Optimal portfolio rules are derived under uncertainty aversion by formulating the portfolio choice problem as a robust control problem. The robust portfolio rule indicates that the total holdings of risky assets as a proportion of the investor's wealth could increase as compared to the holdings...

This paper contains two novelties. First, a unified framework for testing and evaluating the adequacy of an estimated autoregressive conditional duration (ACD) model is presented. Second, two new classes of ACD models, the smooth transition ACD model and the time-varying ACD model, are...

Optimal portfolio rules are derived under uncertainty aversion by formulating the portfolio choice problem as a robust control problem. The robust portfolio rule indicates that the total holdings of risky assets as a proportion of the investor’s wealth could increase as compared to the...

We evaluate the asymptotic and finite-sample properties of direct multi-step estimation (DMS) for forecasting at several horizons. For forecast accuracy gains from DMS in finite samples, mis-specification and non-stationarity of the DGP are necessary, but when a model is well-specified,...

We evaluate the asymptotic and finite-sample properties of direct multi-step estimation (DMS) for forecasting at several horizons. For forecast accuracy gains from DMS in finite samples, mis-specification and non-stationarity of the DGP are necessary, but when a model is well-specified,...

Optimal portfolio rules are derived under uncertainty aversion by formulating the portfolio choice problem as a robust control problem. The robust portfolio rule indicates that the total holdings of risky assets as a proportion of the investor’s wealth could increase as compared to the...

Optimal portfolio rules are derived under uncertainty aversion by formulating the portfolio choice problem as a robust control problem. The robust portfolio rule indicates that the total holdings of risky assets as a proportion of the investor's wealth could increase as compared to the holdings...