This thesis studies the impact of various fundamental frictions in the microstructure of financial markets. Specific market frictions we consider are latency in high-frequency trading, transaction costs arising from price impact or commissions, unhedgeable inventory risks due to stochastic volatility and time-varying liquidity costs. We explore the implications of each of these frictions in rigorous theoretical models from an investor's point of view and derive analytical expressions or efficient computational procedures for dynamic strategies. Specific methodologies in computing these policies include stochastic control theory, dynamic programming and tools from applied probability and stochastic processes.
In the first chapter, we describe a theoretical model for the quantitative valuation of latency and its impact on the optimal dynamic trading strategy. Our model measures the trading frictions created by the presence of latency, by considering the optimal execution problem of a representative investor. Via a dynamic programming analysis, our model provides a closed-form expression for the cost of latency in terms of well-known parameters of the underlying asset. We implement our model by estimating the latency cost incurred by trading on a human time scale. Examining NYSE common stocks from 1995 to 2005 shows that median latency cost across our sample more than tripled during this time period.
In the second chapter, we provide a highly tractable dynamic trading policy for portfolio choice problems with return predictability and transaction costs. Our rebalancing rule is a linear function of the return predicting factors and can be utilized in a wide spectrum of portfolio choice models with minimal assumptions. Linear rebalancing rules enable to compute exact and efficient formulations of portfolio choice models with linear constraints, proportional and nonlinear transaction costs, and quadratic utility function on the terminal wealth. We illustrate the implementation of the best linear rebalancing rule in the context of portfolio execution with positivity constraints in the presence of short-term predictability. We show that there exists a considerable performance gain in using linear rebalancing rules compared to static policies with shrinking horizon or a dynamic policy implied by the solution of the dynamic program without the constraints.
Finally, in the last chapter, we propose a factor-based model that incorporates common factor shocks for the security returns. Under these realistic factor dynamics, we solve for the dynamic trading policy in the class of linear policies analytically. Our model can accommodate stochastic volatility and liquidity costs as a function of factor exposures. Calibrating our model with empirical data, we show that our trading policy achieves superior performance in the presence of common factor shocks.