1. Theme of the Summer School (June 25-29):

Informational and Imperfect Financial Markets

In addition to the courses below, there will be an introduction to backward stochastic differential equations (BSDEs) taught by Romuald Elie (Université Paris-Est Marne-la-Vallée) in the afternoons of June 25 and 26.

Marek Rutkowski (University of Sydney)

Arbitrage Pricing with Funding Costs and Counterparty Credit Risk

Please see this file for an outline of the lectures.

Agnès Sulem (INRIA Paris-Rocquencourt)

Nonlinear Pricing in Imperfect Financial Markets

The aim of the present lectures is to study pricing and hedging issues for various options (European, American, game options) in the case of imperfections on the market. These imperfections are taken into account via the nonlinearity of the wealth dynamics. We moreover include the possibility of a default. In this setting, the pricing system is expressed as a nonlinear g-expectation/g-evaluation induced by a nonlinear BSDE (backward stochastic differential equation) with jumps. A large class of imperfect market models can fit in this framework, including imperfections like different borrowing and lending interest rates, taxes on the profits from risky investments, or the case of a “large investor seller”, in the sense that the trading strategy of the seller affects the market prices. We shall address in particular superhedging issues for American and game options in this context and their links with generalized optimal stopping problems and Dynkin games.

Thaleia Zariphopoulou (University of Texas at Austin)

Abstract of lectures: to be announced.

2. Theme of the Summer School (July 2-6):

Market Microstructure and Algorithmic Trading

Robert Almgren (Quantitative Brokers)

Microstructure from a Trader’s Viewpoint

These lectures will present some aspects of market microstructure that are important for constructing effective trading algorithms. Theory will be developed as much as possible, but the flavor will be practical.

1. Introduction: Structure of double auction markets. Differences between equities, futures, and fixed income. Market data and some of its challenges.
2. Descriptive statistics:  Intraday and cross-asset profiles. Tick size effects. High frequency volatility measurement.
3. Implied quoting in futures markets, and its importance for identifying hidden liquidity.
4. Market simulation for algorithm development: Markov models or data replay?
5. Trading benchmarks, market impact, and optimal algorithm design.

Álvaro Cartea (University of Oxford) and Sebastian Jaimungal (University of Toronto)

Algorithmic and High-Frequency Trading

Please see this file for an outline of the lecture.