15 research outputs found
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Evaluating the Accuracy of Value-at-Risk Forecasts: New Multilevel Tests
We propose independence and conditional coverage tests which are aimed at evaluating the accuracy of Value-at-Risk (VaR) forecasts from the same model at different confidence levels. The proposed procedures are multilevel tests, i.e., joint tests of several quantiles corresponding to different confidence levels. In a comprehensive Monte Carlo exercise, we document the superiority of the proposed tests with respect to existing multilevel tests. In an empirical application, we illustrate the implementation of the tests using several VaR models and daily data for 15 MSCI world indices
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True Versus Spurious Long Memory: Some Theoretical Results and a Monte Carlo Comparison
A common feature of financial time series is their strong persistence. Yet, long memory may just be the spurious effect of either structural breaks or slow switching regimes. We explore the effects of spurious long memory on the elasticity of the stock market price with respect to volatility and show how cross-sectional aggregation may generate spurious persistence in the data. We undertake an extensive Monte Carlo study to compare the performance of five tests, constructed under the null of true long memory versus the alternative of spurious long memory due to level shifts or breaks
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Trading strategies with implied forward credit default swap spreads
Credit default risk for an obligor can be hedged with either a credit de fault swap (CDS) or a constant maturity credit default swap (CMCDS). We find strong evidence of persistent differences in the hedging cost associated with the two comparable contracts. Between 2001 and 2006, it would have been more profitable to sell CDS and buy CMCDS while after the crisis between 2008 and 2013 the opposite strategy was profitable. Panel data tests indicate that for our sample period the implied forward CDS rates are unbiased estimates of future spot CDS rates. The changes in the company implied volatility is the main determinant of trading inefficiencies, followed by the changes in GDP and in the interest rates before the crisis, and the changes in sentiment index and in the VIX after the crisis
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Multilevel and Tail Risk Management
We introduce backtesting methods to assess Value-at-Risk (VaR) and Expected Shortfall (ES) that require no more than desktop VaR violations as inputs. Maintaining an integrated VaR perspective, our methodology relies on multiple testing to combine evidence on the frequency and dynamic evolution of violations, and to capture more information than a single threshold can provide about the magnitude of violations. Contributions include a formal finite sample analysis of the joint distribution of multi-threshold violations, and limiting results that unify discrete and continuous definitions of cumulative violations across thresholds. Simulation studies demonstrate the power advantages of the proposed tests, particularly with small samples and when underlying models are unavailable to assessors. Results also reinforce the usefulness of CaViaR approaches not just for VaR but also as ES backtests. Empirically, we assess desktop data by Bloomberg on exchange traded funds. We find that tail risk is not adequately reflected via a wide spectrum of models and available measures. Results provide useful prescriptions for empirical practice and, more generally, reinforce the recent arguments in favor of combined tests and forecasts in tail risk management