-especially volatility and illiquidity shocks-over the subprime crisis in order to investigate their market timing activities. In a … systematic risk is highly nonlinear in extreme scenarios-especially during the subprime crisis. We find that countercyclical … strategies have an investment technology which differs from procyclical ones. During crises, the former seek to capture non …

how these volatility measures can be used for risk management. We find that momentum risk management significantly …We introduce a new class of momentum strategies, the risk-adjusted time series momentum (RAMOM) strategies, which are … based on averages of past futures returns, normalized by their volatility. We test these strategies on a universe of 64 …

Many financial decisions, such as portfolio allocation, risk management, option pricing and hedge strategies, are based …

This paper studies a basket of risk statistics that are widely used to measure investment performance. Those risk … that not all risk statistics should be included in the model when establishing an investment strategy. Lastly, we … statistics were used to rank the performance of the assets. The dependent information was removed from the set of risk measures …

about estimation risk in FFMs in high dimensions. We investigate whether recent linear and non-linear shrinkage methods help … to reduce the estimation risk in the asset return covariance matrix. Our findings indicate that modest improvements are …

We examine the pricing of tail risk in international stock markets. We find that the tail risk of different countries … mainly driven by global tail risk rather than local tail risk. World fear is also priced in the crosssection of stock returns …

We investigate the question whether macroeconomic variables contain information about future stock volatility beyond … that contained in past volatility. We show that forecasts of GDP and industrial production growth from the Federal Reserve …'s Survey of Professional Forecasters predict volatility in a cross-section of 49 industry portfolios. The expectation of higher …

volatility predictor, the results of an application to tactical asset allocation are presented. -- Multivariate GARCH ; forecast …

risk features like volatility and largest loss, which indicates that complete densities provide useful information for risk. … dynamic factor and a vector autoregressive model and includes stochastic volatility, denoted by FAVAR-SV. Next, a Bayesian … and risk features than very simple and very complex models. Combinations of two strategies help, in particular, to reduce …

models. We show that HF-based predictions yield a significantly lower portfolio volatility than methods employing daily … studies have shown and translate into substantial utility gains from the perspective of an investor with pronounced risk …