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The availability of numerous modeling approaches for volatility forecasting leads to model uncertainty for both researchers and practitioners. A large number of studies provide evidence in favor of combination methods for forecasting a variety of financial variables, but most of them are implemented on returns forecasting and evaluate their performance based solely on statistical evaluation criteria. In this paper, we combine various volatility forecasts based on different combination schemes and evaluate their performance in forecasting the volatility of the S&P 500 index. We use an exhaustive variety of combination methods to forecast volatility, ranging from simple techniques to time‐varying techniques based on the past performance of the single models and regression techniques. We then evaluate the forecasting performance of single and combination volatility forecasts based on both statistical and economic loss functions. The empirical analysis in this paper yields an important conclusion. Although combination forecasts based on more complex methods perform better than the simple combinations and single models, there is no dominant combination technique that outperforms the rest in both statistical and economic terms.

This paper examines the dynamic relationship between stock and bond returns in eleven Eurozone countries during the last seventeen years. The literature so far reports heterogeneous results with respect to the important determinants of the stock-bond relationship. To deal with model uncertainty we employ a Bayesian model averaging technique and examine various macroeconomic and financial variables which are likely to influence stock-bond comovement. Bond and stock market uncertainty, interest rate, inflation and state of the economy are important determinants of cross-asset correlations. Divergence in the dynamic patterns of stock-bond comovement as well as on the effect of economic variables on this comovement is reported during crisis periods and between different European regions. Our results are of high relevance for investment strategies as well as for policy decisions in the European context.

This paper investigates the existence of predictable patterns in the evolution of the implied correlation series. To this end, alternative time-series specifications are employed to model the correlation dynamics, and the statistical and economic significance of out-of sample forecasts is assessed. The statistical measures provide strong evidence in favor of predictable patterns in the SP however, these profits disappear when transaction costs are incorporated. We conclude that the efficient market hypothesis cannot be rejected.

We propose to adopt high-frequency DCC-MIDAS models to estimate high- and low-frequency correlations in the 10-year government bond spreads for Belgium, France, Italy, the Netherlands, and Spain relative to Germany, from June 1, 2007 to May 31, 2012. The high-frequency component, reflecting financial market conditions, is evaluated at 15-minute frequency, while the low-frequency component, fixed through a month, depends on country-specific macroeconomic conditions. We find strong links between spreads volatility and worsening macroeconomic fundamentals; in presence of similar macroeconomic fundamentals relative spreads move together; the increasing correlation in spreads during the burst of the sovereign debt crisis cannot be entirely ascribed to macroeconomic factors but rather to changes in market liquidity.