlike other studies, we update the optimal fraction of the tail for each rolling window of the returns. The research objective is to verify to what extent optimization procedures can improve VaR estimates compared to the fixed threshold approach. Results are presented for a long and a short position applying 10 world stock indices in the period from 2000 to June 2019. Although each approach generates different threshold levels, the GARCH-EVT model produces similar Value at Risk estimates. Therefore, no improvement of VaR accuracy may be observed relative to the conservative approach taking the 95th quantile of returns as a threshold.

show abstract

Realizing the extremes: Estimation of tail-risk measures from a high-frequency perspective

Cited by 37 publications

References 47 publications

Realized extreme quantile: A joint model for conditional quantiles and measures of volatility with EVT refinements

Realized extreme quantile: A joint model for conditional quantiles and measures of volatility with EVT refinements

Estimating and Forecasting Conditional Risk Measures with Extreme Value Theory: A Review

Value at Risk Estimation Using the GARCH-EVT Approach with Optimal Tail Selection

Contact Info

Product

Resources

About