Financial Engineering


The stability of financial markets benefits billions of people. In order to respond to the challenge of maintaining healthy and stable markets, today’s systems engineers must possess quantitative and business know-how to understand and manage the complexity of financial instruments and inter-bank dynamics.

Systems engineers master the core skills of modelling economic and human behaviours, and provide insights regarding how to reach economic, social and individual investors’ objectives.

Financial engineering covers modelling, analysis, implementation of financial decision making and risk management. More than just theories, systems engineers develop practical tools with a combination of multiple disciplines including statistics, probability, optimization and stochastic analysis. Related research topics include pricing and hedging, systematic risk management, stochastic volatility models, and portfolio choice.



First-Loss Capital

X. He
In most U.S. hedge funds, the managers take a performance fee, such as 20%, for any profit they generate for the investors but do not pay in case of a loss. In China private equities and also in some new hedge funds in the United States, the managers, however, need to provide a first-loss capital to absorb the investors’ loss and charge a performance fee at a higher rate, e.g., 40%. We study how the first-loss capital can reduce fund risk, improve the well-being of the managers and  investors, and separate skilled managers from unskilled ones. 

High Frequency Trading
N. Chen
High frequency trading (HFT) is to use computers to process market information and make elaborate decisions to "initiate buy/sell orders. As of July 2009, HFT firms account for 73% of all US equity trading volumes." We study how to develop realistic and analytically tractable models for the dynamics of order-driven trading systems. The implications on optimal execution and investment strategies will be investigated.

Limit order books

X.F. Gao
As a trading mechanism, limit order books have gained growing popularity in equity and derivative markets in the past two decades. The objective of this project is to understand deeper on different time scales, how the price is driven by supply and demand, which is expressed in the geometric property of the time-varying order book shape.

Mining Streams of Financial Data and News
J. Yu
Financial market trends prediction is a technique to forecast market trend changes, which assists financial market participants to spot arbitrage opportunities for investment. Currently, most existing reported data mining studies for trend prediction focused on the time-series perspectives. However, there are numerous social factors that contribute to financial market trends prediction, but cannot be obtained from or represented in time-series data. First, in order to effectively predict market trends, one main objective of this project is to develop new data mining techniques that deal with two different types of data, namely financial data (time-series data or simply data) and news articles (textual data or simply text) concurrently. Second, stock market traders need to monitor tens of thousands of data/text sources coming as open-ended data/text streams in an on-line fashion, and need to analyse and make decisions based on the data/text streams they have received as soon as they can. We will study trend predictions by investigating the above two interrelated issues and finding associations among multiple data/text streams.

Modeling Time Dependency in Financial Engineering
A fundamental task in financial engineering is to develop empirically realistic as well as tractable derivative models. For tractability reasons many standard models are assumed to have time-homogeneous local characteristics (i.e. drift, diffusion coefficient, jump measure), which however, are undesirable from the empirical standpoint in many applications, as they cannot capture time dependent behavior such as seasonal spikes observed in electricity spot prices, and cannot achieve satisfactory results in calibrating the term structure of interests (e.g. implied volatilities). The aim of this project is to study the theory and applications of a new technique called additive subordination for modeling time dependency in financial engineering

Realization Utility

X. He
Individual investors derive realization utility: every time they buy a stock, an investment account is created in their mind and will be closed when the stock is sold. They feel good with a realized gain and bad with a realized loss. In this project, we study how the derivation of realization utility affects the investors’ trading behavior and accounts for various empirical findings such as disposition effect. 

Spectral Methods for Optimal Decision and First Passage Problems
L.F. Li
We develop a new method based on spectral analysis to solve optimal decision problems including optimal stopping, optimal switching and stochastic games, and first passage problems for a rich class of Markov diffusions, jump-diffusions and pure jump processes, which are building blocks for empirically realistic financial models. These problems arise in a variety of applications in financial engineering, including evaluating financial contracts with early exercise rights or/and with barriers, such as American-style options, barrier options, callable and puttable bonds and convertible bonds, and real options arising in commodity extraction, power generation, optimal investment or divestment timing, and other irreversible decisions.