The IMS-FPS-2014 Pre-Workshop: High frequency trading: data, models and strategies
The pre-workshop is aimed at practitioners in risk management, academic researchers in financial mathematics, banking industry regulators and advanced graduate students in the area of Finance, Probability and Statistics.
Philip Protter (Columbia University)
Rene A Carmona (Princeton University)
Xin Guo (University of California, Berkeley)
Time: July 2, 2014, 6.00pm-8.20pm, Reception: 8.20pm-9.30pm
Location: UTS - Broadway (Science) - Aerial Function Centre, Building 10, Level 7
Cost: $150. Please click here to register.
(Free entry for registered participants of IMS-FPS 2014 Workshop).
Abstracts of the talks
Philip Protter: Why high frequency trading is controversial
In 1998 the Securities and Exchange Commission (SEC) of the United States authorized the existence of electronic stock exchanges, and high speed trading began shortly thereafter. In the beginning, the trading was fast, in seconds, but today it is very fast, in microseconds. It has evolved to the point where speed is tantamount to profits, often referred to as liquidity profits. In this talk we will explain what liquidity profits are, and also how high frequency traders (HFTs) arrange to receive them. We will also explain a more sinister side of what is transpiring, providing an important explanation as to why it is such a controversial phenomenon. To do this, we use an elementary mathematical analysis, which also provides, inter alia, a method for quantifying the amount of profits obtained, and at whose expense they are obtained.
Rene Carmona: Trading Frictions in High Frequency Markets
Two structural relationships of high frequency markets are proposed and studied. The first is a self-financing wealth equation in which the trader's wealth is decomposed into three components: frictionless wealth, transaction costs and instantaneous adverse selection. The second relationship quantifies this instantaneous adverse selection. We derive these relationships theoretically and test them on empirical data. We conclude by deriving their continuous time counterpart with an application to option pricing under transaction costs and price impact.
Xin Guo: Algorithmic trading: optimal execution vs optimal placement
Algorithmic trading refers to the automatic and rapid trading of large quantities with orders specified and implemented by an algorithm. Roughly speaking, algorithmic trading is based on two different time scales: the daily or weekly scale and a smaller (ten to hundred seconds) time scale. These two time scales essentially reflect the two steps by which the traders slice and place orders. The first step is to optimally slice big orders into smaller ones on a daily basis with the goal to minimize the price impact and/or to maximize the expected utility; the second step is to optimally place the orders within seconds. The former is the well-known optimal execution problem and the latter is the much less-studied optimal placement problem. This talk will review some basic results and models for both the optimal execution and the optimal placement problems.