# Current events

(Difference between revisions)
 Revision as of 08:46, 22 November 2020 (edit)Cialenco (Talk | contribs) (→Forthcoming Talks)← Previous diff Revision as of 08:57, 22 November 2020 (edit) (undo)Cialenco (Talk | contribs) (→Forthcoming Talks)Next diff → Line 60: Line 60: ''Speaker:'' '''[https://www.fm.mathematik.uni-muenchen.de/personen/professors/francesca_biagini/index.html Francesca Biagini]''', University of Munich ''Speaker:'' '''[https://www.fm.mathematik.uni-muenchen.de/personen/professors/francesca_biagini/index.html Francesca Biagini]''', University of Munich - ''Title:'' TBA + [[Image:Francesca1.jpg]] + + ''Title:'' Reduced-form setting under model uncertainty with non-linear affine intensities + + ''Abstract:'' In this talk we present a market model including financial assets and life insurance liabilities within a reduced-form framework under model uncertainty by following [1]. In particular we extend this framework to include mortality intensities following an affine process under parameter uncertainty, as defined in [2]. + This allows both to introduce the definition of a longevity bond under model uncertainty in a consistent way with the classical case under one prior, as well as to compute it by explicit formulas or by numerical methods. We also study conditions to guarantee the existence of a càdlàg modification for the longevity bond’s value process. Furthermore, we show how the resulting market model extended with the longevity bond is arbitrage-free and study arbitrage-free pricing of contingent claims or life insurance liabilities in this setting. + This talk is based on: + [1] Francesca Biagini and Yinglin Zhang. Reduced-form framework under model uncertainty. The Annals of Applied Probability, 29(4):2481–2522, 2019. + [2] Francesca Biagini and Katharina Oberpriller. Reduced-form setting under model uncertainty with non-linear affine intensities. Preprint University of Munich and Gran Sasso Science Institute, 2020. + [3] Tolulope Fadina, Ariel Neufeld, and Thorsten Schmidt. Affine processes under parameter uncertainty. Probability, Uncertainty and Quantitative Risk volume 4 (5), 2019. - ''Abstract:'' TBA ''Moderator:'' [http://fouque.faculty.pstat.ucsb.edu/ Jean-Pierre Fouque], Department of Statistics and Applied Probability, UC Santa Barbara ''Moderator:'' [http://fouque.faculty.pstat.ucsb.edu/ Jean-Pierre Fouque], Department of Statistics and Applied Probability, UC Santa Barbara

## SIAG/FME virtual seminars series

The series of virtual talks, started by the SIAM Activity Group on Financial Mathematics and Engineering (SIAG/FME), aims at keeping the mathematical finance community connected worldwide beyond traditional formats. The goal is to host a diverse, across all dimensions, lineup of prominent speakers that will present the latest developments in the area of financial mathematics and engineering.

\diamond The talks will run every other week, and at least until the next SIAG/FME Biennial Meeting in June 2021

\diamond The talks will alternate with those set up by the Bachelier Finance Society

\diamond All talks will be delivered remotely using Zoom.

\diamond The talks are open to the public. Due to security reasons, all attendees have to register.

\diamond The registration link will be posted on this web-site, next to the each seminar date below. The detailed information about each talk, and the registration link will be also distributed via SIAG/FME Mailing List.

\diamond The registration is quick (asks only for your name and email), and once registered, you will receive an email with the link to the meeting(s), which is unique to you, so please do not share that email. The registration is usually valid for multiple future talks.

SIAG/FME Seminar Series Committee:

\quad Agostino Capponi (SIAG/FME Chair, Columbia University)

\quad Igor Cialenco (SIAG/FME Program Director, Illinois Institute of Technology)

\quad Sebastian Jaimungal (University of Toronto)

### Forthcoming Talks

Thursday, October 15, 2020, 1PM-2PM (Eastern US; GMT-4);

Panel Discussion: Implications of COVID-19 on financial markets Registration Link

Panelists:

Michael J. Fleming, Vice President and Financial Economist, Federal Reserve Bank of New York, New York, US

Wenqian Huang, Economist, Bank for International Settlements (BIS), Basel, Switzerland

David Rios, Lecturer at Columbia University and NYU Tandon

Abstract: This panel will discuss the implications of COVID 19 on financial markets. Dr. Fleming will discuss the pandemic's effect on the Treasury market and how – and why – the Fed took unprecedented steps to address the market disruptions. Treasury market volatility and illiquidity jumped to unusually high levels in March 2020 due to unwinding of relative value trades, selling by foreign investors, limited dealer intermediation capacity, and the withdrawal of some market participants. Dr. Huang will discuss the resilience of central counterparties (CCPs) during this period of turbulence. CCPs issued large margin calls, but the extent of the procyclicality of margining is the consequence of various design choices. Dr, Huang will highlight systemic considerations related to the nexus between banks and CCPs, and why central banks need to assess banks and CCPs jointly rather than in isolation in regards to margins. Dr. Rios will discuss the massive and quick reaction to COVID by the US Government. With respect to the mortgage market there has been much success in averting a 2008 type drop in home prices despite record high unemployment. He will argue why policies to provide liquidity to the American homeowner through refinancing seem to have improved since 2008, but are still less effective than 2003.

Moderator: Agostino Capponi, Department of Industrial Engineering and Operations Research, Columbia University

Thursday, October 29, 2020, 1PM-2PM (Eastern US; GMT-4);

Speaker: Francesca Biagini, University of Munich

Title: Reduced-form setting under model uncertainty with non-linear affine intensities

Abstract: In this talk we present a market model including financial assets and life insurance liabilities within a reduced-form framework under model uncertainty by following [1]. In particular we extend this framework to include mortality intensities following an affine process under parameter uncertainty, as defined in [2].

This allows both to introduce the definition of a longevity bond under model uncertainty in a consistent way with the classical case under one prior, as well as to compute it by explicit formulas or by numerical methods. We also study conditions to guarantee the existence of a càdlàg modification for the longevity bond’s value process.  Furthermore, we show how the resulting market model extended with the longevity bond is arbitrage-free and study arbitrage-free pricing of contingent claims or life insurance liabilities in this setting.


This talk is based on:

[1] Francesca Biagini and Yinglin Zhang. Reduced-form framework under model uncertainty. The Annals of Applied Probability, 29(4):2481–2522, 2019.


[2] Francesca Biagini and Katharina Oberpriller. Reduced-form setting under model uncertainty with non-linear affine intensities. Preprint University of Munich and Gran Sasso Science Institute, 2020. [3] Tolulope Fadina, Ariel Neufeld, and Thorsten Schmidt. Affine processes under parameter uncertainty. Probability, Uncertainty and Quantitative Risk volume 4 (5), 2019.

Moderator: Jean-Pierre Fouque, Department of Statistics and Applied Probability, UC Santa Barbara

Thursday, November 12, 2020, 1PM-2PM (Eastern US; GMT-4);

Speaker: Damir Filipovic, EPFL and Swiss Finance Institute

Title: A Machine Learning Approach to Portfolio Pricing and Risk Management for High-Dimensional Problems

Abstract: We present a general framework for portfolio risk management in discrete time, based on a replicating martingale. This martingale is learned from a finite sample in a supervised setting. The model learns the features necessary for an effective low-dimensional representation, overcoming the curse of dimensionality common to function approximation in high-dimensional spaces. We show results based on polynomial and neural network bases. Both offer superior results to naive Monte Carlo methods and other existing methods like least-squares Monte Carlo and replicating portfolios.

Moderator: Rene Carmona, Princeton University

Thursday, November 26, 2020

No Seminar due to Thanksgiving Day

Thursday, December 10, 2020, 1PM-2PM (Eastern US; GMT-4);

Early Career Talks

Speaker 1: Dena Firoozi, Department of Decision Sciences, University of Montreal

Title: TBA

Abstract: TBA

Speaker 2: Sveinn Olafsson, Industrial Engineering and Operations Research, Columbia University

Title: TBA

Abstract: TBA

Moderator: Stephan Sturm, Department of Mathematical Sciences, Worcester Polytechnic Institute

.

### Past Talks

Thursday, October 1, 2020, 1PM-2PM (Eastern US; GMT-4);

Speaker: Samuel Drapeau, Shanghai Jiao Tong University

Title: On Detecting Spoofing Strategies in High Frequency Trading Recorded video

Abstract: The development of high frequency and algorithmic trading allowed to considerably reduce the bid ask spread by increasing liquidity in limit order books. Beyond the problem of optimal placement of market and limit orders, the possibility to cancel orders for free leaves room for price manipulations, one of such being spoofing. Detecting spoofing from a regulatory viewpoint is challenging due to the sheer amount of orders and difficulty to discriminate between legitimate and manipulative flows of orders. However, it is empirical evidence that volume imbalance reflecting offer and demand on both sides of the limit order book has an impact on subsequent price movements. Spoofers use this effect to artificially modify the imbalance by posting limit orders and then execute market orders at subsequent better prices while canceling at a high speed their previous limit orders. In this work we set up a model to determine where a spoofer would place its limit orders to maximize its gains as a function of the imbalance impact on the price movement. We study the solution of this non local optimization problem as a function of the imbalance. With this at hand, we calibrate on real data from TMX the imbalance impact (as a function of its depth) on the resulting price movement. Based on this calibration and theoretical results, we then provide some methods and numerical results as how to detect in real time some eventual spoofing behavior based on Wasserstein distances. Joint work with Tao Xuan (SJTU), Ling Lan (SJTU) and Andrew Day (Western University)

Moderator: Mike Ludkovski, Department of Statistics and Applied Probability, UC Santa Barbara

Thursday, September 17, 2020, 1PM-2PM (Eastern US; GMT-4);

Speaker: Rene Carmona, Princeton University

Title: Contract theory and mean field games to inform epidemic models Recorded video

Abstract: After a short introduction to contract theory, we review recent results on models involving one principal and a field of agents, both for continuous and discrete state spaces. We conclude with the discussion of an application to the control of the spread of an epidemic to illustrate the potential to inform regulatory decisions.

Moderator: Sebastian Jaimungal, University of Toronto

Thursday, September 3, 2020, 1PM-2PM (Eastern US; GMT-4);

Speaker: Sergey Nadtochiy, Illinois Institute of Technology

Title: A simple microstructural explanation of the concavity of price impact Recorded video

Abstract: I will present a simple model of market microstructure which explains the concavity of price impact. In the proposed model, the local relationship between the order flow and the fundamental price (i.e. the local price impact) is linear, with a constant slope, which makes the model dynamically consistent. Nevertheless, the expected impact on midprice from a large sequence of co-directional trades is nonlinear and asymptotically concave. The main practical conclusion of the model is that, throughout a meta-order, the volumes at the best bid and ask prices change (on average) in favor of the executor. This conclusion, in turn, relies on two more concrete predictions of the model, one of which can be tested using publicly available market data and does not require the (difficult to obtain) information about meta-orders. I will present the theoretical results and will support them with the empirical analysis.

Moderator: Ronnie Sircar, Princeton University

Thursday, August 20, 2020, 1PM-2PM (Eastern US; GMT-4);

Speaker: Paolo Guasoni, Dublin City University

Title: The cost of Lightning Network channels and its implications for the network's structure Recorded video

Abstract: A channel in the Lightning Network is a protocol to secure bitcoin payments and escrow holdings between two parties, designed to increase transaction immediacy and reduce blockchain congestion. In a lightning channel, each party commits collateral towards future payments to the counterparty. Payments are cryptographically secured updates of the collaterals. This paper obtains conditions under which two parties optimally establish a channel, finds explicit formulas for channels’ costs, and derives implications for the network’s structure under cooperation assumptions among small sets of users. As optimal network structures eschew redundant channels, they typically exhibit low degree. If agents’ payment rates are sufficiently homogeneous, centralization through a common intermediary may become optimal.

Moderator: Agostino Capponi, Columbia University

Thursday, July 23, 2020, 1PM-2PM (Eastern US; GMT-4);

Early Career Talks

Ruimeng Hu, University of California Santa Barbara

Title: Deep fictitious play for stochastic differential games Recorded Video

Abstract: Differential games, as an offspring of game theory and optimal control, provide the modeling and analysis of conflict in the context of a dynamic system. Computing Nash equilibria is one of the core objectives in differential games, with a major bottleneck coming from the notorious intractability of N-player games. This leads to the difficulty of the curse of dimensionality, which will be overcome by the algorithms of deep fictitious play using machine learning tools. We discuss the approaches to solve open-loop and Markovian Nash equilibria with convergence analysis.

A. Max Reppen, Boston University

Title: Discrete dividend payments in continuous time Recorded Video

Abstract: We propose a model in which dividend payments occur at regular, deterministic intervals in an otherwise continuous model. This contrasts traditional models where either the payment of continuous dividends is controlled or the dynamics are given by discrete time processes. Moreover, between two dividend payments, the structure allows for other types of control; we consider the possibility of equity issuance at any point in time. The value is characterized as the fixed point of an optimal control problem with periodic initial and terminal conditions. We prove the regularity and uniqueness of the corresponding dynamic programming equation, and the convergence of an efficient numerical algorithm that we use to study the problem. The model enables us to find the loss caused by infrequent dividend payments. We show that under realistic parameter values this loss varies from around 1% to 24% depending on the state of the system, and that using the optimal policy from the continuous problem further increases the loss.

Moderator: Igor Cialenco, Illinois Institute of Technology

Thursday, June 25, 2020, 1PM-2PM (Eastern US; GMT-4);

Speaker: Jean-Pierre Fouque, University of California Santa Barbara

Title: Accuracy of Approximation for Portfolio Optimization under Multiscale Stochastic Environment Recorded Video

Abstract: For the problem of portfolio optimization when returns and volatilities are driven by stochastic factors, approximations for value functions and optimal strategies have been proposed in the regime where these factors are running on slow and fast timescales. But, until now, rigorous results of accuracy of these approximations have only been obtained for cases that can be linearized, typically limited to power utilities and a single factor driving the environment. This talk is about treating cases with general utility functions and multi factors. Our approach is to construct sub- and super- solutions to the fully nonlinear problem such that their difference is at the desired level of accuracy. We first present a regular perturbation case with a power utility and two factors nearly fully correlated. Then, we show how to deal with a singular perturbation in the case of a general utility function with a fast varying factor. Joint work with Maxim Bichuch, Ruimeng Hu, and Ronnie Sircar.

Moderator: Agostino Capponi, Department of Industrial Engineering and Operations Research, Columbia University

Thursday, June 11, 2020, 1PM-2PM (Eastern US; GMT-4);

Speaker: Patrick Cheridito, ETH Zurich

Title: Deep optimal stopping Recorded Video

Abstract: I present a deep learning method for optimal stopping problems which directly learns the optimal stopping rule from Monte Carlo samples. As such, it is broadly applicable in situations where the underlying randomness can efficiently be simulated. The approach is tested on three problems: the pricing of a Bermudan max-call option, the pricing of a callable multi barrier reverse convertible and the problem of optimally stopping a fractional Brownian motion. In all three cases it produces very accurate results in high-dimensional situations with short computing times. Joint work with Sebastian Becker and Arnulf Jentzen.

Moderator: Sebastian Jaimungal, Department of Statistical Sciences, University of Toronto

Thursday, May 28, 2020, 1PM-2PM (Eastern US; GMT-4);

Panel Discussion: Energy Markets

Abstract: The aim is to discuss recent events in energy/electricity/commodity markets, such as negative prices, as well as related mathematical modeling challenges.

Panelists:

\qquad Rene Aid, Université Paris-Dauphine, France

\qquad Glen Swindle, Scoville Risk Partners, USA

\qquad Zef Lokhandwalla, Bloomberg LP, USA

\qquad Mike Ludkovski, University of California Santa Barbara, USA

Moderator: Ronnie Sircar, ORFE, Princeton University

Thursday, May 14, 2020, 1PM-2:30PM (Eastern US; GMT-4);

Speaker: Bruno Dupire, Head of Quantitative Research, Bloomberg LP

Title: The Geometry of Money and the Perils of Parameterization

Abstract: Market participants use parametric forms to make sure prices are orderly aligned. It may prevent static arbitrages but could it lead to dynamic arbitrages? Markets trade thousands of underlying, each one with tens or even hundreds of options, quoted throughout the day. Needless to say, the quotes are not generated manually. They are automated and derived from a functional form with a few parameters. If we know this parameterization, we know in advance how the prices tomorrow of many traded securities will belong to a low dimensional (number of parameters) manifold in a high dimensional (number of securities). If the vector of today prices does not belong to the convex hull of the manifold it creates arbitrage. We examine market practice (Black-Scholes, stochastic volatility models, interest rate interpolation by piecewise constant instantaneous forward rates, converging implied volatilities for extreme strikes in FX...) and show that many violate the no arbitrage condition.

Moderator: Igor Cialenco, Illinois Institute of Technology

Thursday, April 30, 2020, 1PM-2PM (Eastern US; GMT-4);

Speaker: Blanka Horvath, Department of Mathematics, King's College London, UK

Title: A Data-driven Market Simulator for Small Data Environments

Abstract: In this talk we investigate how Deep Hedging brings a new impetus into the modelling of financial markets. While a DNN-based data-driven market generation unveils a new and highly flexible way of modelling financial time series, it is by no means "model-free". In fact, the concrete modelling choice is decisive for the features of the resulting generative model. After a very short walk through historical market models we proceed to neural network based generative modelling approaches for financial time series. We then investigate some of the challenges to achieve good results in the latter, and highlight some applications and pitfalls. While most generative models tend to rely on large amounts of training data, we present here a parsimonious generative model that works reliably even in environments where the amount of available training data is notoriously small. Furthermore, we discuss how a rough paths perspective combined with a parsimonious Variational Autoencoder framework provides a powerful way for encoding and evaluating financial time series data in such environments. Lastly, we also discuss some pricing and hedging considerations in a DNN framework and their connection to Market Generation. The talk is based on joint work with H. Buehler, I. Perez Arribaz, T. Lyons and B. Wood.

Moderator: Agostino Capponi, Department of Industrial Engineering and Operations Research, Columbia University

Thursday, April 16, 2020, 1PM-2PM (Eastern US; GMT-4)

Speaker: Mete Soner, Department of Operations Research and Financial Engineering, Princeton University