Spring 2016 seminars:
Wednesday, May 18th, 11am Speaker: Zura Kakushadze, Quantigic Solutions and Free University of Tbilisi 
Statistical Risk Models, Billion Alphas, and ... Cancer Signatures We discuss how to construct statistical risk models, including methods for fixing the number of risk factors. One such method is based on eRank (effective rank). One application is computing optimal weights for combining a large number N of alphas. The algorithm does not cost O(N^3) or O(N^2) operations but is much cheaper: optimization simplifies when N is large and the # of operations scales as N. We also present a novel method for extracting cancer signatures from genome data, including identification and removal of somatic mutational noise. We apply nonnegative matrix factorization (NMF) to 1389 genome samples aggregated by 14 cancer types and filtered using our method. The resultant cancer signatures have substantially lower variability than from raw data. The computational cost is cut by a factor ~10. We find 3 novel cancer signatures, including a liver cancer dominant signature (96%) and a renal cell carcinoma signature (70%). 
Wednesday, May 4th, 11am
Speaker: Maxim Bichuch, Johns Hopkins University 
Optimal Investment with Transaction Costs and Stochastic Volatility
Two major financial market complexities are transaction costs and uncertain volatility, and we analyze their joint impact on the problem of portfolio optimization. When volatility is constant, the transaction costs optimal investment problem has a long history, especially in the use of asymptotic approximations when the cost is small. Under stochastic volatility, but with no transaction costs, the Merton problem under general utility functions can also be analyzed with asymptotic methods. Here, we look at the final time optimal investment and consumption problem, when both complexities are present, using separation of time scales approximations. We find the first term in the asymptotic expansion in the time scale parameter, of the optimal value function, consumption, and of the optimal strategy, for fixed small transaction costs. We give a proof of accuracy in the case of fast meanreverting stochastic volatility. Additionally, we deduce the optimal longterm growth rate. This is a joint work with Ronnie Sircar. 
Wednesday, April 27th, 11am Speaker: Serge Resnick, Bloomberg L.P. 
Scaling Properties of the 3/2 Model of Stochastic Volatility: Random Time Scale, SelfSimilarity, and the “Frozen Smile” The socalled 3/2 model of stochastic volatility has been around for a long time, but attracted considerably less attention than its more famous sibling the Heston model. It has been regarded somewhat as a curiosity among the workhorse stochastic volatility models (Heston, SABR) because (1) the closedform option pricing in the 3/2 model uses esoteric special functions, the Euler Gamma of complex arguments and the confluent hypergeometric function; and (2) in the Heston model, it is easier to extend analytic pricing to the case of timedependent model parameters which is so important in applications. However, the 3/2 model turns out to have special features of both theoretical and practical interest: it is selfsimilar, and its scaling properties cause the shortterm limit of its implied volatility skew to behave as a "frozen smile" that moves unchanged with the instantaneous volatility and, moreover, has a simple explicit analytic form. In this talk I am going to consider these and related properties of the 3/2 model that follow from its scaling behavior, show that it is (almost) the only model that possesses the frozen smile property, and discuss possible practical implications of using frozensmile assumptions in actual trading. I also propose a conjecture about arbitragedriven convergence to dynamically consistent frozen smiles that, if supported by empirical data, would make the 3/2 model a natural choice for the markets in which participants use frozensmiletype approximations to price illiquid in and outofthemoney shortdated options. 
Wednesday, April 20th, 11am
Speaker: Peter Cotton, JP Morgan 
Benchmark – An As Yet Speculative Story About How Filtering Theory Changed the Credit Markets In 2009 a small, obscure small company named Julius Finance was rebooted by a large, prestigious private equity firm, Warburg Pincus. The odd marriage resulted in the first industrial scale use of filtering theory in the otherwise quaint bond and CDS markets (and subsequently, Bloomberg’s realtime pricing service BMRK). The company combined inhouse parsing technology with market microstructure filters to shrink the traditional daily cycle of computing hazard and funding curves down to a ten second interval – thus providing unprecedented transparency into both markets. I will discuss some of the issues involved, provide previously unpublished accuracy statistics demonstrating that the machine greatly outperformed seasoned “evaluators” (even when given time) and discuss what I think will be the next big evolution in the credit markets from the mathematical perspective. 
Wednesday, April 6th, 11am
Charles Tapiero, NYU Tandon School of Engineering 
Financial Pricing, MultiAgents and Economic Inequalities Financial Pricing models are essentially based on two approaches: The ArrowDebreu framework and a Utility valuation and consumption. The ArrowDebreu framework is based on assumptions of economic equilibrium resulting in no arbitrage, predictability of ALL future state preferences etc. The Utility approach was extended to a kernel pricing approach (or the CCAPM). The seminar presentation will propose to do the following:

Wednesday, March 23rd, 11am Speaker: Segolene DessertinePanhard, NYU Tandon School of Engineering 
Determinants of the Global MMF's Evolution : a Joint Analysis of Monetary Policy, Regulation and Macro Economic Conditions Prior to the 2008 crisis, money market funds (part of the socalled shadow banking) were traditionally viewed as the safest investment products. But the demise of the Reserve Primary Fund in 2008 highlighted the strong interconnection between these funds with the banking industry, which turned out to be a systemic risk’s contagion channel in periods of stress. The first part of our research work intends to quantify the main determinants of the global money market funds ‘growth (i.e. part of the shadow banking). In an exploratory study, we run some panel data regressions on macro, monetary and regulatory variables to explain MMF’s inflows and outflows. Next, we apply SVAR models on the significant variables to describe the dynamic relationships (Long term and contemporaneous) between MM flows and global financial conditions. The last part of this research assesses the MMF contribution to the global volatility between 2007 and 2015. We perform a hard clustering procedure on VARMGARCH models to cluster volatilities. We identify potential flights to quality and we find that the MMFs are less sensitive to global financial market shocks after the new regulations’ implementation. 
Wednesday, March 9th, 11am
Speaker: Yup Izhakian, NYU Stern and Baruch College 
Risk, Ambiguity, and the Exercise of Employee Stock Options We investigate the importance of ambiguity, or Knightian uncertainty, in executives' stock option exercise decisions. We develop an empirical estimate of ambiguity and include it in regression models alongside the traditional measure of risk, equity volatility. We show that each variable has a significant effect on the timing of option exercises, with volatility causing executives to hold options longer to preserve option value, and ambiguity increasing the tendency for executives to exercise 
Wednesday, February 10th, 11am
Speaker: Konstantinides Dimitrios, Department of Mathematics, University of the Aegean, Karlovassi, Greece 
Asymptotics for Ruin Probabilities in a DiscreteTime Risk Model with Dependent Financial and Insurance Risks In this seminar, we consider some nonstandard renewal risk models with some dependent claim sizes and stochastic return, where an insurance company is allowed to invest her/his wealth in financial assets, and the price process of the investment portfolio is described as a geometric Lévy process. When the claimsize distribution belongs to some classes of heavytailed distributions and a constraint is imposed on the Lévy process in terms of its Laplace exponent, we obtain some asymptotic formulas for the tail probability of discounted aggregate claims and ruin probabilities holding uniformly for some finite or infinite time horizons. 
Follow Us: Facebook Twitter InstagramYouTube