Dominant Factor Analysis by Nonlinear Polymodels

Monday, October 9, 2017 - 5:30pm to 6:30pm
Location: 
Keller Hall

Nonlinear polymodels is a factor-based statistical analysis technique, which can be applied, in a broad range of fields. Inspired by pattern recognition methods used in DNA analysis or in hand writing recognition, it is particularly adapted to situations where space-dependent changes of regime modify the relation between dependent variables and their independent drivers, making it difficult for a single multi-factor model to fit all the possible situations that may potentially occur. We shall present in the particular context of financial modeling.

Traditional multi-factor analysis is essentially used in finance in a linear setting. Asset returns are replicated by a linear combination of factor returns. Not only it provides answers to questions related to the statistical behavior of assets with respect to the market, but it is intellectually comfortable, as a portfolio is naturally represented as a reduced "portfolio" of risk factors. However, this representation sadly lacks of any predictive value, especially when we need it​the most, that is, when a crisis is coming. We shall show how nonlinear polymodels provide a reliable solution to the main questions factor analysis aims at addressing:

1) finding the probability distribution of individual asset returns (risk measurement)
2) assessing the impact of a given shift of risk factors (stress testing)
3) estimating the joint probability distribution of family of assets (portfolio risk and optimization)

We shall show how the nonlinear polymodel-based "Dominant Factors™" methodology provides superior portfolio returns, simply thanks to a better control of the downside dynamics, without the procyclicality pitfalls of traditional Markowitz and Black-Litterman methods.

Dominant Factor Analysis by Polymodels PDF

5:00 p.m. Tea, Keller Hall 3-176

5:30 p.m. - 6:30 p.m. Lecture, Keller Hall 3-180

Presenters

Raphael Douady

Raphael Douady

Raphael Douady is a French mathematician and economist specializing in financial mathematics and chaos theory.  He holds the Frey Family endowed chair of quantitative finance at Stony Brook University (SUNY), and is also the international representative (and former academic director) of the Laboratory of Excellence on Financial Regulation (Labex ReFi, a joint initiative of University of Paris 1-Sorbonne, ESCP-Europe, CNAM and ENA) and affiliated with the French National Centre for Scientific Research (CNRS).  He co-founded fin-tech firms Riskdata (1999) and Datacore (2015).  He has more than twenty years of experience in the banking industry (risk management, option models, trading strategies) and thirty-five years of research in pure and applied mathematics.  His work in mathematical finance has focused on extreme risk, for which he developed the theory of polymodels, and on systemic risk and the anticipation of market instabilities and crises. He also authored a seminal article on infinite dimensional interest rate models, and a rating-based credit derivatives model that introduced the notion of “rating surface”. His background in pure mathematics is in dynamical systems, chaos theory and symplectic geometry. He studied at Ecole Normale Supérieure in Paris and earned his PhD in mathematics in 1982 from the University of Paris 7.