Optimal Dynamic Strategies for Index Tracking and Algorithmic Trading
By: Brian Michael Ward
Publisher: [publisher not identified]
Published: 2017
Place of publication: [New York, N.Y.?]
Language: eng
Description: In this thesis we study dynamic strategies for index tracking and algorithmic trading. Tracking problems have become ever more important in Financial Engineering as investors seek to precisely control their portfolio risks and exposures over different time horizons. This thesis analyzes various tracking problems and elucidates the tracking errors and strategies one can employ to minimize those errors and maximize profit. In Chapters 2 and 3, we study the empirical tracking properties of exchange traded funds (ETFs), leveraged ETFs (LETFs), and futures products related to spot gold and the Chicago Board Option Exchange (CBOE) Volatility Index (VIX), respectively. These two markets provide interesting and differing examples for understanding index tracking. We find that static strategies work well in the nonleveraged case for gold, but fail to track well in the corresponding leveraged case. For VIX, tracking via neither ETFs, nor futures portfolios succeeds, even in the nonleveraged case.

This motivates the need for dynamic strategies, some of which we construct in these two chapters and further expand on in Chapter 4. There, we analyze a framework for index tracking and risk exposure control through financial derivatives. We derive a tracking condition that restricts our exposure choices and also define a slippage process that characterizes the deviations from the index over longer horizons. The framework is applied to a number of models, for example, Black-Scholes model and Heston model for equity index tracking, as well as the Square Root (SQR) model and the Concatenated Square Root (CSQR) model for VIX tracking. By specifying how each of these models fall into our framework, we are able to understand the tracking errors in each of these models. Finally, Chapter 5 analyzes a tracking problem of a different kind that arises in algorithmic trading: schedule following for optimal execution. We formulate and solve a stochastic control problem to obtain the optimal trading rates using both market and limit orders.

There is a quadratic terminal penalty to ensure complete liquidation as well as a trade speed limiter and trader director to provide better control on the trading rates. The latter two penalties allow the trader to tailor the magnitude and sign (respectively) of the optimal trading rates. We demonstrate the applicability of the model to following a benchmark schedule. In addition, we identify conditions on the model parameters to ensure optimality of the controls and finiteness of the associated value functions. Throughout the chapter, numerical simulations are provided to demonstrate the properties of the optimal trading rates.
Notes: Department: Industrial Engineering and Operations Research.

Thesis advisor: Tim Siu-Tang Leung.

Thesis (Ph.D.)--Columbia University, 2017.