Value at Risk

Gouriéroux, Christian; Jasiak, Joann

doi:10.1016/b978-0-444-50897-3.50013-4

Cited by 20 publications

(8 citation statements)

References 78 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The setup () is related to a large literature on Value‐at‐Risk (VaR); see, for instance, Duffie and Pan () and Gourieroux and Jasiak (). While VaR is usually used as a restriction of the domain of choice in the usual mean‐maximization program, Gaivoronski and Pflug (, p. 4) discussed a model for portfolio optimization for short observation periods where the investor or manager decides on the optimal portfolio allocation to maximize the τ ‐quantile of the return of the portfolio at the end of the period (the wealth), since the mean is approximately zero for any reasonable portfolio.…”

Section: Quantile Preferencesmentioning

confidence: 99%

Dynamic Quantile Models of Rational Behavior

Castro

Galvao

2019

ECTA

View full text Add to dashboard Cite

This paper develops a dynamic model of rational behavior under uncertainty, in which the agent maximizes the stream of future τ‐quantile utilities, for τ ∈ (0,1). That is, the agent has a quantile utility preference instead of the standard expected utility. Quantile preferences have useful advantages, including the ability to capture heterogeneity and allowing the separation between risk aversion and elasticity of intertemporal substitution. Although quantiles do not share some of the helpful properties of expectations, such as linearity and the law of iterated expectations, we are able to establish all the standard results in dynamic models. Namely, we show that the quantile preferences are dynamically consistent, the corresponding dynamic problem yields a value function, via a fixed point argument, this value function is concave and differentiable, and the principle of optimality holds. Additionally, we derive the corresponding Euler equation, which is well suited for using well‐known quantile regression methods for estimating and testing the economic model. In this way, the parameters of the model can be interpreted as structural objects. Therefore, the proposed methods provide microeconomic foundations for quantile regression methods. To illustrate the developments, we construct an intertemporal consumption model and estimate the discount factor and elasticity of intertemporal substitution parameters across the quantiles. The results provide evidence of heterogeneity in these parameters.

show abstract

Section: Quantile Preferencesmentioning

confidence: 99%

Dynamic Quantile Models of Rational Behavior

Castro

Galvao

2019

ECTA

View full text Add to dashboard Cite

show abstract

“…They concluded that a model with the consideration of the asymmetric and fractional integrated trait in volatility always behaved better in the prediction of VaR value. The Basel Accord recommends an Integrated Auto Regressive Conditional Heteroskedasticity(IGARCH) model with the assumption of Gaussian Distribution for the estimation of VaR value(Gourieroux and Jasiak, 2010). Chen et al (2011) have evaluated 4 kinds of stock index in Asian pacific markets.…”

Section: Related Literature Reviewmentioning

confidence: 99%

A spectral analysis based heteroscedastic model for the estimation of value at risk

Zhao

2018

JRF

View full text Add to dashboard Cite

Purpose This paper aims to focus on a better model to capture the trait of varying volatility in various financial time series, as well as to obtain reliable estimate of value at risk (VaR). Design/methodology/approach The typical methods in spectral analysis are used to obtain the sample of conditional mean, conditional variance and residual term. The generalized regression neural network is used to establish a time-varying non-linear model, and the non-parametric kernel density estimation method is applied for the estimation of VaR. Findings The proposed model is able to follow the heteroscedastic characteristic which is common in financial time series, and the estimated VaR is satisfactory. Practical implications The analysis method in this study allows the hedgers, bankers, financial analysts as well as economists to draw a better inference from financial time series. Also, relatively more precise estimate of the VaR value for a certain kind of financial asset is available. The model with its derived estimates would definitely help in developing other models. Originality/value Up-to-date, the study in literature which models financial time serial from the viewpoint of spectral analysis is rare to see. Thus, the proposed model, along with a comprehensive empirical study which reveals desirable result for the estimation of VaR would enrich the related researches at present.

show abstract

“…Alternatively, instead of testing implications based on hits, several authors consider tests based on the duration sequence, i.e. the number of observations between two consecutive failures; see Christoffersen and Pelletier (2004) and Haas (2005) Gourieroux and Jasiak (2010a) who point out that the count of exceedances may be misleading as an instrument of VaR control. The problem arises because the binary variables, δ α t , are constructed to represent rare events and, in finite samples, there could be few extreme observations leading to a lack of information needed to reject a misspecified model.…”

Section: Tests For a Single Var Levelmentioning

confidence: 99%

Frontiers in VaR forecasting and backtesting

Nieto

Ruiz

2016

International Journal of Forecasting

117

View full text Add to dashboard Cite

The interest on forecasting Value at Risk (VaR) has been growing over the last two decades due to the practical relevance of this risk measure for financial and insurance institutions. Furthermore, VaR forecasts are often used as a battleground when alternative models are fitted to represent the dynamic evolution of time series of financial returns. There is a vast amount of alternative methods for constructing and evaluating VaR forecasts. In this paper, we survey the new benchmarks proposed in the recent literature.

show abstract

Value at Risk

Cited by 20 publications

References 78 publications

Dynamic Quantile Models of Rational Behavior

Dynamic Quantile Models of Rational Behavior

A spectral analysis based heteroscedastic model for the estimation of value at risk

Frontiers in VaR forecasting and backtesting

Contact Info

Product

Resources

About