Tag Archives: Financial Engineering

Brooklyn Quant Experience Lecture Series: Sanjay Nawalkha

Brooklyn Quant Experience Lecture Series, NYU Tandon

The Department of Finance and Risk Engineering at NYU Tandon School of Engineering, welcomes Sanjay K. Nawalkha, Professor of Finance, University of Massachusetts, to the BQE Lecture Series on Thursday, November 5, 2020, at 6 p.m. on Zoom.

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Meeting ID: 945 2031 9822
Password: BQESN

Title

A Theory of Equivalent Expectations Measures for Expected Prices of Contingent Claims

Abstract

This paper introduces a theory of equivalent expectation measures, such as the R measure and the R1T measure, generalizing the martingale pricing theory of Harrison and Kreps (1979) for deriving analytical solutions of expected prices (both the expected current price and the expected future price) of contingent claims. We also present new R-transforms which extend the Q-transforms of Bakshi and Madan (2000) and Duffie et al. (2000), for computing the expected prices of a variety of standard and exotic claims under a broad range of stochastic processes. Finally, as a generalization of Breeden and Litzenberger (1978), we propose a new concept of the expected future state price density which allows the estimation of the expected future prices of complex European contingent claims as well as the physical density of the underlying asset’s future price, using the current prices and only the first return moment of standard European OTM call and put options.

Bio

Sanjay Nawalkha is a Professor of Finance at the Isenberg School of Management. His areas of research are fixed income valuation, derivative pricing, and asset pricing. Professor Nawalkha chaired the Finance Department at the Isenberg School of Management from Sept. 2011 until August 2018. He has co-authored four books, Dynamic Term Structure Modeling: The Fixed Income Valuation Course (Wiley & Sons, 2007), Interest Rate Risk Modeling: The Fixed Income Valuation Course (Wiley & Sons, 2005), Interest Rate Risk Measurement and Management (Institutional Investors, 1999) and Closed-Form Duration Measures and Strategy Applications (The Research Foundation of the Institute of Chartered Financial Analysts, 1990). He has published over 35 scholarly articles in the areas of term structure modeling, risk management, and arbitrage pricing theory.

Brooklyn Quant Experience Lecture Series: Peter Carr

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.

Brooklyn Quant Experience Lecture Series: Bruno Kamdem

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.

Brooklyn Quant Experience Lecture Series: Weilong Fu

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. 

Brooklyn Quant Experience Lecture Series: Pasquale Cirillo

Brooklyn Quant Experience Lecture Series, NYU TandonDear All,

You are cordially invited to the Brooklyn Quant Experience Lecture Series (BQE) on Thursday, October 1st at 6 p.m. on Zoom.

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*Please note a meeting password is required for this event.
Meeting ID: 925 3238 8440
Password:
BQEPC

Pasquale Cirillo, Professor of Risk Management at the University of Nicosia, Cyprus, and NYU FRE Boot Camp Instructor, will give the following talk:

Title

From P to Q and Beyond, a Tale of Inequality

Abstract

We use tools from inequality studies, like the Lorenz curve and the Gini index, to study the relation between the market measure P and the risk-neutral measure Q, but we also deal with the share measure and the T-forward measure. This alternative approach to the change of measure operation is extremely useful to understand some profound and non-trivial connections among measures, and—in some cases— it can also simplify pricing problems. No preliminary knowledge of inequality measures will be assumed.

Bio

Pasquale Cirillo is a Professor of Risk Management at the University of Nicosia, Cyprus, where he is also a member of the Institute For the Future. He previously held positions at the Delft University of Technology (NL) and the University of Bern (CH). He has been a visiting scholar of NYU FRE, and one of the instructors of the FRE Summer boot camp. His research interests include quantitative risk management, extreme value theory, and urn models. He has published in top international journals and is currently writing a book on fat tails. Besides his academic career, Pasquale has also collaborated with international institutions and many top private companies and banks as a statistical consultant. His MOOCs in risk management have been attended by more than a hundred thousand students from all over the world. He is a proud amateur cook.

Brooklyn Quant Experience Lecture Series: Steve Heston

Brooklyn Quant Experience Lecture Series, NYU TandonDear All,

You are cordially invited to the Brooklyn Quant Experience Lecture Series (BQE) on Thursday, September 24th at 6 p.m. on Zoom.

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Meeting ID:971 5155 1289
Password:
BQESH

Dr. Steve Heston, Professor of Finance at the University of Maryland, College Park, will give the following talk:

Title:

Option Momentum

Abstract

This paper computes exact returns on equity-V IX option portfolios to investigate momentum in options across different S&P 500 stocks. Stock options with high historical returns continue to outperform options with low returns. This predictability has a quarterly pattern, resembling the pattern of stock momentum found by Heston and Sadka (2008). In contrast to stock momentum, option momentum lasts for up to five years and does not reverse.

The profitability of option momentum is distinct from the profitability of option value, as measured by historical variance divided by current equity-V IX price. It is also not explained by systematic risk, stock characteristics, nor bid-ask spreads.

Bio:

Steve Heston graduated with a BS double major in Mathematics and Economics from the University of Maryland, College Park in 1983. He attended the Graduate School of Industrial Administration and earned an MBA in 1985 followed by a Ph.D. in Finance in 1990. He has held previous faculty positions at Yale, Columbia, Washington University, and the University of Auckland in New Zealand. He has worked in the private sector with Goldman Sachs in Fixed Income Arbitrage and in Asset Management Quantitative Equities. He is known for analyzing options with stochastic volatility and international stock risk.

Brooklyn Quant Experience Lecture Series: Jon Hill

Brooklyn Quant Experience Lecture Series, NYU TandonDear All,

You are cordially invited to the Brooklyn Quant Experience Lecture Series (BQE) on Thursday, September 17th at 6 p.m.  on Zoom.

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*Please note a meeting password is required for this event.
Meeting ID:958 8116 1956
Password:
BQEJH

Dr. Jon Hill, NYU Tandon Adjunct Professor, will give the following talk:

Title:

A Smarter Model Risk Management Discipline Will Follow From Making Smarter Models

Abstract

What if a financial firm decided to delete its entire set of models and redevelop them from scratch. What might it do differently in the process of rebuilding its entire model eco-system in order to avoid and leverage from some of its previous mistakes? How could such a firm make the Model Risk Management (MRM) platform smarter and less resource intensive than it was before?

This article describes one forward-looking possibility for making the manually intensive practice of MRM smarter by building models that are smarter in the sense of having a rudimentary level of ‘self-awareness’. Similar to the ways that tech firms have tracked the usage of their smartphones, cars, laptop computers and printers for many years, active intelligent agents embedded in model source code can support the creation of a dynamic model inventory to serve as a repository of historical data that accurately describes how, when and where a firm’s models are used and to diagram firm-wide inter-dependencies between data and models.

Keywords: model risk management, governance, validation, dynamic model inventory, model usage, transponder function, model-embedded, active intelligent agents, machine learning, big data, SR11-7, OCC2011-16.

Bio:

Jon leads the New York Chapter of the Model Risk Managers International Association. With over twenty years of experience in diverse areas of quantitative finance, Jon is recognized as a subject matter expert in model risk management, governance and validation and is the author of numerous publications on these topics. Jon is also an adjunct professor in NYU’s Financial Risk Engineering Dept. where he teaches a graduate course in Advanced Model Risk Management, Governance and Validation.

Jon holds a Ph.D. in Biophysics from the University of Utah. He is a frequent speaker and chairperson at model risk conferences throughout the US and Europe.

Financial Engineering Seminar Series

Adding Optionality

Stevens Institute of Technology

Einstein’s velocity addition formula keeps the “sum” of two velocities inside [-c,c], where c is the speed of light. Similarly, a $1 bet that a security will be priced below a threshold must have a value inside [-1,1] . We explore the consequences of reducing derivative security valuation to a generalized sum. We find in particular that the value of repeated optionality is just repeated generalized summation. As a result, we can value particular kinds of Bermudan options in closed form and hedge them with vanilla.

August 27, 2020

5:00 PM – 6:00 PM
Online Zoom discussion
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Presenter: Peter Carr

Peter CarrDr. Peter Carr has been chair of the finance and risk engineering department at NYU Tandon School of Engineering for the last four years. He also presently serves as a trustee for the National Museum of Mathematics and WorldQuant University. Previously, he had a 20-year career heading quant groups in finance. Prior to joining the financial industry, Dr. Carr was a finance professor at Cornell University, after obtaining his Ph.D. from UCLA in 1989. He has more than 90 publications in academic and industry-oriented journals and serves as an associate editor for eight journals related to mathematical finance. He was selected as Quant of the Year by Risk Magazine in 2003 and Financial Engineer of the Year by IAQF/Sungard in 2010.

About this series

The Financial Engineering Seminar Series is a recurring event featuring thought leaders from industry and academia, who bring their experiences to a variety of important topics in this discipline. For more on financial engineering at Stevens, visit the master’s program homepage.

[Virtual] Brooklyn Quant Experience Lecture Series: Agostino Capponi

In light of NYU’s ongoing response to COVID-19, our BQE Lecture Series will take place virtually using Zoom. 

Brooklyn Quant Experience Lecture Series, NYU TandonDear All,

You are cordially invited to the Brooklyn Quant Experience Lecture Series (BQE) on Thursday, April 23rd at 6 p.m.  

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*Please note a meeting password is required for this event.
Meeting ID: 433 746 420
Password: 003405

Dr. Agostino Capponi will present a talk on the following topic:

Title:

Personalized Robo-Advising: Enhancing Investment through Client Interactions

Abstract

Automated investment managers, or robo-advisors, have emerged as an alternative to traditional financial advisors. Their viability crucially depends on timely communication of information from the clients they serve. We introduce and develop a novel human-machine interaction framework, in which the robo-advisor solves an adaptive mean-variance control problem with the risk-return tradeoff dynamically updated based on the risk profile communicated by the client. We quantify the tradeoff between more frequent interactions, which allow the robo-advisor to construct a portfolio tailored to the client’s risk profile, and less frequent communication, which mitigates the effect of behavioral biases in the client’s risk profile. We show that a high frequency of interaction may have the unintended consequence of lowering the Sharpe ratio of the optimal investment strategy. (joint work with S. Olafsson and T. Zariphopoulou)

Bio:

Agostino Capponi is an Associate Professor in the Department of Operations Research at Columbia University, and a member of the Data Science Institute. He also serves as a consultant at the U.S. Commodity Futures Trading Commission, Office of the Chief Economist, on topics related to clearinghouses and financial stability. Agostino’s current research interests are in systemic risk, networks, market microstructure, and financial technology. Agostino’s research has been funded by NSF, DARPA, the Institute for New Economic Thinking, the Global Risk Institute, the Clearpool Group, and the OCP Group. Agostino’s research has been recognized with the 2018 NSF CAREER award, the JP Morgan AI Research Faculty award, and an honorable mention from the MIT Center for Finance and the Harvard Crowd Innovation Laboratory. Agostino serves or has served on the editorial board of several journals in his field, including Management Science, Operations Research, SIAM Journal in Financial Mathematics, Mathematical Finance, Finance and Stochastics, Mathematics and Financial Economics, Stochastic Systems, and many others. Agostino serves as the chair of the SIAM Activity Group in Financial Engineering, and as the president of the INFORMS Finance Section.

Click on link below for the full spring BQE Lecture Series:
https://engineering.nyu.edu/academics/departments/finance-and-risk-engineering/upcoming-events

[Virtual] Brooklyn Quant Experience Lecture Series: Glenn Shafer

In light of NYU’s ongoing response to COVID-19, our BQE Lecture Series will take place virtually using Zoom. 

Brooklyn Quant Experience Lecture Series, NYU TandonDear All,

You are cordially invited to the Brooklyn Quant Experience Lecture Series (BQE) on Thursday, April 2nd at 6 p.m.  

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Dr. Glenn Shafer will present a talk on the following topic:

Title:

Let’s Replace P-Values with Betting Outcomes!

Abstract

How can we test the constantly fluctuating probabilities that Nate Silver offers for the outcomes of elections and sporting events? The natural (and perhaps only) way is to interpret Silver’s probabilities as betting offers and to bet against him. He fails our test if we multiply our money by a large factor. We can test a statistical hypothesis, as well as the efficiency of a financial market, in the same way. In the case of statistical hypotheses, this leads to a new understanding of likelihood ratios and to an alternative to the notion of power. See Working Paper 54 at www.probabilityandfinance.com and Game-Theoretic Foundations for Probability and Finance (Glenn Shafer and Vladimir Vovk, Wiley, 2019).

Bio:

Glenn Shafer is best known for his work on the Dempster-Shafer theory of belief functions, especially his 1976 book A Mathematical Theory of Evidence. Beginning in the 1980s, Glenn has studied the mathematical, philosophical, and historical foundations of standard probability theory and on its limitations as a theory of evidence. Since the 1990s, he has collaborated with Vladimir Vovk on understanding the benefits of using betting games (as opposed to measure theory) as a mathematical foundation for the standard theory. Glenn began his career as an educator by teaching geometry in Afghanistan in 1968; he subsequently taught at Princeton, the University of Kansas, and Rutgers. From January 2011 to December 2014, he served as dean of the Rutgers Business School.

Click on link below for the full spring BQE Lecture Series:
https://engineering.nyu.edu/academics/departments/finance-and-risk-engineering/upcoming-events