This study complements the scarce literature on conditional market timing in the mutual fund industry by assessing determinants of market timing throughout the distribution of market exposure. It builds on the intuition that the degree of responsiveness by fund managers to investigated factors...

We study the intra-horizon value at risk (iVaR) in a general jump diffusion setup and propose a new model of asset returns called displaced mixed-exponential model, which can arbitrarily closely approximate finite-activity jump-diffusions and completely monotone Levy processes. We derive...

The Value at Risk of a portfolio differs from the sum of the Values at Risk of the portfolio's components. In this paper, we analyze the problem of how a single economic risk figure for the Value at Risk of a hypothetical portfolio composed of different commercial banks might be obtained for a...

We investigate the impact of shrinkage estimation techniques for the moments of asset returns on risk-parity portfolios. In contrast to mean-variance portfolios, the risk contributions of individual assets in risk-parity portfolios are fixed a priori. This additional restriction stabilizes...

Value-at-Risk (VaR) forecasting generally relies on a parametric density function of portfolio returns that ignores higher moments or assumes them constant. In this paper, we propose a new simple approach to estimation of a portfolio VaR. We employ the Gram-Charlier expansion (GCE) augmenting...

The activity of any participant in the stock market (from a private non-professional investor to investment companies) is associated with a certain level of risk. An effective mechanism in the context of leveling investment risks can be an assessment of the investment attractiveness of stock...

This study predicts stock market volatility and applies them to the standard problem in finance, namely, asset allocation. Based on machine learning and model averaging approaches, we integrate the drivers’ predictive information to forecast market volatilities. Using various evaluation...

We investigate covariance matrix estimation in vast-dimensional spaces of 1,500 up to 2,000 stocks using fundamental factor models (FFMs). FFMs are the typical benchmark in the asset management industry and depart from the usual statistical factor models and the factor models with observed...

We show analytically under quite general conditions that implied rates of return based on analysts' earnings forecasts are only a downward biased estimator for future expected one-period returns and therefore not suited for computing market risk premia. The extent of this bias is substantial as...