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Article
Publication date: 1 May 2006

Chu‐Hsiung Lin and Shan‐Shan Shen

This paper aims to investigate how effectively the value at risk (VaR) estimated using the student‐t distribution captures the market risk.

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Abstract

Purpose

This paper aims to investigate how effectively the value at risk (VaR) estimated using the student‐t distribution captures the market risk.

Design/methodology/approach

Two alternative VaR models, VaR‐t and VaR‐x models, are presented and compared with the benchmark model (VaR‐n model). In this study, we consider the Student‐t distribution as a fit to the empirical distribution for estimating the VaR measure, namely, VaR‐t method. Since the Student‐t distribution is criticized for its inability to capture the asymmetry of distribution of asset returns, we use the extreme value theory (EVT)‐based model, VaR‐x model, to take into account the asymmetry of distribution of asset returns. In addition, two different approaches, excess‐kurtosis and tail‐index techniques, for determining the degrees of freedom of the Student‐t distribution in VaR estimation are introduced.

Findings

The main finding of the study is that using the student‐t distribution for estimating VaR can improve the VaR estimation and offer accurate VaR estimates, particularly when tail index technique is used to determine the degrees of freedom and the confidence level exceeds 98.5 percent.

Originality/value

The main value is to demonstrate in detail how well the student‐t distribution behaves in estimating VaR measure for stock market index. Moreover, this study illustrates the easy process for determining the degrees of freedom of the student‐t, which is required in VaR estimation.

Details

The Journal of Risk Finance, vol. 7 no. 3
Type: Research Article
ISSN: 1526-5943

Keywords

Abstract

Details

VAR Models in Macroeconomics – New Developments and Applications: Essays in Honor of Christopher A. Sims
Type: Book
ISBN: 978-1-78190-752-8

Book part
Publication date: 15 April 2020

Cindy S. H. Wang and Shui Ki Wan

This chapter extends the univariate forecasting method proposed by Wang, Luc, and Hsiao (2013) to forecast the multivariate long memory model subject to structural breaks. The…

Abstract

This chapter extends the univariate forecasting method proposed by Wang, Luc, and Hsiao (2013) to forecast the multivariate long memory model subject to structural breaks. The approach does not need to estimate the parameters of this multivariate system nor need to detect the structural breaks. The only procedure is to employ a VAR(k) model to approximate the multivariate long memory model subject to structural breaks. Therefore, this approach reduces the computational burden substantially and also avoids estimation of the parameters of the multivariate long memory model, which can lead to poor forecasting performance. Moreover, when there are multiple breaks, when the breaks occur close to the end of the sample or when the breaks occur at different locations for the time series in the system, our VAR approximation approach solves the issue of spurious breaks in finite samples, even though the exact orders of the multivariate long memory process are unknown. Insights from our theoretical analysis are confirmed by a set of Monte Carlo experiments, through which we demonstrate that our approach provides a substantial improvement over existing multivariate prediction methods. Finally, an empirical application to the multivariate realized volatility illustrates the usefulness of our forecasting procedure.

Book part
Publication date: 13 December 2013

Fabio Canova and Matteo Ciccarelli

This article provides an overview of the panel vector autoregressive models (VAR) used in macroeconomics and finance to study the dynamic relationships between heterogeneous…

Abstract

This article provides an overview of the panel vector autoregressive models (VAR) used in macroeconomics and finance to study the dynamic relationships between heterogeneous assets, households, firms, sectors, and countries. We discuss what their distinctive features are, what they are used for, and how they can be derived from economic theory. We also describe how they are estimated and how shock identification is performed. We compare panel VAR models to other approaches used in the literature to estimate dynamic models involving heterogeneous units. Finally, we show how structural time variation can be dealt with.

Details

VAR Models in Macroeconomics – New Developments and Applications: Essays in Honor of Christopher A. Sims
Type: Book
ISBN: 978-1-78190-752-8

Keywords

Book part
Publication date: 13 December 2013

Nikolay Gospodinov, Ana María Herrera and Elena Pesavento

This article investigates the robustness of impulse response estimators to near unit roots and near cointegration in vector autoregressive (VAR) models. We compare estimators…

Abstract

This article investigates the robustness of impulse response estimators to near unit roots and near cointegration in vector autoregressive (VAR) models. We compare estimators based on VAR specifications determined by pretests for unit roots and cointegration as well as unrestricted VAR specifications in levels. Our main finding is that the impulse response estimators obtained from the levels specification tend to be most robust when the magnitude of the roots is not known. The pretest specification works well only when the restrictions imposed by the model are satisfied. Its performance deteriorates even for small deviations from the exact unit root for one or more model variables. We illustrate the practical relevance of our results through simulation examples and an empirical application.

Details

VAR Models in Macroeconomics – New Developments and Applications: Essays in Honor of Christopher A. Sims
Type: Book
ISBN: 978-1-78190-752-8

Keywords

Abstract

Details

Economics, Econometrics and the LINK: Essays in Honor of Lawrence R.Klein
Type: Book
ISBN: 978-0-44481-787-7

Article
Publication date: 1 January 2003

CHRIS BROOKS and GITA PERSAND

It is widely accepted that equity return volatility increases more following negative shocks rather than positive shocks. However, much of value‐at‐risk (VaR) analysis relies on…

814

Abstract

It is widely accepted that equity return volatility increases more following negative shocks rather than positive shocks. However, much of value‐at‐risk (VaR) analysis relies on the assumption that returns are normally distributed (a symmetric distribution). This article considers the effect of asymmetries on the evaluation and accuracy of VaR by comparing estimates based on various models.

Details

The Journal of Risk Finance, vol. 4 no. 2
Type: Research Article
ISSN: 1526-5943

Article
Publication date: 17 August 2015

Pankaj Sinha and Shalini Agnihotri

This paper aims to investigate the effect of non-normality in returns and market capitalization of stock portfolios and stock indices on value at risk and conditional VaR

Abstract

Purpose

This paper aims to investigate the effect of non-normality in returns and market capitalization of stock portfolios and stock indices on value at risk and conditional VaR estimation. It is a well-documented fact that returns of stocks and stock indices are not normally distributed, as Indian financial markets are more prone to shocks caused by regulatory changes, exchange rate fluctuations, financial instability, political uncertainty and inadequate economic reforms. Further, the relationship of liquidity represented by volume traded of stocks and the market risk calculated by VaR of the firms is studied.

Design/methodology/approach

In this paper, VaR is estimated by fitting empirical distribution of returns, parametric method and by using GARCH(1,1) with Student’s t innovation method.

Findings

It is observed that both the stocks, stock indices and their residuals exhibit non-normality; therefore, conventional methods of VaR calculation are not accurate in real word situation. It is observed that parametric method of VaR calculation is underestimating VaR and CVaR but, VaR estimated by fitting empirical distribution of return and finding out 1-a percentile is giving better results as non-normality in returns is considered. The distributions fitted by the return series are following Logistic, Weibull and Laplace. It is also observed that VaR violations are increasing with decreasing market capitalization. Therefore, we can say that market capitalization also affects accurate VaR calculation. Further, the relationship of liquidity represented by volume traded of stocks and the market risk calculated by VaR of the firms is studied. It is observed that the decrease in liquidity increases the value at risk of the firms.

Research limitations/implications

This methodology can further be extended to other assets’ VaR calculation like foreign exchange rates, commodities and bank loan portfolios, etc.

Practical implications

This finding can help risk managers and mutual fund managers (as they have portfolios of different assets size) in estimating VaR of portfolios with non-normal returns and different market capitalization with precision. VaR is used as tool in setting trading limits at trading desks. Therefore, if VaR is calculated which takes into account non-normality of underlying distribution of return then trading limits can be set with precision. Hence, both risk management and risk measurement through VaR can be enhanced if VaR is calculated with accuracy.

Originality/value

This paper is considering the joint issue of non-normality in returns and effect of market capitalization in VaR estimation.

Details

Journal of Indian Business Research, vol. 7 no. 3
Type: Research Article
ISSN: 1755-4195

Keywords

Article
Publication date: 11 May 2010

David Basterfield, Thomas Bundt and Kevin Nordt

The purpose of this paper is to explore risk management models applied to electric power markets. Several Value‐at‐Risk (VaR) models are applied to day‐ahead forward contract…

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Abstract

Purpose

The purpose of this paper is to explore risk management models applied to electric power markets. Several Value‐at‐Risk (VaR) models are applied to day‐ahead forward contract electric power price data to see which, if any, could be best used in practice.

Design/methodology/approach

A time‐varying parameter estimation procedure is used which gives all models the ability to track volatility clustering.

Findings

The RiskMetrics model outperforms the GARCH model for 95 per cent VaR, whereas the GARCH model outperforms RiskMetrics for 99 per cent VaR. Both these models are better at handling volatility clustering than the Stable model. However, the Stable model was more accurate in detecting the numbers of daily returns beyond the VaR limits. The fact that the parsimonious RiskMetrics model performed well suggests that efforts to specify the model dynamics may be unnecessary in practice.

Research limitations/implications

The present study provides a starting point for further research and suggests models that could be applied to electricity markets.

Originality/value

Electricity markets are a challenge to risk modelers, as they typically exhibit non‐Normal return distributions with time‐varying volatility. Previous academic research in this area is rather scarce.

Details

Managerial Finance, vol. 36 no. 6
Type: Research Article
ISSN: 0307-4358

Keywords

Book part
Publication date: 13 December 2013

Refet S. Gürkaynak, Burçin Kısacıkoğlu and Barbara Rossi

Recently, it has been suggested that macroeconomic forecasts from estimated dynamic stochastic general equilibrium (DSGE) models tend to be more accurate out-of-sample than random…

Abstract

Recently, it has been suggested that macroeconomic forecasts from estimated dynamic stochastic general equilibrium (DSGE) models tend to be more accurate out-of-sample than random walk forecasts or Bayesian vector autoregression (VAR) forecasts. Del Negro and Schorfheide (2013) in particular suggest that the DSGE model forecast should become the benchmark for forecasting horse-races. We compare the real-time forecasting accuracy of the Smets and Wouters (2007) DSGE model with that of several reduced-form time series models. We first demonstrate that none of the forecasting models is efficient. Our second finding is that there is no single best forecasting method. For example, typically simple AR models are most accurate at short horizons and DSGE models are most accurate at long horizons when forecasting output growth, while for inflation forecasts the results are reversed. Moreover, the relative accuracy of all models tends to evolve over time. Third, we show that there is no support to the common practice of using large-scale Bayesian VAR models as the forecast benchmark when evaluating DSGE models. Indeed, low-dimensional unrestricted AR and VAR forecasts may forecast more accurately.

Details

VAR Models in Macroeconomics – New Developments and Applications: Essays in Honor of Christopher A. Sims
Type: Book
ISBN: 978-1-78190-752-8

Keywords

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