I introduce a finite-difference solution method based on the envelope condition in continuous-time stochastic dynamic programming problems. The envelope method is easier to code and, in the presence of occasionally binding constraints, faster and more stable than popular methods based on the...

Purpose: Managing stock market risk and making an optimal investment decision in a stock market requires study- ing the dynamics of this market and analyzing the fluctuations of its benchmark index in order to avoid heavy damage in the event of crises. This paper aims to study and analyze the...

Purpose: The purpose of this study is to investigate how ambiguity faced by the risk-neutral manager affects the firm’s optimal level of safety stock. Design/methodology/approach: This study adopts the traditional model of Arrow et al. (1951) and employ a manager who has multiple prior beliefs...

The (R, s, S) is a stochastic inventory control policy widely used by practitioners. In an inventory system managed according to this policy, the inventory is reviewed at instant R; if the observed inventory position is lower than the reorder level s an order is placed. The order’s quantity is...

We show that illiquidity risk matters for asset pricing independently of the specific functional form of the asset pricing model. Employing an out-of-sample non-parametric stochastic discount factor (SDF), that we estimate from portfolio returns of the US equity market, we find that market-wide...

We examine the efficacy with which the CBOE Volatility Index (VIX) predicts future (30 day forward) S&P realized volatility. We find that the VIX’s accuracy is about 63%. An alternative framework that we present, built on asymptotic distribution theory, predicts this volatility with an...

This study investigates the impacts of crude oil-market-specific fundamental factors and financial indicators on the realized volatility of West Texas Intermediate (WTI) crude oil price. A time-varying parameter vector autoregression model with stochastic volatility (TVP-VAR-SV) is applied to...

This paper extends the Bayesian semiparametric stochastic volatility (SV-DPM) model of Jensen and Maheu (2010). Instead of using a Dirichlet process mixture (DPM) to model return innovations, we use an infinite hidden Markov model (IHMM). This allows for time variation in the return density...