Quantitative Risk Management: Concepts, Techniques and Tools, by Alexander J.McNeil, RüdigerFrey and PaulEmbrechts. Revised edition. Published by Princeton University Press, 2015. Total number of pages: 720. ISBN:  978‐0‐691‐16627‐8 (Hardback)

Nešlehová, Johanna

doi:10.1111/jtsa.12177

Cited by 4 publications

(2 citation statements)

References 42 publications

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“…If the null hypothesis of a zero difference between the two means cannot be rejected, the option-implied CVaR forecasts future losses in a consistent way. Second, we test the null hypothesis of a correctly calibrated CVaR against the alternative of a risk underestimation (McNeil et al, 2015). Third, we test the null of a correct joint calibration of the VaR and CVaR, thanks to the fact that while the CVaR is not elicitable, the VaR and CVaR are instead jointly elicitable (Fissler et al, 2016).…”

Section: Resultsmentioning

confidence: 99%

Forecasting value at risk and conditional value at risk using option market data

Molino

Sala

2021

Journal of Forecasting

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We forecast monthly value at risk (VaR) and conditional value at risk (CVaR) using option market data and four different econometric techniques. Independent from the econometric approach used, all models produce quick to estimate forward-looking risk measures that do not depend from the amount of historical data used and that, through the implied moments of options, better reflect the ever-changing market scenario. All proposed option-based approaches outperform or are equally good to different "traditional" forecasts that use historical returns as input. The extensive robustness of our results shows that the real driver of the better forecasts is the use of option market data as inputs for the analysis, more than the type of econometric approach implemented.

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Section: Resultsmentioning

confidence: 99%

Forecasting value at risk and conditional value at risk using option market data

Molino

Sala

2021

Journal of Forecasting

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“…Financial returns exhibit several stylized facts that need to be taken into consideration to produce reliable risk forecasts. Empirically, the distribution of returns is (left) skewed and fat tailed, and its variance is time varying (i.e., returns exhibit the so-called volatility clustering); see, for example, McNeil et al (2015).…”

Section: A Flexible Gas Specification For Modeling Financial Returnsmentioning

confidence: 99%

Downside Risk Evaluation with the R Package GAS

Ardia¹,

Boudt²,

Catania³

2019

The R Journal

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Financial risk managers routinely use non-linear time series models to predict the downside risk of the capital under management. They also need to evaluate the adequacy of their model using so-called backtesting procedures. The latter involve hypothesis testing and evaluation of loss functions. This paper shows how the R package GAS can be used for both the dynamic prediction and the evaluation of downside risk. Emphasis is given to the two key financial downside risk measures: Value-at-Risk (VaR) and Expected Shortfall (ES). High-level functions for: (i) prediction, (ii) backtesting, and (iii) model comparison are discussed, and code examples are provided. An illustration using the series of log-returns of the Dow Jones Industrial Average constituents is reported.

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Value-at-Risk Prediction in R with the GAS Package

2016

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GAS models have been recently proposed in time-series econometrics as valuable tools for signal extraction and prediction. This paper details how financial risk managers can use GAS models for Value-at-Risk (VaR) prediction using the novel GAS package for R. Details and code snippets for prediction, comparison and backtesting with GAS models are presented. An empirical application considering Dow Jones Index constituents investigates the VaR forecasting performance of GAS models.Recently, the new class of Score Driven (SD) models has been introduced by Creal et al. (2013) andHarvey (2013) offering an alternative to the Generalized AutoRegressive Heteroscedasticity (GARCH) framework pioneered by Bollerslev (1986) to model the conditional variance of financial returns. SD models are also referred to as Generalized Autoregressive Score (GAS) and Dynamic Conditional Score (DCS) models. In this paper, we follow the GAS nomenclature.Formally, in GAS models the vector of time-varying parameters, θ t , is updated through a dynamic equation based on the score of the conditional 2 probability density function of r t , f (·; θ t , ψ), i.e.:where s t is the scaled score of f (·; θ t , ψ) with respect to θ t , evaluated in r t ; see Creal et al. (2013). The coefficients κ, A and B control for the evolution of θ t and need to be estimated along with ψ from the data, usually by Maximum Likelihood. GAS models have been employed for a variety of applications in financial econometrics, mostly because they offer a link between the two most common frameworks to model volatility, namely: GARCH models and stochastic volatility (SV) models (Taylor, 1986). Indeed, while resorting on straightforward estimation procedures as for GARCH, they update models parameters accounting for the whole shape of the conditional distribution of the data, as in the SV context; see . Hence, they offer a good trade-of between ease of estimation and flexibility.Within the R statistical environment, the GAS package of Catania et al. (2016) allows practitioners 1 Sometimes VaR is defined with respect to the the loss variable l t = −r t , i.e., the negative of the return. Clearly, all the arguments of this paper can be easily adapted to this case. 2 The conditioning is intended with respect to the past observations r t−s (s > 0), however, for notational purposes, this is not always reported. arXiv:1611.06010v1 [q-fin.RM]

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Quantitative Risk Management: Concepts, Techniques and Tools, by Alexander J.McNeil, RüdigerFrey and PaulEmbrechts. Revised edition. Published by Princeton University Press, 2015. Total number of pages: 720. ISBN: 978‐0‐691‐16627‐8 (Hardback)

Cited by 4 publications

References 42 publications

Forecasting value at risk and conditional value at risk using option market data

Forecasting value at risk and conditional value at risk using option market data

Downside Risk Evaluation with the R Package GAS

Value-at-Risk Prediction in R with the GAS Package

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