We find economically and statistically significant gains when using machine learning for portfolio allocation between the market index and risk-free asset. Optimal portfolio rules for time-varying expected returns and volatility are implemented with two Random Forest models. One model is...

Selecting stock portfolios and assessing their relative volatility risk compared to the market as a whole, market indices, or other portfolios is of great importance to professional fund managers and individual investors alike. Our research uses the cross-sectional intrinsic entropy (CSIE) model...

In this paper I describe several methods of volatility estimation. First I focus on the classical parametric methods of variance estimation, such as the historical method, the implied volatility method and GARCH modeling. I also briefly review some stochastic volatility approaches. Then I...

Estimating the volatility from the underlying asset price history for the discrete observations case is a challenging inference problem. Yet it has attracted much research interest due to the key role of volatility in many areas of finance. In this paper we consider the Heston stochastic...

We propose and implement an empirical automatic bias correction (ABC) procedure for correcting the downward bias in the volatility estimators that utilize extreme value of asset prices. The bias originates from the random walk effect. The proposed estimator does not require knowledge of N, the...