Abstract

http://ssrn.com/abstract=2010417
 
 

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Risk Metrics and Fine Tuning of High Frequency Trading Strategies


Álvaro Cartea


University College London

Sebastian Jaimungal


University of Toronto - Department of Statistics

February 24, 2012

Forthcoming, Mathematical Finance

Abstract:     
We propose risk metrics to assess the performance of High Frequency (HF) trading strategies that seek to maximize profits from making the realized spread where the holding period is extremely short (fractions of a second, seconds or at most minutes). The HF trader maximizes expected terminal wealth and is constrained by both capital and the amount of inventory that she can hold at any time. The risk metrics enable the HF trader to fine tune her strategies by trading off different metrics of inventory risk, which also proxy for capital risk, against expected profits. The dynamics of the midprice of the asset are driven by information flows which are impounded in the midprice by market participants who update their quotes in the limit order book. Furthermore, the midprice also exhibits stochastic jumps as a consequence of the arrival of market orders that have an impact on prices which can give rise to market momentum (expected prices to trend up or down). The HF trader's optimal strategy incorporates a buffer to cover adverse selection costs and manages inventories to maximize the expected gains from market momentum.

Number of Pages in PDF File: 37

Keywords: Algorithmic Trading, High Frequency Trading, Momentum Trading, Market Impact, Adverse Selection, Risk Metrics, Inventory Risk

JEL Classification: C6, C61, D81, G1, G13

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Date posted: February 26, 2012 ; Last revised: October 5, 2012

Suggested Citation

Cartea, Álvaro and Jaimungal, Sebastian, Risk Metrics and Fine Tuning of High Frequency Trading Strategies (February 24, 2012). Forthcoming, Mathematical Finance. Available at SSRN: http://ssrn.com/abstract=2010417 or http://dx.doi.org/10.2139/ssrn.2010417

Contact Information

Álvaro Cartea (Contact Author)
University College London ( email )
Gower Street
London, WC1E 6BT
United Kingdom
HOME PAGE: http://www.cartea.net
Sebastian Jaimungal
University of Toronto - Department of Statistics ( email )
100 St. George St.
Toronto, Ontario M5S 3G3
Canada
HOME PAGE: http://www.utstat.utoronto.ca/sjaimung
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