Portfolio optimization under dynamic risk constraints: Continuous vs. discrete time trading

Abstract

Abstract:We consider an investor facing a classical portfolio problem of optimal investment in a log-Brownian stock and a fixed-interest bond, but constrained to choose portfolio and consumption strategies that reduce a dynamic shortfall risk measure. For continuous-and discrete-time financial markets we investigate the loss in expected utility of intermediate consumption and terminal wealth caused by imposing a dynamic risk constraint. We derive the dynamic programming equations for the resulting stochastic optimal control problems and solve them numerically. Our numerical results indicate that the loss of portfolio performance is not too large while the risk is notably reduced. We then investigate time discretization effects and find that the loss of portfolio performance resulting from imposing a risk constraint is typically bigger than the loss resulting from infrequent trading.

Discrete-time portfolio optimization under maximum drawdown constraint with partial information and deep learning resolution

“…To cite just a few, see Lakner (1998), Rogers (2001), Cvitanić et al (2006), Karatzas and Zhao (2001), Bismuth et al (2019), andPham (2019a). Some articles deal with risk constraints in a portfolio allocation framework, see for instance the paper by Redeker and Wunderlich (2018) which tackles dynamic risk constraints and compares the continuous and discrete time trading. Other papers especially focus on drawdown constraints, see in particular the seminal paper by Grossman and Zhou (1993) or Cvitanić and Karatzas (1994).…”