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AbstractSince "Value at Risk" (VaR) received its first wide introduction in the July 1993 Group of Thirty report, the number of users of—and uses for—VaR have increased dramatically. However, VaR itself has been evolving. In this article, we will first review some of the important refinements in VaR that have appeared—improved speed of computation, improved accuracy, and improved stress testing. We then look at the "next steps" (which we refer to as "Beyond VaR"), in which we review extensions to standard VaR, the emergence of "risk contribution" measures, and alternatives to standard VaR (including Extreme Value Theory [EVT] and Coherent Risk Measures).

Summary. The history, taxonomy, distribution, ecology and cultivation of the Javan Epigeneium triflorum var. orientale (Orchidaceae) are discussed; a full botanical description and a diagnostic key to the three varieties are provided.

Jón Ólafsson heimspekingur telur sig hafa fundið heimild fyrir því, að Komintern hafi verið andvígt stofnun Sósíalistaflokksins. Hún er minnisblað eins starfsmanns Kominterns frá sumrinu 1938. Þór Whitehead sagnfræðingur andmælir því með þeim rökum, að þetta hafi ekki verið opinber samþykkt Kominterns, auk þess sem allar aðrar heimildir bendi til þess, að stofnunin hafi verið gerð með samþykki Kominterns. Fram er komin ný heimild, heillaóskir í bréfi frá Michal Wolf, öðrum ritara Alþjóðasambands ungra kommúnista og varamanni í framkvæmdastjórn Kominterns, til Æskulýðsfylkingarinnar við stofnun hennar haustið 1938. Wolf varð síðar varnarmálaráðherra Ungverjalands undir nafninu Mihály Farkas og var eindreginn stalínisti. Þessi heimild eyðir öllum vafa um það, að stofnun Sósíalistaflokksins var gerð með samþykki Kominterns.

Since Value-at-Risk (VaR) disregards tail losses beyond the VaR boundary, the expected shortfall (ES), which measures the average loss when a VaR is exceeded, and the tail-risk-of-VaR (TR), which sums the sizes of tail losses, are used to investigate risks at the tails of distributions for major stock markets. As VaR exceptions are rare, we employ the saddlepoint or small sample asymptotic technique to backtest ES and TR. Because the two risk measures are complementary to each other and hence provide more powerful backtests, we are able to show that (a) the correct specification of distribution tail, rather than heteroscedastic process, plays a key role to accurate risk forecasts; and (b) it is best to model the tails separately from the central part of distribution using the Generalized Pareto Distribution (GPD). To sum up, we provide empirical evidence that financial markets behave differently during crises, and extreme risks cannot be modeled effectively under normal market conditions or based on a short data history.

The interest in forecasting the 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 testing ground when fitting alternative models for representing the dynamic evolution of time series of financial returns. There are vast numbers of alternative methods for constructing and evaluating VaR forecasts. In this paper, we survey the new benchmarks proposed in the recent literature. ; Financial support from Project ECO2012-32401 by the Spanish Government is gratefully acknowledged by the second author. We are also grateful to the Editor Rob Hyndman for his support and to three anonymous reviewers for their detailed and constructive comments.

Most analyses of the U.S. Great Moderation have been based on structural VAR methods, and have consistently pointed towards good luck as the main explanation for the greater macroeconomic stability of recent years. Based on an estimated New-Keynesian model in which the only source of change is the move from passive to active monetary policy, we show that VARs may misinterpret good policy for good luck. First, the policy shift is suficient to generate decreases in the theoretical innovation variances for all series, and decreases in the variances of inflation and the output gap, without any need of sunspot shocks. With sunspots, the estimated model exhibits decreases in both variances and innovation variances for all series. Second, policy counterfactuals based on the theoretical structural VAR representations of the model under the two regimes fail to capture the truth, whereas impulse-response functions to a monetary policy shock exhibit little change across regimes. Since these results are in line with those found in the structural VARbased literature on the Great Moderation, our analysis suggests that existing VAR evidence is compatible with the 'good policy' explanation of the Great Moderation.