Risk management under extreme events

This article presents two applications of extreme value theory (EVT) to financial markets: computation of value at risk (VaR) and cross-section dependence of extreme returns (i.e., tail dependence). We use a sample comprised of the United States, Europe, Asia, and Latin America. Our main findings are the following. First, on average, EVT gives the most accurate estimate of VaR. Second, tail dependence of paired returns decreases substantially when both heteroscedasticity and serial correlation are filtered out by a multivariate GARCH model. Both findings are in agreement with previous research in this area for other financial markets.     

Computing value at risk with high frequency data

We compare the computation of value at risk with daily and with high frequency data for the Deutsche mark–US dollar exchange rate. Among the main points considered in the paper are: (a) the comparison of measures of value at risk on the basis of multi-step volatility forecasts; (b) the computation of the degree of fractional differencing for high frequency data in the context of a Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity (FIGARCH) model; and (c) the comparison between deterministic and stochastic models for the filtering of high frequency returns.     

Estimation of tail‐related risk measures in the Indian stock market: An extreme value approach

The purpose of the study is to estimate tail-related risk measures using extreme value theory (EVT) in the Indian stock market. The study employs a two stage approach of conditional EVT originally proposed by McNeil and Frey (2000) to estimate dynamic Value at Risk (VaR) and expected shortfall (ES). The dynamic risk measures have been estimated for different percentiles for negative and positive returns. The estimates of risk measures computed under different quantile levels exhibit strong stability across a range of the selected thresholds, implying the accuracy and reliability of the estimated quantile based risk measures.     

Environmental and financial performance in the European manufacturing sector: An analysis of extreme tail dependency

In this study, we investigate the impact of environmental performance on financial performance. We argue that environmental performance heterogeneously affects firms with different profitability level. Using data for 288 European manufacturing firms over the period 2005–2016, we investigate the said relationship under the financial slack argument and the contrasting paradigms of neoclassical and the instrumental stakeholder theory. Employing a quantile regression framework enriched with a set of instrumental variables to more effectively approximate environmental performance, we find (i) firms with superior environmental performance tend to be more profitable; (ii) the relationship between environmental and financial performance can be characterised as positive and heterogeneous across the conditional distribution; (iii) financial and environmental performance are endogenously related only when high profitability firms are examined.