Tag Archives: Financial Engineering

Seminar

Brooklyn Quant Experience Lecture Series: Ioana Boier

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Brooklyn Quant Experience Lecture Series, NYU Tandon

Ioana Boier, Head of Quantitative Portfolio Solutions at Alphadyne Asset Management will give the following talk on Thursday, May 6th at 9:30 AM EDT. 

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Meeting ID: 963 6903 9340
Password: FREBQEIB

Title

Computer Science for Finance (Beyond Programming)

Bio

Ioana Boier is the Head of Quantitative Portfolio Solutions at Alphadyne Asset Management. Prior to joining Alphadyne in 2019, she held senior quantitative research and management roles at Citadel LLC, BNP Paribas, and the IBM T. J. Watson Research Center. Ioana is the author of multiple peer-reviewed publications, patents, and the recipient of several awards for applied research delivered into products. She has a Ph.D. in Computer Science and M.Sc. degrees in Computer Science and Mathematics.

Seminar

Brooklyn Quant Experience Lecture Series: Sandrine Ungari

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This event has been rescheduled to Thursday, May 13th at 9:30 AM EDT. Please see the updated event details below.

Brooklyn Quant Experience Lecture Series, NYU Tandon

Sandrine Ungari, Head of Cross-Asset Quantitative Research Team at Société Générale will give the following talk on Thursday, April  22nd at 9:30 AM EDT. 

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Meeting ID: 953 4085 3209
Passcode: BQESU

Title

A Brief History of Quant Investing – from Traditional Equity Factors to Machine Learning

Abstract

Over the past few decades, systematic quantitative investing has gathered interest from a wide range of investors ranging from hedge funds to asset owners. In this presentation, we review a few of the most emblematic systematic strategies, and discuss their more recent implementations making use of modern statistical learning. Differences in performance across factors and cycles highlight the importance of having a portfolio framework. We show how diversification can be a factor of performance in that field too.

Bio

Sandrine Ungari is currently Head of Cross-Asset Quantitative Research team at Société Générale. The Quantitative Research team has been recognized as a market leader in quantitative research and is the recipient of the 2020 Risk Award for Research House of the Year. Sandrine’s research topics cover systematic strategies across asset classes, interest rate modeling, machine learning, statistical analysis, and portfolio construction. She joined Société Générale in 2006. Prior to that, she worked as a quantitative analyst at HBOS Treasury and at Reech Sungard in London. She is a graduate of ENSTA (Paris) and holds a Master’s in Quantitative Finance from Paris VI University.

Seminar

Brooklyn Quant Experience Lecture Series: George Skiadopoulos

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Brooklyn Quant Experience Lecture Series, NYU Tandon

George Skiadopoulos, Professor of Finance in the School of Economics and Finance, Queen Mary University of London and Department of Banking and Financial Management, University of Piraeus, will give the following talk on Thursday, April  22nd at 9:30 AM EST. 
*Kindly note that we have changed the time to 9:30 AM on Thursdays. The new time change allows our invited international guests to join these important virtual talks.

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Meeting ID: 962 1586 0443
Password: FREBQEGS

Title

The Contribution of Frictions to Expected Returns: An Options-based Estimation Approach

Abstract

We document that properly scaled deviations from put-call parity estimate the contribution of market frictions to expected returns (CFER) accurately, by means of a nonparametric theoretically founded identification strategy. The required conditions are that our estimator predicts the underlying but not the synthetic stock’s return. The data satisfy the two conditions; the alphas of the estimated CFER-sorted spread portfolios are up to 1.86% per month. The estimated CFER covaries non-linearly with proxies of market frictions. An agent-based equilibrium model explains our findings; alphas can be twice as big as the round-trip transaction costs, thus corroborating the accuracy of our estimator.

Bio

George Skiadopoulos is a Professor of Finance at the Department of Banking and Financial Management of the University of Piraeus and at the School of Economics and Finance of Queen Mary University of London. He is also Director and co-Founder of the Institute of Finance and Financial Regulation (IFFR, www.iffr.gr) and an Honorary Senior Visiting Fellow at Business School (formerly Cass) City, University of London.

His research interests and professional expertise lie in asset pricing, commodities, financial derivatives, risk management, and portfolio management. He has published in academic journals, including the Management Science, Journal of Financial and Quantitative Analysis, Journal of Business and Economic Statistics, Journal of Banking and Finance, and the Journal of Financial Markets. He has been awarded research grants by the Chicago Mercantile Exchange Foundation Group, the J.P. Morgan Research Centre in Commodities at University of Denver Colorado, the Athens Derivatives Exchange, and the Portuguese Foundation for Science and Technology (FCT). His work has been featured in CFO Magazine, Economonitor, Forbes, Market Watch, Seeking Alpha, The Verdict Wall Street Journal, and the CFA, Citigroup, and Global Commodities Applied Research Digest Volumes.

Professor Skiadopoulos has been consulting financial institutions. He has also worked as a Research Fellow at the Financial Options Research Centre at Warwick Business School, the R&D Group of the Athens Derivatives Exchange, and he has provided various executive training courses.

He holds a Ph.D. in Finance from the University of Warwick, an M.Sc. In Mathematical Economics and Econometrics from the London School of Economics, and a Ptychion (ranked first in his graduating class) in Economics from the Athens University of Economics and Business. For more information, visit https://sites.google.com/view/george-skiadopoulos.

Seminar

Brooklyn Quant Experience Lecture Series: Sasha Stoikov

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Brooklyn Quant Experience Lecture Series, NYU Tandon

Sasha Stoikov, Senior Research Associate at Cornell Financial Engineering Manhattan (CFEM), will give the following talk on Thursday, April  15th at 9:30 AM EST. 
*Kindly note that we have changed the time to 9:30 AM on Thursdays. The new time change allows our invited international guests to join these important virtual talks.

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Meeting ID: 942 9844 6763
Password: FREBQESS

Title

The Microstructure of Cointegrated Assets

Abstract

I will present a generalization of the micro price to multiple assets. This yields a notion of fair prices, as a function of the observable state of multiple order books. I will show how to compute the microprices of two highly cointegrated assets, using Level-1 data collected on Interactive Brokers. I will then test the model by designing an execution algorithm based on this two-dimensional microprice and show that it can save half of the bid-ask spread cost.

Bio

Sasha Stoikov has 15 years of experience at the interface of academia, startups, and the financial industry. He is a Senior Research Associate at Cornell Financial Engineering Manhattan (CFEM) and was a VP of High Frequency Trading at Cantor Fitzgerald. He has also launched a music tech startup called PIKI.

Seminar

Brooklyn Quant Experience Lecture Series: Samim Ghamami

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Brooklyn Quant Experience Lecture Series, NYU Tandon

Samim Ghamami, Senior Researcher at NYU and UC Berkeley, Senior Economist and Managing Director at the Financial Services Forum, and an Adjunct Professor of Finance at New York University, will give the following talk on Thursday, April  8th at 9:30 AM EST. 
*Kindly note that we have changed the time to 9:30 AM on Thursdays. The new time change allows our invited international guests to join these important virtual talks.

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Meeting ID: 950 8799 6334
Password: FREBQESG

Title

The Impact of Collateral and Stays on Financial Stability

Abstract

We study the spread of losses and defaults in financial networks with two features: collateral requirements and resolution and bankruptcy stay rules. When collateral is committed to a firm’s counterparties, a solvent firm may default if it lacks sufficient liquid assets to meet its payment obligations. Collateral requirements can thus increase the risk of contagion. Moreover, one firm may benefit from the failure of another if the failure frees collateral committed by the surviving firm, giving it additional resources to make other payments. Contract termination at default may also similarly improve the ability of other firms to meet their obligations. As a consequence of these features, the timing of payments and collateral liquidation must be carefully specified to establish the existence of payments that clear the network. Using this framework, we show that committed collateral in the form of initial margin in over-the-counter derivatives markets may increase contagion and financial instability. We also compare networks under different stay rules in OTC markets. Our analysis shows that when firms are not highly leveraged in terms of derivatives transactions, full contract termination may reduce contagion.

Bio

Samim Ghamami is a senior researcher at NYU and UC Berkeley, the senior economist and managing director at the Financial Services Forum, and an adjunct professor of finance at New York University. Ghamami also serves on the advisory board of the Mathematics in Finance Program at the NYU Courant Institute. He has also been a senior financial economist and senior vice president at Goldman Sachs, an associate director, and a senior economist at the U.S. Department of the Treasury, Office of Financial Research, and an economist at the Board of Governors of the Federal Reserve System.

Ghamami’s work has broadly focused on financial economics and more recently on the interplay of finance and macroeconomics. Ghamami has been an advisor to the Bank for International Settlements and has also worked as an expert with the Financial Stability Board on post-financial crisis reforms. He served on the National Science Foundation panel on Financial Mathematics in 2017 and 2018.

Seminar

Brooklyn Quant Experience Lecture Series: Leon Tatevossian

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Brooklyn Quant Experience Lecture Series, NYU Tandon

Leon Tatevossian, NYU Tandon FRE Adjunct Professor, will give the following talk on Thursday, March 25th at 9:30 AM EST. 
*Kindly note that we have changed the time to 9:30 AM on Thursdays. The new time change allows our invited international guests to join these important virtual talks.

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Meeting ID: 949 0708 1079
Password: FREBQELT

Title

Can We Still Blame MBS Hedgers?

Abstract

Part of fixed-income folklore is the idea that directional moves in the rates markets can get exaggerated by the duration-rebalancing decisions of mortgage-backed security (MBS) investors and market-makers.  Under stable regimes, we could posit that this theme resides in the market’s “background noise.”  In those backdrops, there’s a consensus toolkit for interpreting its effect and how it fits with other price drivers.  In periods of harder-to-rationalize repricings in rates space this “convexity effect” attracts more attention:  The risk attributes (such as the distribution across the coupon “stack”) of the outstanding “stock” of MBSs and the sector’s relative value are major determinants of the rebalancing strategy, and thus of the effect’s magnitude.

The advent of the Fed’s quantitative easing (QE) programs (currently taking $40 billion of agency MBSs out of private hands per month, net of principal redemptions) and the continuing sell-down of GSE’s balance sheets mean that there’s a significantly reduced amount of MBS paper held by “players who hedge.”  Can we still blame the MBS hedgers when we experience greater-than-expected rates volatility?

Bio

Leon Tatevossian is a fellow/adjunct instructor in the Mathematics in Finance Program at NYU-Courant Institute. From 2009-16, Leon was a director in Group Risk Management at RBC Capital Markets, LLC, covering market risk for securitized products in secondary-trading, origination, and proprietary-trading areas.  He has twenty-nine years of sell-side experience in the fixed-income markets, including positions as a trader, quantitative strategist, derivatives modeler, and market-risk analyst. His product background includes US Treasury securities, US agency securities, interest-rate derivatives, MBSs, ABSs, and credit derivatives.  Leon graduated from MIT (SB; mathematics); he was a graduate student in mathematics at Brown University.

Seminar

Brooklyn Quant Experience Lecture Series: Viktor Todorov

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Brooklyn Quant Experience Lecture Series, NYU Tandon

Viktor Todorov, Professor of Risk Management and Professor of Finance at the Kellogg School of Management, Northwestern University, will give the following talk on Thursday, March 18th at 9:30 AM EST. 
*Kindly note that we have changed the time to 9:30 AM on Thursdays. The new time change allows our invited international guests to join these important virtual talks.

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Meeting ID: 979 5163 8107
Password: FREBQEVT

Title

Option-Implied Semimartingale Characteristics

Abstract

We propose nonparametric methods for the recovery of the spot semimartingale characteristics of an asset price from noisy short-dated option data. The estimation is based on forming portfolios of options with different strikes that replicate the (risk-neutral) conditional characteristic function of the underlying price in a model-free way. The recovery of spot volatility is done by making use of the dominant role of the volatility in the conditional characteristic function over short time intervals and for large values of the characteristic exponent. The estimation of the tail jump variation measures, on the other hand, is based on their representation as integrals of the Laplace transforms of the jump compensator. The latter are in turn recovered from the second derivative of the option-implied characteristic function estimate, de-biased by its value at high frequencies to account for the diffusive volatility. We apply the estimation techniques to real data and illustrate the use of the extracted option-implied semimartingale characteristics in asset pricing applications.

Bio

Viktor Todorov is Harold H. Hines Jr. Professor of Risk Management and Professor of Finance at the Kellogg School of Management, Northwestern University. Professor Todorov is a Fellow of the Society for Financial Econometrics and the Journal of Econometrics. His research interests are in the areas of theoretical and empirical asset pricing, econometrics, and applied probability. He has published extensively in these fields.

His recent work focuses on the robust estimation of asset pricing models using high-frequency financial data as well as the development and application of parametric and nonparametric methods of inference for studying risks and risk premia using derivatives markets data. He currently serves as a Co-Editor for Econometric Theory and is on the editorial board of a number of leading academic journals, including Econometrica and the Journal of Econometrics. He received his Ph.D. in Economics from Duke University in 2007.

Seminar

Brooklyn Quant Experience Lecture Series: Roza Galeeva

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Brooklyn Quant Experience Lecture Series, NYU Tandon

Roza Galeeva, Adjunct Professor, NYU Tandon FRE, will give the following talk on Thursday, March 11th at 9:30 AM EST. 
*Kindly note that we have changed the time to 9:30 AM on Thursdays. The new time change allows our invited international guests to join these important virtual talks.

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Meeting ID: 928 3123 9504
Password: FREBQERG

Title

In Pursuit of Samuelson: Studies of Commodity Volatilities and Correlations

Abstract

Empirical analysis of price returns is an essential component in the valuation methods in any asset class. Energy commodities present unique challenges: seasonality and inventory are crucial for covariance structure; forward price volatility increases dramatically while approaching contract’s expiration: the famous Samuelson effect; liquidity in commodity futures and options liquidity is concentrated at short tenors.

This fact makes the term structure of volatility and correlations very important in pricing and hedging decisions. In this presentation, I give the results of my work with NYU students devoted to this subject. We will follow three goals:

  • Parameterization of Samuelson effect and the calibration procedures for commodities futures, including seasonal commodities as gas and power.
  • Samuelson effect for commodity correlations, parameterizing calendar correlations.
  • Contrast between the traditional Black and its long-ago predecessor Bachelier model in view of recent dramatic events in oil markets in Spring 2020.

Bio

Roza Galeeva has extensive experience of over 18 years with commodity derivatives – modelling, pricing, and risk management. She has been employed at senior levels as a quant at Williams Energy, Northeast Utilities, and most recently, for 13 years, 2005-2018 at Morgan Stanley. She worked at MS in different roles and departments, including the Valuation Group, and later the MS strats and modelling group. Prior to the industry, Roza was teaching courses in mathematics in different countries. She has a PhD from Moscow State University in Mathematical Physics. She published papers in geometry, PDE, dynamical systems, and financial engineering. She made her come back to academia in 2017 at NYU with teaching courses in financial engineering and working with NYU students on research projects on Commodity Derivatives.

Seminar

Cornell – Citi Financial Data Science Webinars

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

Featuring Machine Learning experts from Cornell, Citi, and more…

You and your colleagues are invited to attend the Cornell – Citi Financial Data Science Webinars. Through the online talks in Spring 2021, 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 EST.

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 (the confirmation email will come from
no-reply@zoom.us)

Date: Tuesday, March 9th, 2021
Time: 5:00 pm – 6:00 pm EST
Speaker: Bruno Dupire | Bloomberg L.P.
Title: “Some Applications of Machine Learning in Finance”

Abstract

Finance has always tried to make use of all available information to optimize investment decisions. The advent of efficient Machine Learning algorithms, alternative data, and computational powers has deeply impacted many fields in finance. To mention a few, rotation of factors according to market regimes in factor investing, option pricing and hedging, anomaly detection, covariance matrix cleaning, transaction cost analysis. Alternative data include texts from news and tweets, supply chain data, satellite images, vessel routes, weather data, credit card transactions, geolocation data.

This overflow of information opens the door to endless number crunching and apophenia. The desperate search for a signal leads to overfitting and unstable relationships, so beware. As I like to say, the market is a machine made to destroy the signal!

Program Agenda:

  1. Bruno Dupire’s Presentation
  2. Q&A
  3. “Lightning Talk” – Yumeng Ding
  4. Discussion

Speaker Bio

Bruno Dupire is head of Quantitative Research at Bloomberg L.P., which he joined in 2004. Prior to this assignment in New York, he has headed the Derivatives Research teams at Société Générale, Paribas Capital Markets, and Nikko Financial Products where he was a Managing Director. He is best known for having pioneered the widely used Local Volatility model (simplest extension of the Black-Scholes-Merton model to fit all option prices) in 1993 and the Functional Itô Calculus (framework for path dependency) in 2009. He is a Fellow and Adjunct Professor at NYU and he is in the Risk Magazine “Hall of Fame”. He is the recipient of the 2006 “Cutting Edge Research” award of Wilmott Magazine and of the Risk Magazine “Lifetime Achievement” award for 2008.

After a Master’s degree in Artificial Intelligence in 1982 and a Ph.D. in Numerical Analysis in 1985, he has conducted in 1987-88 a study to apply Neural Nets to time series forecasting for Caisse des Dépôts et Consignations. He has been applying Machine Learning to a variety of problems in Finance and has given many lectures on the topic in the Americas, Europe, and Asia over the past few years.

“Lightning Talk” Info:

CFEM alumna Yumeng Ding will discuss her team capstone project, which was titled, “Interpreting Machine Learning Models.” By utilizing Machine Learning interpretability models, the Cornell CFEM team, sponsored by Alliance Bernstein, explored how black-box models can be explained and evaluated in finance. The team analyzed S&P 500 constituents and explored the interpretability of some widely-used ML modes.

Yumeng Ding (MFE Cornell ’20, BA Finance Fudan University‘15) is a soon-to-be analyst in Strategic and Analytics at Deutsche Bank.

We hope to see you online!

The Cornell-Citi Team

**Please excuse any duplication of this announcement

If you are interested in our past seminars, you are welcome to subscribe to our YouTube Channel and watch our videos!

Past CFEM Events

February 16th, 2021
Speaker: Charles-Albert Lehalle (Capital Fund Management)
Title of Presentation: “An Attempt to Understand Natural Language Processing and Illustration on a Financial Dataset”

Upcoming CFEM Events

April 13th, 2021
Speaker: Peter Carr (NYU)
Title of Presentation: Adding Optionality

May 11th, 2021
Speaker: Raja Velu (Syracuse University)
Title of Presentation: TBD

Seminar

Brooklyn Quant Experience Lecture Series: Laura Ballotta

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Brooklyn Quant Experience Lecture Series, NYU Tandon

 Laura Ballotta, Reader in Financial Mathematics at Cass Business School, will give the following talk on Thursday, March 4th at 9:30 AM EST. 
*Kindly note that we have changed the time to 9:30 AM on Thursdays. The new time change allows our invited international guests to join these important virtual talks.

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Meeting ID: 999 3949 8124
Password: FREBQELB

Title

Fourier-based methods for the management of complex insurance products

Abstract

This paper proposes a framework for the valuation and the management of complex life insurance contracts, whose design can be described by a portfolio of embedded options, which are activated according to one or more triggering events. These events are in general monitored discretely over the life of the policy, due to the contract terms. Similar designs can also be found in other contexts, such as counterparty credit risk for example.

The framework is based on Fourier transform methods as they allow to derive convenient closed analytical formulas for a broad spectrum of underlying dynamics. Multidimensionality issues generated by the discrete monitoring of the triggering events are dealt with efficiently designed Monte Carlo integration strategies. We illustrate the tractability of the proposed approach by means of a detailed study of ratchet variable annuities, which can be considered a prototypical example of these complex structured products.

This is joint work with Ernst Eberlein, Thorsten Schmidt and Raghid Zeineddine.

Bio

Laura Ballotta is a reader in Financial Mathematics at Cass Business School, London. She works in the areas of quantitative finance and risk management and has written on topics including stochastic modelling for financial valuation and risk management, numerical methods aimed at supporting financial applications, and the interplay between finance and insurance. She holds a Ph.D. in Mathematical and Computational Methods for Economics and Finance from the Università degli Studi di Bergamo (Italy).