Seminars
The Quantitative Finance Center of AMS is dedicated to connecting its faculties and students to both the academic world and the real finance world, such as Wall Street. Weekly seminars serve this purpose.
Wednesday, February 29, 2012, Time from 2:00PM -3:00PM, Location: Math Tower, Seminar Room 1-122
Speaker: Alan De Genaro Dario
Courant Institute of Mathematical Sciences - NYU
Title: Properties of Doubly Stochastic Poisson Processes with Affine Intensities
Abstract:
This paper discusses properties of a Doubly Stochastic Poisson Process (DSPP) where the intensity process belongs to a class of affine diffusions. For any intensity process from this class we derive an analytical expression for probability distribution functions of the corresponding DSPP. A specification of our results is provided in a particular case where the intensity is given by one-dimensional Feller process and its parameters are estimated by Kalman filtering for high frequency transaction data.
Speaker: Yuedong Wang
Chair, Dept of Statistics & Applied Probability, University of California Santa Barbara
Title: Nonparametric Nonlinear Regression Models
Abstract:
Almost all of the current nonparametric regression methods such as
smoothing splines, generalized additive models and varying coefficients
models assume a linear relationship when nonparametric functions are
regarded as parameters. In this talk we present a general class of
nonparametric nonlinear models that allow nonparametric functions to
act nonlinearly. They arise in many fields as either theoretical or
empirical models. We propose new estimation methods based on an extension
of the Gauss-Newton method to infinite dimensional spaces and
the backfitting procedure. We extend the generalized cross validation
and the generalized maximum likelihood methods to estimate
smoothing parameters. Connections between nonlinear nonparametric
models and nonlinear mixed effects models are established. Approximate
Bayesian confidence intervals are derived for inference. We will also
present a user friendly R function for fitting these models.
The methods will be illustrated using two real data examples
Tuesday, May 8, 2012, 2:00 PM - 3:00 PM, Math Tower, Seminar Room 1-122
Speaker: Alexander MelnikovProfessor, University of Alberta, Edmonton, Canada
E-mail: melnikov@ualberta.ca
Title: On Quantitative Risk-Management in Equity-Linked Life Inurance
Abstract:
In the talk we study equity-linked life insurance contracts with fixed and stochastic guarantees linked to the evolution of a financial market. The presence of a client’s mortality risk does not allow perfect hedging, and we utilize imperfect hedging methodologies. These methodologies were developed in mathematical finance based on loss function conceptions (quantile and efficient hedging) and risk measures. We allow an insurance company to be exposed to a financial risk. The price of the contracts will be subject to a maximization/minimization of the expected loss function/risk measure under initial budget constraints. In the Black-Scholes and jump-diffusion setting we derive equations separating financial and insurance risks embedded in the contracts and propose a methodology for effective risk-management of the contracts. Pooling homogeneous clients together enables the insurance company to take advantage of diversification of a mortality risk. A large enough portfolio of life insurance contracts will result in a more predictable mortality exposure and reduced prices. The results will be illustrated with the help of financial indices (S&P 500 and the Russell 2000).
Title: Quantitative Challenges in Algorithmic Execution
Professor Robert Almgren of NYU Courant Institute
Title: Challenges in Pricing Mortgage Backed Securities
Dr. Ying Chen, Former JP Morgan Analyst
Title: Option Pricing Under a Stressed-Beta Model
Adam Tashman, UC-Santa Barbara Department of Statistics and Applied Probability
Title: Market Crashes and Modeling Volatile
Professor Svetlozar Rachev
School of Economics and Business Engineering
University of Karlsruhe, Germany
Title: American Options: Free-Boundary-Value Problems in Finance
Qiang Zhang
Department of Mathematics
David
Cru
Ph.D. Candidate SUNY Stony Brook
Asst. Vice President, Ivy Asset Management
Michael
Driscoll, Ph.D
Managing Director at Cogent Partners
Wednesday,
September 30, 2009, 3:50 - 5:10 pm, Math Common Room 4-125
Andrew
P. Mullhaupt, Ph.D.
Former Director of Research and Portfolio Manager at SAC Meridien Fund
Wednesday,
October 28th, 2009 3:50PM - 5:10PM, Physics Tower S-240
Ann Tucker, Ph.D.
Executive Director of the Center for Quantitative Finance in the Department
of Applied Mathematics and Statistics at SUNY Stony Brook
Tuesday, March 23, 2010, 2:30 pm, AMS Seminar Room 1-122
Title: Risk Management and Real Life
Eugene Stern
Research Group, RiskMetrics
Monday, April 19, 2010, 2:15pm, AMS Seminar Room 1-122
Title: Operational Risk Assessment
Advanced Statistical Methodology and its Practical Implementation
Prof. Dr.Sci. Svetlozar Rachev
University of Karlsruhe, Germany
The main topics of this talk include:
a. Compound Cox process models for operational losses;
b. Fitting loss distributions to truncated and full operational loss data;
c. Fitting non-homogeneous Poisson process models to operational frequency data;
d. Applications of heavy-tailed -stable distributions to loss data;
e. Estimation of the dependence (copula) structure of losses from various business lines and event-types;
f. Forecasting of one-period ahead Value-at-Risk and Expected Tail Loss for (i) every individual business-line, event-type, and for (ii) the total operational loss from all business-lines and event types;
g. In-sample goodness-of-fit tests (such as Kolmogorov-Smirnoff, Kuiper, Anderson-Darling, Cramer-von Mises);
h. Backtesting;
i. Robust modeling techniques and comparative analysis with classical
models.
Wednesday, Sept 15, 2010, 3:00pm, AMS Seminar Room 1-122
Title: Algorithmic Trading for Interest Rate FuturesAbstract: Interest rate futures markets present several novel microstructural features, not found in equities and foreign exchange markets. For algorithmic trading, these features must be fully understood and properly exploited. Three features are the most important. First is pro rata order matching, which has strong effects on the optimal order placement strategy. Second is implied quoting via calendar spread and butterfly contracts, which presents opportunities to find hidden liquidity and better order fills. Third is the highly coupled nature of contracts at different points on the yield curve, requiring an inherently multidimensional analysis even to trade a single contract. We shall provide an overview of all these aspects, and the quantitative tools that are used to model them.
Speaker Bio: Robert Almgren, co-founder of Quantitative Brokers, providing agency algorithmic execution and cost measurement in fixed income markets. Until 2008, Dr Almgren was a Managing Director and Head of Quantitative Strategies in the Electronic Trading Services group of Banc of America Securities. From 2000-2005, he was a tenured Associate Professor of Mathematics and Computer Science at the University of Toronto, and Director of its Master of Mathematical Finance program. Before that, he was an Assistant Professor of Mathematics at the University of Chicago and Associate Director of the Program on Financial Mathematics; he is currently a Fellow in the Mathematics in Finance Program at New York University. Dr. Almgren holds a B.S. in Physics and Mathematics from the Massachusetts Institute of Technology, an M.S. in Applied Mathematics from Harvard University and a Ph.D. in Applied and Computational Mathematics from Princeton University. He has an extensive research record in applied mathematics, including several papers on optimal securities trading, transaction cost measurement, and portfolio formation.
Wednesday, Sept 29, 2010, 3:00pm, AMS Seminar Room 1-122
Speaker: Greg Frank
Title: "Using Database Systems for Tick Data Mining"
Abstract: As high frequency traders of instruments in various asset classes, we are faced with the challenge of analyzing the characteristics of vast quantities of data. Tools like Matlab and Quantlib are great for quickly investigating high order relationships in financial data. But how does one approach analysis when data sets run into terabytes? And what about when the data is streaming in real-time?
In this talk, we'll take a practical look at how common relational database systems and commercial business intelligence platforms can be used for analyzing tick data. We'll take a look at how various estimation and classification techniques like Logistic Regression or ARIMA can be deployed - and their relative performances compared - with common database tools.
In some asset classes, such as spot FX, getting the data itself into a form that can be analyzed with traditional techniques poses a challenge. Price updates are irregularly spaced in time, there are data drop-outs and spurious "zero" prices, and because FX is traded between banks rather than on an exchange, there is no centrally authoritative source for reporting what the "correct" numbers are. Yet the mathematical techniques we use usually only work correctly on regularly spaced, clean, accurate input data. We'll look at some lessons learned for basic data conditioning, which we view as an important step to real-world financial data analysis.
About the speaker: Greg Frank is a founding partner of Presagium, a proprietary trading firm. Previously, he was managing partner of Ovation Capital, a venture capital firm investing in software companies. He is chairman of Connectiva Systems, a 400-person company providing revenue assurance and fraud management solutions to telecommunications companies globally. He has held senior positions at Microsoft in Redmond, WA, and at Murray & Roberts. Greg has an MBA from Harvard Business School and a degree in electronic engineering from the University of Cape Town, South Africa. He is a recreational glider pilot and distance runner, and lives in Manhattan with his wife and two sons.
Wednesday, March 16, 2011, 1:00-2:00pm, Math Tower 1-122
Speaker: Yuedong Wang
Chair, Dept of Statistics & Applied Probability, University of California Santa Barbara
Title: Nonparametric Nonlinear Regression Models
Abstract:
Almost all of the current nonparametric regression methods such as
smoothing splines, generalized additive models and varying coefficients
models assume a linear relationship when nonparametric functions are
regarded as parameters. In this talk we present a general class of
nonparametric nonlinear models that allow nonparametric functions to
act nonlinearly. They arise in many fields as either theoretical or
empirical models. We propose new estimation methods based on an extension
of the Gauss-Newton method to infinite dimensional spaces and
the backfitting procedure. We extend the generalized cross validation
and the generalized maximum likelihood methods to estimate
smoothing parameters. Connections between nonlinear nonparametric
models and nonlinear mixed effects models are established. Approximate
Bayesian confidence intervals are derived for inference. We will also
present a user friendly R function for fitting these models.
The methods will be illustrated using two real data examples.