Search results

1 – 10 of over 6000
To view the access options for this content please click here
Book part
Publication date: 4 December 2018

Indranarain Ramlall

Abstract

Details

Tools and Techniques for Financial Stability Analysis
Type: Book
ISBN: 978-1-78756-846-4

To view the access options for this content please click here
Article
Publication date: 1 March 2000

KEVIN DOWD

This article outlines a subjective approach to estimating value at risk (VaR) and its related confidence intervals based on priors of the profit/loss distribution and its…

Abstract

This article outlines a subjective approach to estimating value at risk (VaR) and its related confidence intervals based on priors of the profit/loss distribution and its parameters. In the tradition of Bayesian statistics, this pro‐duces probability density functions for VaR that allow for subjective uncertainty. The author shows that imple‐menting this approach can be intuitive, straightforward, and applicable to any parametric VaR. One of the more difficult issues in this area is how to assess the precision of estimates: VaR estimation is usually straightforward, but estimating a confidence interval for a VaR estimate is not. This article suggests that, by inferring VaR from prior beliefs, rather than thinking of VaR as dependent on an “objective” P/L distribution, interpreting estimated confidence intervals is less problematic

Details

The Journal of Risk Finance, vol. 1 no. 4
Type: Research Article
ISSN: 1526-5943

To view the access options for this content please click here
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.

Downloads
2310

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

To view the access options for this content please click here
Article
Publication date: 9 January 2007

Andrey Rogachev

The purpose of this paper is to consider the problem of using the Value‐at‐Risk (VaR) technique and examine its practical implementation by Swiss Private Banks.

Downloads
2600

Abstract

Purpose

The purpose of this paper is to consider the problem of using the Value‐at‐Risk (VaR) technique and examine its practical implementation by Swiss Private Banks.

Design/methodology/approach

The paper is based on a survey originally undertaken in 2003 and updated in 2005. The research results provide details on how asset and portfolio managers understand and apply VaR methodology in their daily business.

Findings

From the banks' perspectives, VaR has both positive and negative points. It is like a common denominator for various risks. The reason is that VaR is used by portfolio managers as comparable risk measurement across different asset classes and business lines.

Originality/value

This analysis shows how banks can implement VaR concept more effectively through its practical implementation areas in: portfolio management decisions and asset allocation; the “what‐if” modeling of candidate traders; and measuring and monitoring market risk.

Details

The Journal of Risk Finance, vol. 8 no. 1
Type: Research Article
ISSN: 1526-5943

Keywords

To view the access options for this content please click here
Article
Publication date: 1 February 2004

KEVIN DOWD, DAVID BLAKE and ANDREW CAIRNS

One of the most significant recent developments in the risk measurement and management area has been the emergence of value at risk (VaR). The VaR of a portfolio is the…

Abstract

One of the most significant recent developments in the risk measurement and management area has been the emergence of value at risk (VaR). The VaR of a portfolio is the maximum loss that the portfolio will suffer over a defined time horizon, at a specified level of probability known as the VaR confidence level. The VaR has proven to be a very useful measure of market risk, and is widely used in the securities and derivatives sectors: a good example is the RiskMetrics system developed by J.P. Morgan. VaR measures based on systems such as RiskMetrics' sister, CreditMetrics, have also shown their worth as measures of credit risk, and for dealing with credit‐related derivatives. In addition, VaR can be used to measure cashflow risks and even operational risks. However, these areas are mainly concerned with risks over a relatively short time horizon, and VaR has had a more limited impact so far on the insurance and pensions literatures that are mainly concerned with longer‐term risks.

Details

The Journal of Risk Finance, vol. 5 no. 2
Type: Research Article
ISSN: 1526-5943

Content available
Article
Publication date: 24 November 2021

Ramona Serrano Bautista and José Antonio Nuñez Mora

This paper tests the accuracies of the models that predict the Value-at-Risk (VaR) for the Market Integrated Latin America (MILA) and Association of Southeast Asian…

Abstract

Purpose

This paper tests the accuracies of the models that predict the Value-at-Risk (VaR) for the Market Integrated Latin America (MILA) and Association of Southeast Asian Nations (ASEAN) emerging stock markets during crisis periods.

Design/methodology/approach

Many VaR estimation models have been presented in the literature. In this paper, the VaR is estimated using the Generalized Autoregressive Conditional Heteroskedasticity, EGARCH and GJR-GARCH models under normal, skewed-normal, Student-t and skewed-Student-t distributional assumptions and compared with the predictive performance of the Conditional Autoregressive Value-at-Risk (CaViaR) considering the four alternative specifications proposed by Engle and Manganelli (2004).

Findings

The results support the robustness of the CaViaR model in out-sample VaR forecasting for the MILA and ASEAN-5 emerging stock markets in crisis periods. This evidence is based on the results of the backtesting approach that analyzed the predictive performance of the models according to their accuracy.

Originality/value

An important issue in market risk is the inaccurate estimation of risk since different VaR models lead to different risk measures, which means that there is not yet an accepted method for all situations and markets. In particular, quantifying and forecasting the risk for the MILA and ASEAN-5 stock markets is crucial for evaluating global market risk since the MILA is the biggest stock exchange in Latin America and the ASEAN region accounted for 11% of the total global foreign direct investment inflows in 2014. Furthermore, according to the Asian Development Bank, this region is projected to average 7% annual growth by 2025.

Details

Journal of Economics, Finance and Administrative Science, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2077-1886

Keywords

To view the access options for this content please click here
Article
Publication date: 31 May 2021

Sebastian Schlütter

This paper aims to propose a scenario-based approach for measuring interest rate risks. Many regulatory capital standards in banking and insurance make use of similar…

Abstract

Purpose

This paper aims to propose a scenario-based approach for measuring interest rate risks. Many regulatory capital standards in banking and insurance make use of similar approaches. The authors provide a theoretical justification and extensive backtesting of our approach.

Design/methodology/approach

The authors theoretically derive a scenario-based value-at-risk for interest rate risks based on a principal component analysis. The authors calibrate their approach based on the Nelson–Siegel model, which is modified to account for lower bounds for interest rates. The authors backtest the model outcomes against historical yield curve changes for a large number of generated asset–liability portfolios. In addition, the authors backtest the scenario-based value-at-risk against the stochastic model.

Findings

The backtesting results of the adjusted Nelson–Siegel model (accounting for a lower bound) are similar to those of the traditional Nelson–Siegel model. The suitability of the scenario-based value-at-risk can be substantially improved by allowing for correlation parameters in the aggregation of the scenario outcomes. Implementing those parameters is straightforward with the replacement of Pearson correlations by value-at-risk-implied tail correlations in situations where risk factors are not elliptically distributed.

Research limitations/implications

The paper assumes deterministic cash flow patterns. The authors discuss the applicability of their approach, e.g. for insurance companies.

Practical implications

The authors’ approach can be used to better communicate interest rate risks using scenarios. Discussing risk measurement results with decision makers can help to backtest stochastic-term structure models.

Originality/value

The authors’ adjustment of the Nelson–Siegel model to account for lower bounds makes the model more useful in the current low-yield environment when unjustifiably high negative interest rates need to be avoided. The proposed scenario-based value-at-risk allows for a pragmatic measurement of interest rate risks, which nevertheless closely approximates the value-at-risk according to the stochastic model.

Details

The Journal of Risk Finance, vol. 22 no. 1
Type: Research Article
ISSN: 1526-5943

Keywords

To view the access options for this content please click here
Book part
Publication date: 28 October 2019

Angelo Corelli

Abstract

Details

Understanding Financial Risk Management, Second Edition
Type: Book
ISBN: 978-1-78973-794-3

To view the access options for this content please click here
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…

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

To view the access options for this content please click here
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…

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.

1 – 10 of over 6000