This paper aims at improved accuracy in testing for long-run predictability in noisy series, such as stock market returns. Long-horizon regressions have previously been the dominant approach in this area. We suggest an alternative method that yields more accurate results. We find evidence of...

Univariate dependencies in market volatility, both objective and risk neutral, are best described by long-memory fractionally integrated processes. Meanwhile, the ex post difference, or the variance swap payoff reflecting the reward for bearing volatility risk, displays far less persistent...

Stock market volatility clusters in time, appears fractionally integrated, carries a risk premium, and exhibits asymmetric leverage effects relative to returns. At the same time, the volatility risk premium, defined by the difference between the risk-neutral and objective expectations of the...

Stock market volatility clusters in time, appears fractionally integrated, carries a risk premium, and exhibits asymmetric leverage effects. At the same time, the volatility risk premium, defined by the difference between the risk-neutral and objective expectations of the volatility, features...

Stock market volatility clusters in time, appears fractionally integrated, carries a risk premium, and exhibits asymmetric leverage e®ects relative to returns. At the same time, the volatility risk premium, de¯ned by the di®erence between the risk-neutral and objective expectations of the...

The predictability of long-term asset returns increases with the time horizon as estimated in regressions of aggregated-forward returns on aggregated-backward predictive variables. This previously established evidence is consistent with the presence of common slow-moving components that are...

This paper compares model-based and reduced-form forecasts of financial volatility when high-frequency return data are available. We derived exact formulas for the forecast errors and analyzed the contribution of the "wrong" data modeling and errors in forecast inputs. The comparison is...

The availability of high frequency intra-day observations has created a new paradigm in volatility measurement. New methods in conjunction with high-frequency data allow nonparametric estimation of daily volatility and its forecast, variance-covariance matrices, instantaneous volatility and the...