An adaptive filtering and control approach to problems in high frequency trading
Abstract/Contents
- Abstract
- Today's security markets for most asset classes including equities, fixed income, currencies, and vanilla derivatives are increasingly dominated by algorithmic and high frequency traders. Market participants routinely place millions of quotes and trades per second with most individual trading decisions being made by smart computer algorithms and automated systems rather than by humans. As these trading systems have become more sophisticated over the last decade, very important but traditionally simple tasks of trade execution and market making now require traders to be smarter and more agile in order to be competitive and profitable. In view of these developments, we propose a novel approach to building trading strategies for both these kinds of traders. For the trade execution problem, in addition to the traditionally studied price impact models, we recognize the impact of trading on volatility, which is in turn related to liquidity. We model this new impact and solve for the optimal trading schedule, which now requires a dynamic response from the trader based on time-varying liquidity conditions. For the market making problem, we propose a flexible market model that not only captures the basic trade-offs faced by a market maker but is also robust to microstructure noise effects and anticipates the time-varying risk factors of volatility and correlation observed in real markets. We provide closed-form solutions describing the optimal quoting strategy in our market model, the first of its kind for making markets in multiple correlated securities. Furthermore, we call upon the most recent advances in sequential Monte Carlo methods (also known as particle filters, used filtering in general non-linear state-space models), employing a computationally efficient Markov Chain Monte Carlo scheme to perform joint filtering and parameter estimation, termed adaptive particle filters. We integrate these adaptive particle filters with our trade execution as well as market making models to filter for unobservable quantities of interest as well as to estimate unknown model parameters simultaneously and in real time. The use of this powerful method bridges some of the gap between theoretical models and applying them in practice, where unobserved quantities and unknown, time-varying parameters are the norm.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2013 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Trichur Subramanian, Abhay |
|---|---|
| Associated with | Stanford University, Department of Management Science and Engineering. |
| Primary advisor | Lai, T. L |
| Thesis advisor | Lai, T. L |
| Thesis advisor | Giesecke, Kay |
| Thesis advisor | Rajaratnam, Balakanapathy |
| Advisor | Giesecke, Kay |
| Advisor | Rajaratnam, Balakanapathy |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Abhay Trichur Subramanian. |
|---|---|
| Note | Submitted to the Department of Management Science and Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2013. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2013 by Abhay Trichur Subramanian
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Also listed in
Loading usage metrics...