Professor Paul Glasserman Speaks at Bloomberg Quant Seminar on Jan 31st 2013
Bloomberg Quant Seminar - Thursday January 31, 2013
The Bloomberg Quant Seminar seminar features two speakers every month and covers a wide range of topics in quantitative finance.
In this first session, chaired by Bruno Dupire, Professors Jim Gatheral and Paul Glasserman will present their recent research.
Register and secure your spot for the first session of the BBQ Seminar, Thursday, January 31, 2013 via BU<GO> on the Bloomberg Professional service or via email to firstname.lastname@example.org with BBQ in the subject.
5:00 pm Check-in, Bloomberg LP, 731 Lexington Avenue, New York, NY 10022
5:30 pm Professor Jim Gatheral, Baruch College, The Volatility Surface: Statics and Dynamics
We will apply recent work on the SVI parameterization to examine the static and dynamical properties of the empirically observed SPX implied volatility surface. In particular, we will show how to calibrate SVI in such a way as to guarantee the absence of static arbitrage, and exhibit a large class of arbitrage-free SVI volatility surfaces with a simple closed-form representation.
6:15 pm Professor Paul Glasserman, Columbia University, Model Risk and Robust Monte Carlo
Financial risk measurement relies on imperfect modeling assumptions, creating model risk. Moreover, optimization decisions, such as portfolio selection, amplify the effect of model error. We develop a framework for quantifying the impact of model error and for measuring risk in a way that is robust to model error. This robust approach starts from a baseline model and finds the worst-case error in risk measurement that would be incurred through a deviation from the baseline model, given a precise constraint on the plausibility of the deviation. Using relative entropy to constrain model distance leads to an explicit characterization of worst-case model errors; this characterization lends itself to Monte Carlo simulation, allowing straightforward calculation of bounds on model error with very little computational effort beyond that required to evaluate performance under the baseline nominal model. This approach goes beyond the effect of errors in parameter estimates to consider errors in the underlying stochastic assumptions of the model and to characterize the greatest vulnerabilities to error in a model. We apply this approach to problems of portfolio risk measurement, credit risk, delta hedging, and counterparty risk. This is joint work with Xingbo Xu.
7:00 pm Cocktail reception
More details at http://www.bloomberg.com/