A robust VaR model under different time periods and weighting schemes

This paper analyses several volatility models by examining their ability to forecast Value-at-Risk (VaR) for two different time periods and two capitalization weighting schemes. Specifically, VaR is calculated for large and small capitalization stocks, based on Dow Jones (DJ) Euro Stoxx indices and is modeled for long and short trading positions by using non parametric, semi parametric and parametric methods. In order to choose one model among the various forecasting methods, a two-stage backtesting procedure is implemented. In the first stage the unconditional coverage test is used to examine the statistical accuracy of the models. In the second stage a loss function is applied to investigate whether the differences between the models, that calculated accurately the VaR, are statistically significant. Under this framework, the combination of a parametric model with the historical simulation produced robust results across the sample periods, market capitalization schemes, trading positions and confidence levels and therefore there is a risk measure that is reliable.

Proprietary trading losses in banks: do banks invest sufficiently in control?

This paper analyzes management and control issues linked to the employment of traders who engage in proprietary trading activity for their employer (a bank). The bank can invest in control and monitoring of these traders, and the paper evaluates the profitability of such investments. We find that the investment in control is distorted due to interfering market microstructure effects. The bank is inclined to underinvest in control of its traders because traders who are not too closely monitored generate extra liquidity in the market. Bank supervision might be needed, therefore, to correct for such effects. We evaluate the effectiveness of the value-at-risk capital adequacy requirement proposed by the Bank for International Settlements, and find that this approach correctly targets the banks that are the most vulnerable, i.e. those that are the most at risk of underinvesting in its control and monitoring systems.      

Does prudential regulation contribute to effective measurement and management of interest rate risk? Evidence from Italian banks

This paper contributes to prior literature and to the current debate concerning recent revisions of the regulatory approach to measuring bank exposure to interest rate risk in the banking book by focusing on assessment of the appropriate amount of capital banks should set aside against this specific risk. We first discuss how banks might develop internal measurement systems to model changes in interest rates and measure their exposure to interest rate risk that are more refined and effective than are regulatory methodologies. We then develop a backtesting framework to test the consistency of methodology results with actual bank risk exposure. Using a representative sample of Italian banks between 2006 and 2013, our empirical analysis supports the need to improve the standardized shock currently enforced by the Basel Committee on Banking Supervision. It also provides useful insights for properly measuring the amount of capital to cover interest rate risk that is sufficient to ensure both financial system functioning and banking stability.     

Value relevance of value-at-risk disclosure

The SEC issued FRR No. 48 in 1997 to enhance public disclosure of firms’ exposures to market risk. We examine whether the quantitative value-at-risk (VAR) estimates disclosed by 81 non-financial firms during the period 1997–2002 are value-relevant using the earnings-returns relation. The empirical results indicate that high VAR is associated with weaker earnings-returns relation. Further analysis shows that VAR is positively and significantly associated with future stock return volatility. Our evidence suggests that investors perceive the earnings of firms with substantial market risk exposure to be less persistent, and adjust the future abnormal earnings for the higher risk exposure. Thus, this results in a lower expected rate of return.

Extremes on the discounted aggregate claims in a time dependent risk model

This paper presents an extension of the classical compound Poisson risk model for which the inter-claim time and the forthcoming claim amount are no longer independent random variables (rv's). Asymptotic tail probabilities for the discounted aggregate claims are presented when the force of interest is constant and the claim amounts are heavy tail distributed rv's. Furthermore, we derive asymptotic finite time ruin probabilities, as well as asymptotic approximations for some common risk measures associated with the discounted aggregate claims. A simulation study is performed in order to validate the results obtained in the free interest risk model.     

Emerging markets and portfolio foreign exchange risk: An empirical investigation using a value-at-risk decomposition technique

The correlation between a portfolio's equity and foreign exchange components plays a role in reducing foreign exchange exposure. Investors must account for this correlation when determining the extent of foreign exchange risk in emerging market equity portfolio investments. This study employs a VaR risk factor mapping technique, under the variance–covariance VaR approach, to decompose portfolio risk in Argentina, Brazil, China, India, Mexico and Russia. For comparison purposes, the same technique is used to decompose portfolio risk in the US. The study is conducted from the perspective of a European equity investor with a portfolio of equities in each country. By employing the VaR decomposition technique, the correlation between a portfolio's equity and foreign exchange components is taken into account and portfolio foreign exchange risk is extracted from portfolio systematic risk. Our results uniquely demonstrate significant variation in foreign exchange risk in emerging markets.     

The effectiveness of the VaR-based portfolio insurance strategy: An empirical analysis

This paper proposes an approach to constructing the insured portfolios under the VaR-based portfolio insurance strategy (VBPI) and provides a comprehensive analysis of its hedging effectiveness in comparison with the buy-and-hold (B&H) as well as the constant proportion portfolio insurance (CPPI) strategies in the context of the Chinese market. The results show that both of the insurance strategies are able to limit the downward returns while retaining certain upside returns, and their capabilities of reshaping the return distributions increase as the guarantee or the confidence level rises. In general, the VBPI strategy tends to outperform the CPPI strategy in terms of both the degree of downside protection and the return performance.     

Value-at-risk forecasts under scrutiny—the German experience

We present an analysis of the VaR forecasts and the P&L series of all 12 German banks that used internal models for regulatory purposes throughout the period from the beginning of 2001 to the end of 2004. One task of a supervisor is to estimate the ‘recalibration factor’, i.e. by how much a bank over- or underestimates its VaR. The Basel traffic light approach to backtesting, which maps the count of exceptions in the trailing year to a multiplicative penalty factor, can be viewed as a way to estimate the ‘recalibration factor’. We introduce techniques that provide a much more powerful inference on the recalibration factor than the Basel approach based on the count of exceptions. The notions ‘return on VaR (RoVaR)’ and ‘well-behaved forecast system’ are keys to linking the problem at hand to the established literature on the evaluation of density forecasts. We perform extensive bootstrapping analyses allowing (1) an assessment of the accuracy of our estimates of the recalibration factor and (2) a comparison of the estimation error of different scale and quantile estimators. Certain robust estimators turn out to outperform the more popular estimators used in the literature. Empirical results for the non-public data are compared to the corresponding results for hypothetical portfolios based on publicly available market data. While these comparisons have to be interpreted with care since the banks' P&L data tend to be more contaminated with errors than the major market indices, they shed light on the similarities and differences between banks' RoVaRs and market index returns.     

Managing extreme risks in tranquil and volatile markets using conditional extreme value theory

Financial risk management typically deals with low-probability events in the tails of asset price distributions. To capture the behavior of these tails, one should therefore rely on models that explicitly focus on the tails. Extreme value theory (EVT)-based models do exactly that, and in this paper, we apply both unconditional and conditional EVT models to the management of extreme market risks in stock markets. We find conditional EVT models to give particularly accurate Value-at-Risk (VaR) measures, and a comparison with traditional (Generalized ARCH (GARCH)) approaches to calculate VaR demonstrates EVT as being the superior approach both for standard and more extreme VaR quantiles.     

Conditional higher order moments in metal asset returns

This study examines the role of higher order moments in the returns of four important metals, aluminium, copper, gold and silver, using the asymmetric GARCH (AGARCH) model with a conditional skewed generalized-t (SGT) distribution. Implications of higher order moments in metal returns are evaluated by comparing the performances of conditional value-at-risk measures obtained from the AGARCH models with SGT distributions to those obtained from the AGARCH models with normal and student-t distributions. With the exception of gold, the AGARCH model with the SGT distribution appears to have the best fit for all metals examined.