Risk Measure Inference

Hurlin, Christophe; Laurent, SÃ©bastien; Quaedvlieg, Rogier; Smeekes, Stephan

doi:10.1080/07350015.2015.1127815

Cited by 14 publications

(6 citation statements)

References 36 publications

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“…For instance, a risk manager could test whether the extreme VaR (or ES) of one asset is larger than the extreme VaR (or ES) of another asset. For non‐extreme conditional (on past returns) risk measures, such a test was recently developed by Hurlin et al . Our results may also be used to test whether the extreme VaRs differ in both tails of univariate observations.…”

Section: Motivationmentioning

confidence: 97%

Detecting Tail Risk Differences in Multivariate Time Series

Hoga

2018

Journal Time Series Analysis

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We derive functional central limit theory for tail index estimates in multivariate time series under mild conditions on the extremal dependence between the components. We use this result to also derive convergence results for extreme value‐at‐risk and extreme expected shortfall estimates. This allows us to construct tests for equality of ‘tail risk’ in multivariate data, which can be useful in a number of empirical contexts. In constructing test statistics, we avoid estimating long‐run variances by using self‐normalization. Size and power of the tests for equal ‘tail risk’ are assessed in simulations. An empirical application to exchange returns illustrates the practical usefulness of the tests.

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Section: Motivationmentioning

confidence: 97%

Detecting Tail Risk Differences in Multivariate Time Series

Hoga

2018

Journal Time Series Analysis

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“…The Monte Carlo simulation method serves as a simulation approach that leverages random numbers to address problems associated with uncertain states where conventional mathematical evaluation proves unfeasible. A Monte Carlo simulation attests to the robust size and power properties of this technique (Hurlin et al 2017). The foundation of Monte Carlo simulations rests on probabilistic experiments conducted with random sampling.…”

Section: Value At Risk With Monte Carlo Simulationmentioning

confidence: 99%

The Gumbel Copula Method for Estimating Value at Risk: Evidence from Telecommunication Stocks in Indonesia during the COVID-19 Pandemic

Tinungki,

Siswanto,

Najiha

2023

JRFM

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The COVID-19 pandemic has had a substantial and far-reaching impact on global economic growth, extending its effects to Indonesia as well. Various sectors have witnessed a decline in stock returns as a consequence. Interestingly, the telecommunications sector has bucked this trend by experiencing an increase in stock returns, defying the negative implications of the pandemic. The relationship between returns and risk is inherently intertwined, necessitating a meticulous risk assessment. In response to this need, the Value at Risk (VaR) method has emerged as a rapidly growing and widely adopted risk measurement tool. Among the techniques employed for VaR estimation, the Monte Carlo simulation stands out due to its flexibility and comprehensiveness in accommodating factors such as time variance, volatility, returns, fat tails, and extreme scenarios. The Gumbel copula method, known for its heightened sensitivity to high-risk events, is utilized for VaR estimation on abnormal stock returns. This study aims to quantify the Value at Risk by leveraging the estimated Gumbel copula parameter for the return on the shares of PT. Indosat Ooredoo Hutchison Tbk, and PT. Smartfren Telecom Tbk during the COVID-19 pandemic. At a 90% confidence level, the VaR is determined to be 7.6%. Notably, this estimate closely aligns with the actual values, underscoring the reliability of the VaR estimation conducted using the Gumbel copula parameter estimator. Therefore, this model serves as a robust reference, particularly suitable when dealing with investment return data that deviate from the normal distribution, while considering the unique stock return characteristics within each dataset.

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“…Several recent papers propose to correct tests of forecasts for estimation error; see, e.g., Escanciano and Olmo (2010); Du and Escanciano (2017); Hurlin et al (2017). Implementation of these corrections generally requires knowledge of the forecasting model and estimation scheme and is thus infeasible in the regulatory context we describe.…”

Section: Spectral Transformations Of Pit Exceedancesmentioning

confidence: 99%

Spectral Backtests of Forecast Distributions with Application to Risk Management

Gordy

McNeil²

2018

FEDS

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We study a class of backtests for forecast distributions in which the test statistic depends on a spectral transformation that weights exceedance events by a function of the modeled probability level. The weighting scheme is specified by a kernel measure which makes explicit the user's priorities for model performance. The class of spectral backtests includes tests of unconditional coverage and tests of conditional coverage. We show how the class embeds a wide variety of backtests in the existing literature, and further propose novel variants which are easily implemented, well-sized and have good power. In an empirical application, we backtest forecast distributions for the overnight P&L of ten bank trading portfolios. For some portfolios, test results depend materially on the choice of kernel.

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Risk Measure Inference

Cited by 14 publications

References 36 publications

Detecting Tail Risk Differences in Multivariate Time Series

Detecting Tail Risk Differences in Multivariate Time Series

The Gumbel Copula Method for Estimating Value at Risk: Evidence from Telecommunication Stocks in Indonesia during the COVID-19 Pandemic

Spectral Backtests of Forecast Distributions with Application to Risk Management

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