A Resource Extraction Model with Technology Adoption under Time Inconsistent Preferences

A two-stage non-standard optimal control problem with time inconsistent preferences is studied. In an infinite horizon setting, a time consistent (sophisticated) decision maker chooses the time of switching between two consecutive regimes. The second regime corresponds to the implementation of a new technology, and a cost must be paid at the switching time. Although the problem is formulated for a general discount function, special attention is devoted to models with nonconstant discounting and heterogeneous discounting. The problem is solved by transforming it into a problem in a finite horizon and free terminal time. The corresponding dynamic programming equations are presented, and conditions for the derivation of the switching time by decision makers with different degrees of sophistication are studied. A resource extraction model with technology adoption is solved in detail. Effects of the adoption of different discount functions are illustrated numerically.

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

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.

ALGORITHMIC TRADING WITH LEARNING

We propose a model where an algorithmic trader takes a view on the distribution of prices at a future date and then decides how to trade in the direction of their predictions using the optimal mix of market and limit orders. As time goes by, the trader learns from changes in prices and updates their predictions to tweak their strategy. Compared to a trader who cannot learn from market dynamics or from a view of the market, the algorithmic trader’s profits are higher and more certain. Even though the trader executes a strategy based on a directional view, the sources of profits are both from making the spread as well as capital appreciation of inventories. Higher volatility of prices considerably impairs the trader’s ability to learn from price innovations, but this adverse effect can be circumvented by learning from a collection of assets that comove. Finally, we provide a proof of convergence of the numerical scheme to the viscosity solution of the dynamic programming equations which uses new results for systems of PDEs.