Monthly Archives: October 2020

Seminar

Brooklyn Quant Experience Lecture Series: Peter Carr

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Brooklyn Quant Experience Lecture Series, NYU Tandon

The Department of Finance and Risk Engineering at NYU Tandon welcomes Peter Carr, NYU Tandon, FRE Department Chair, to the BQE Lecture Series on Thursday, October 29th at 6 p.m. on Zoom.   

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Meeting ID:996 5148 5212
Password: BQEPC

Title

Simple Bermudan Option Pricing

Abstract

Many option contracts allow exercise at two or more future times. While numerical methods handle Bermudan optionality in stride, analytic approaches have historically been cumbersome. We present a particular Bermudan option and a particular valuation model for which Bermudan option pricing uses high school mathematics.

Bio

Peter Carr is the current Chair of the Finance and Risk Engineering Department at NYU Tandon School of Engineering. Prior to joining NYU, he headed various quant groups in the financial industry for twenty years. He has won numerous awards and has many publications in both academic and industry journals. He is currently ranked second in the world by Google Scholar for citations on derivatives and third in the world for citations on quantitative finance.

Seminar

Brooklyn Quant Experience Lecture Series: Bruno Kamdem

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Brooklyn Quant Experience Lecture Series, NYU Tandon

The Department of Finance and Risk Engineering welcomes Bruno Kamdem, Professorial Lecturer, The George Washington University, to the BQE Lecture Series on Thursday, October 22nd at 6 p.m. on Zoom.   

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Meeting ID: 919 9128 5424
Password: BQEBK

Title

Optimal Strategies for Oil Production and Taxation Under Cap‑and‑Trade

Abstract

Rising global oil production is contributing to the increase of domestic revenues through a variety of fiscal mechanisms in many governments around the world. Concomitantly, the global low‑carbon transition is compelling the same governments to require special taxes such as carbon tax on oil fields. This paper develops a repertoire through which an oil field extraction policy and a government fiscal policy are optimal under carbon emissions constraints. Traditional models have not been able to capture the mean reverting regime‑switching jump‑diffusion dimension of the oil price as well as the stochastic differential game aspect of the extraction‑taxation dichotomy under carbon emissions constraints. On the one hand, to account for the sensitivity of the oil price to global and seasonal macroeconomic parameters, we model its evolution as a mean reverting regime‑switching jump‑diffusion process. On the other hand, as oil producing countries rely on taxes levied on oil companies whose aim is to maximize revenues generated from their extracting activities, we categorize our model as a stochastic differential game problem. The existence of a Nash Equilibrium is proven. Value functions of the stochastic differential game problem are characterized as the unique viscosity solutions of the corresponding Hamilton Jacobi Isaacs equations. Under Greenhouse gas emissions constraints, optimal extraction and fiscal policies are derived. In the light of a numerical blueprint, our analysis holds that a strategic cap‑and‑trade fiscal protocol under which oil producing governments and oil companies efficiently cooperate can yield positive effects on climate policies.

Bio

Bruno Kamdem is the Co‑founder and Principal of Lepton Actuarial & Consulting, LLC (http://leptonactuarial.com/), a New York based professional firm. Dr. Kamdem concomitantly teaches at the George Washington University, School of Engineering and Applied Science in the department of Engineering Management & Systems Engineering. Prior to consulting and teaching, he worked with the Office of Research, Evaluation, & Statistics at the Social Security Administration where he advised the commissioner on mathematical statistical trends regarding Medical‑ Vocational Guidelines and formulated models involving retirement probabilities for multiple years designed for optimizing individual retirement decisions. Bruno has published articles at the “Renewable & Sustainable Energy Reviews” (Impact Factor: 12.110) and the “Energy Policy” journal, along with two forthcoming papers at the “Review of Economics and Statistics” and “Econometrica”. For several years, Bruno has accumulated experience in teaching and in working with Analysis & Modeling tools (iThink, GAMS, MINITAB, MATHEMATICA, MAPLE), Applications & Operating Systems (System Dynamics, SAS‑Visual Analytics, e‑Enterprise, MATLAB‑Simulink), and Data Management applications (VBA, R, SAS, MATLAB). Bruno’s background encompasses a Ph.D. (Systems Engineering, Operations Research) from the School of Engineering and Applied Science at the George Washington University, an M.S. (Applied Mathematics), and B.S. (Mathematics & Economics), both from the University of Maryland, Baltimore County.

Seminar

Brooklyn Quant Experience Lecture Series: Weilong Fu

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Brooklyn Quant Experience Lecture Series, NYU TandonDear All,

You are cordially invited to the Brooklyn Quant Experience Lecture Series (BQE) on Thursday, October 15th at 6 p.m. on Zoom.

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*Please note a meeting password is required for this event.
Meeting ID: 921 6010 6824
Password: BQEWF

Weilong Fu is a Ph.D. Candidate in the Department of Industrial Engineering and Operations Research at Columbia University

Title

Fast Pricing of American Options Under Variance Gamma

Abstract

We investigate methods for pricing American options under the variance gamma model. The variance gamma process is a pure-jump process that is constructed by replacing the calendar time by the gamma time in a Brownian motion with drift, which makes it a time-changed Brownian motion. In the case of the Black-Merton-Scholes model, there are fast approximation methods for pricing American options, but they cannot be utilized for the variance gamma model. We develop a new fast and accurate approximation method inspired by the quadratic approximation to get rid of the time steps required in finite difference methods and simulation methods while reducing the error by making use of a machine learning technique on pre-calculated quantities. We compare the performance of our method with those of the existing methods and show that this method is efficient and accurate for practical use.

Bio

Weilong is a fourth-year Ph.D. candidate at Columbia University in the Department of Industrial Engineering and Operations Research. Before that, he received his bachelor’s degree in Statistics from Peking University. Weilong’s research interest is focused on computational and quantitative finance. 

Seminar

Brooklyn Quant Experience Lecture Series: Conall O’Sullivan

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Brooklyn Quant Experience Lecture Series, NYU TandonDear All,

You are cordially invited to the Brooklyn Quant Experience Lecture Series (BQE) on Thursday, October 8th at 6 p.m. on Zoom.

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*Please note a meeting password is required for this event.
Meeting ID: 919 9128 5424
Password: BQEBK

Conall O’Sullivan, Assistant Professor in Finance at the UCD Michael Smurfit Graduate Business School, and NYU FRE collaborator to the BQE Lecture Series.

Title

Option-Implied Quantiles and Market Returns

Abstract

Disaster risk embedded in short-term option contracts is reflected in the long-term equity risk premium (ERP). A novel formula is proposed to identify risk-neutral return quantiles from European option prices in a model-free manner. We use this formula to extract risk-neutral return quantiles on the S&P 500 index from January 1996 to June 2019. In univariate predictive regressions, we find the difference between 5% and 95% risk-neutral quantiles, which we call the tail difference (TD), significantly predicts equity risk premiums at horizons of more than one year based on a variety of standard error estimates. In bivariate predictive regressions, TD is found to be complementary to the variance risk premium (VRP) of Bollerslev, Tauchen, and Zhou (2009) and Carr and Wu (2009), which is a significant predictor of the ERP at shorter horizons. The stochastic disaster risk consumption-based asset pricing model of Wachter (2013) is used to motivate our empirical findings.

Bio

Conall O’Sullivan graduated with a double major in Mathematics and Physics from University College Dublin (UCD) and subsequently obtained a Ph.D. in Finance from the UCD Michael Smurfit Graduate Business School. After a spell in the asset management industry working in quantitative strategy, Conall became an Assistant Professor in Finance at the UCD Michael Smurfit Graduate Business School. His primary research interests are in derivatives and fixed income markets. Recent research has been published in the Journal of Banking and Finance, Quantitative Finance, the International Journal of Theoretical & Applied Finance, and the Journal of Computational Finance. Conall was a visiting scholar at New York University’s Finance and Risk Engineering department in 2019 and is an instructor on their MSc in Financial Engineering boot camp.

Seminar

Cornell – Citi Financial Data Science Webinars

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Cornell Engineering. Operations Research and Information Engineering. Financial Engineering Manhattan

You and your colleagues are invited to attend the Cornell – Citi Financial Data Science Webinars. Through the online talks this semester, we are excited to collaborate with Citi in highlighting machine learning applications in finance.

All webinars are from 5:00 pm to 6:00 pm.

This webinar is free and open to all guests. Registration is required (RSVP). You will receive the webinar link and dial-in info upon registration.

Date: Tuesday, Oct. 6th, 2020
Time: 5:00 pm – 6:00 pm EDT
Speaker: Rama Cont | Oxford University
Title: “Cross-Impact in Equity Markets” – Joint work with Francesco CapponiData Science in Financial Markets: Hype vs. Useful Practical Reality

Abstract

The empirical finding that market movements in stock prices may be correlated with the order flow of other stocks has led to the notion of “cross-impact” and has prompted the development of multivariate models of market impact. These models are parameterized by a matrix of impact coefficients whose off-diagonal elements are meant to capture how trades in one asset influence the price of other assets, leading to a large number of ‘cross-impact’ parameters which may not be identified solely based on the covariance of returns with order flow. Moreover, empirical evidence suggests that these cross-impact terms are unstable and change signs randomly over time, which poses a problem for their interpretation and use.

We show that the observed correlations between the returns of an asset and the order flow imbalance (OFI) of other assets have a simpler explanation in terms of common components in order to flow across stocks. This commonality in order flow arises naturally from multi-asset trading strategies such as index or ETF portfolios. We provide empirical evidence from order flow and price changes of NASDAQ-100 stocks to support this explanation. Our results show the main determinants of impact to be each stock’s own order flow imbalance (OFI) and the common component of OFI across stocks. Additional ‘cross-impact’ terms account for less than 1% of the total impact. This leads to a parsimonious approach for modeling multi-asset impact, which does not require introducing any “cross-impact” coefficients

Speaker Bio

 Rama Cont is a Professor of Mathematics and Chair of Mathematical Finance at Oxford University. He has held previous positions at Columbia University, Imperial College London, Ecole Polytechnique, and Sorbonne University, and has served as an advisor to IMF, ECB, CME, ICE Clear, Norges Bank, Bovespa, and the US Office of Financial Research. His research focuses on stochastic processes and mathematical modeling in finance, with a focus on market instabilities and systemic risk.

He is a recipient of the Louis Bachelier Prize (2010) and the Royal Society Award for Excellence in Interdisciplinary Research (2017), and was elected Fellow of the Society for Industrial and Applied Mathematics (SIAM) in 2017 for his ‘contributions to stochastic analysis and mathematical modeling in finance.’

We hope to see you online!

The Cornell-Citi Team

**Please excuse any duplication of this announcement

Previous CFEM Events

Sep. 1st, 2020
Speaker: Michael Rabadi (Balyasny Asset Management)

Upcoming CFEM Events

Nov. 17, 2020
Speaker: Paul Besson (Euronext)