BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Ilaria Peri (University of London) DTSTART:20201028T210000Z DTEND:20201028T220000Z DTSTAMP:20260422T212749Z UID:Quantitative_Finance/1 DESCRIPTION:Title: On the properties of Lambda quantiles and financial applicati ons\nby Ilaria Peri (University of London) as part of Quantitative Fin ance Seminar\n\n\nAbstract\nThe aim of this talk is to provide an overview of the theory on Lambda quantiles and present its versatility via financi al applications. Lambda quantiles are generalised quantiles\, introduced b y Frittelli\, Maggis\, P. (2014) under the name of Lambda Value at Risk. I n particular\, Lambda quantiles differ from usual quantiles in that the co nstant lambda is replaced with a threshold function Lambda allowing for mo re flexibility of the confidence level. We discuss alternative definitions of Lambda quantiles and derive their fundamental properties. We provide a n axiomatic foundation for non-increasing Lambda quantiles based on the we ll-known locality property of quantiles that here we formalize. As origina l statistical application\, we introduce the so-called Lambda quantile reg ression. We present the estimation of Lambda quantiles in a market risk se tting by comparing methods based on classical assumptions on the return di stribution and the Lambda quantile regression. We conclude with a backtest ing exercise and discuss how this backtesting framework can be extended to other risk measures.\n\nIlaria Peri is a lecturer in mathematical finance at the Birkbeck University of London. She earned her doctorate from the U niversity of Milan-Bicocca under the supervision of Marco Frittelli. Prior to joining academics\, she worked as a financial consultant gaining exper ience in risk management and banking operations. Her research focuses on r isk measures' theory and applications. Her major contribution is the intro duction of the generalized quantile called Lambda value at risk on which s he has been conducting theoretical studies and empirical applications. Her research has been published in internationally recognized journals and pr esented at invited seminars in academic and professional contexts\, includ ing regulatory authorities.\n LOCATION:https://researchseminars.org/talk/Quantitative_Finance/1/ END:VEVENT BEGIN:VEVENT SUMMARY:Lukasz Szpruch (University of Edinburgh and Alan Turing Institute) DTSTART:20201125T220000Z DTEND:20201125T230000Z DTSTAMP:20260422T212749Z UID:Quantitative_Finance/2 DESCRIPTION:Title: Gradient Flows\, Stochastic Control and Robust pricing and he dging via neural SDEs\nby Lukasz Szpruch (University of Edinburgh and Alan Turing Institute) as part of Quantitative Finance Seminar\n\n\nAbstra ct\nThere is overwhelming empirical evidence that deep neural networks tra ined with stochastic gradient descent perform (extremely) well in the high dimensional settings. Nonetheless\, a complete mathematical theory that w ould provide theoretical guarantees why and when these methods work so wel l has been elusive. In this talk\, I will demonstrate how one may leverage control theory and the theory of statistical sampling to study the conver gence of stochastic gradient algorithms used in deep learning. Conversely\ , I will show that machine learning perspective leads to new algorithms fo r (stochastic) control problems and offers a fresh perspective on classica l quantitative finance problems. Indeed\, modern data science techniques a re opening the door to more robust and data-driven model selection mechani sms. Indeed\, deep generative modelling is opening the door to more robust and data-driven model selection mechanisms. By combining neural networks with risk models based on classical stochastic differential equations (SDE s)\, we find robust bounds for prices of derivatives and the corresponding hedging strategies while incorporating relevant market data. Neural SDEs allow consistent calibration under both the risk-neutral and the real-worl d measures. Thus the model can be used to simulate market scenarios needed for assessing risk profiles and hedging strategies. We develop and analys e novel algorithms needed for efficient use of neural SDEs. We validate ou r approach with numerical experiments using both local and stochastic vola tility models. We will also show that neural SDEs can be used to calibrate to SPX/VIX options.\n\nBio: Lukasz is a Reader (Associate Professor) at t he School of Mathematics\, University of Edinburgh. He is also the directo r of the Finance and Economics programme at The Alan Turing Institute\, th e UK national institute for data science and AI. Previously Lukasz was a N omura Junior Research Fellow at the Institute of Mathematics\, University of Oxford\, and a member of the Oxford-Man Institute for Quantitative Fina nce. Lukasz has a broad research interest in Machine and Reinforcement Lea rning\, Statistics and Game Theory.\n LOCATION:https://researchseminars.org/talk/Quantitative_Finance/2/ END:VEVENT BEGIN:VEVENT SUMMARY:Mark Reesor (Wilfrid Laurier University) DTSTART:20210127T220000Z DTEND:20210127T230000Z DTSTAMP:20260422T212749Z UID:Quantitative_Finance/3 DESCRIPTION:Title: Know Your Clients' Behaviours: A Cluster Analysis of Financia l Transactions\nby Mark Reesor (Wilfrid Laurier University) as part of Quantitative Finance Seminar\n\n\nAbstract\nn Canada\, financial advisors and dealers are required by provincial securities commissions and self-re gulatory organizations---charged with direct regulation over investment de alers and mutual fund dealers---to respectively collect and maintain know your client (KYC) information\, such as their age or risk tolerance\, for investor accounts. With this information\, investors\, under their advisor 's guidance\, make decisions on their investments that are presumed to be beneficial to their investment goals. Our unique dataset is provided by a financial investment dealer with over 50\,000 accounts for over 23\,000 cl ients covering the period from January 1st to August 12th 2019. We use a m odified behavioral finance recency\, frequency\, monetary model for engine ering features that quantify investor behaviours\, and (unsupervised) mach ine learning clustering algorithms to find groups of investors that behave similarly. We show that the KYC information---such as gender\, residence region\, and marital status---does not explain client behaviours\, whereas eight variables for trade and transaction frequency and volume are most i nformative. Hence\, our results should encourage financial regulators and advisors to use more advanced metrics to better understand and predict inv estor behaviours.\n\nThis is joint work with John Thompson\, Longlong Feng \, and Adam Metzler of Wilfrid Laurier University and Chuck Grace of the R ichard Ivey School of Business\, Western University.\n\nBio: Mark Reesor e arned his Master's and PhD degree in Statistics from the University of Wat erloo. After earning his doctorate Mark worked as an analyst in the Financ ial Markets Department at the Bank of Canada. From 2002 until 2016\, Dr. R eesor was a faculty member at Western University in the Departments of Sta tistics and Actuarial Science and of Applied Mathematics and in the financ e area at the Richard Ivey School of Business. Since 2016\, he has been in the Math Department at Wilfrid Laurier University. Dr. Reesor has a varie d research program including works in personal finance\, corporate finance \, financial stability\, securities class actions\, risk management\, deri vatives\, and Monte Carlo Methods. In addition\, Mark was a founding membe r of the Committee to Establish the National Institute of Finance\, helpin g to create of the U.S. Office of Financial Research.\n LOCATION:https://researchseminars.org/talk/Quantitative_Finance/3/ END:VEVENT BEGIN:VEVENT SUMMARY:Alexey Rubtsov (Ryerson University) DTSTART:20210224T220000Z DTEND:20210224T230000Z DTSTAMP:20260422T212749Z UID:Quantitative_Finance/4 DESCRIPTION:Title: Systemic Risk Driven Portfolio Selection\nby Alexey Rubts ov (Ryerson University) as part of Quantitative Finance Seminar\n\n\nAbstr act\nWe consider an investor whose objective is to trade off tail risk and expected growth of the investment. We measure tail risk through portfolio 's expected losses conditioned on the occurrence of a systemic event: fina ncial market loss being exactly at\, or at least at\, its VaR level and in vestor's portfolio losses being above their CoVaR level. We obtain a close d-form solution to the investment problem\, and decompose it in terms of t he Markowitz mean--variance portfolio and an adjustment for systemic risk. We show that VaR and CoVaR confidence levels control\, respectively\, the relative sensitivity of the investor's objective function to portfolio--m arket correlation and portfolio variance. Our empirical analysis demonstra tes that the investor attains higher risk-adjusted returns\, compared to w ell known benchmark portfolio criteria\, during times of market downturn. Portfolios that perform best in adverse market conditions are less diversi fied and concentrate on few stocks which have low correlation with the mar ket.\n LOCATION:https://researchseminars.org/talk/Quantitative_Finance/4/ END:VEVENT BEGIN:VEVENT SUMMARY:Stan Uryasev (Stony Brook University) DTSTART:20210428T210000Z DTEND:20210428T220000Z DTSTAMP:20260422T212749Z UID:Quantitative_Finance/5 DESCRIPTION:Title: Drawdown Beta and Portfolio Optimization\nby Stan Uryasev (Stony Brook University) as part of Quantitative Finance Seminar\n\n\nAbs tract\nWe introduce a new dynamic portfolio performance risk measure calle d Expected Regret of Drawdown (ERoD) which is an average of drawdowns exce eding a specified thresholde(e.g.\,e=10%). ERoD is similar to Conditional Drawdown-at-Risk (CDaR) which is the average of osmepercentage of largest drawdowns. CDaR and ERoD portfolio portfolio optimization are equivalent a nd results in the same portfolios. Necessary optimally conditions for ERoD portfolio optimization lead to Capital Asset Pricing Model (CAPM) equatio ns. ERoD Beta\, similar to the Standard Beta\,relates expected returns of securities and market. ERoD Beta equals to [average losses of a securityov er times intervals when market is in drawdown exceedinge] divided by [aver age losses of marketin drawdowns exceedinge]. Therefore\, a negative ERoD Beta identifies a security which has positive returns when market is in dr awdown. ERoD Beta accounts for only time intervals when market is indrawdo wn and conceptually differs from Standard Beta which does not distinguish up and downmovements of the market. However\, ERoD Beta also provides quit e different results compared toDownside Beta which is based on Lower Semi- deviation. ERoD Beta is conceptually close to CDaRBeta which is based on a percentage of worst case market drawdowns. We have built a website report ing CDaR and ERoD Betas for stocks and S&P 500 index as an optimal market portfolio. The case study showed that CDaR and ERoD Betas exhibit persiste nce over time intervals and can beused in risk management and portfolio co nstruction. This talk is based on joint work with Rui Ding from Stony Broo k University.\n\nBio: Stan Uryasev received his M.S. in Applied Mathematic s from the Moscow Institute of Physics and Technology (MIPT)\, Russia\, in 1979 and Ph.D. in Applied Mathematics from the Glushkov Institute of Cybe rnetics\, Kiev\, Ukraine in 1983. From 1979 to 1987 he held a research pos ition at the Glushkov Institute. From 1988 to 1992 he was a Research Schol ar at the International Institute for Applied System Analysis\, Luxenburg\ , Austria. From 1992 to 1998 he held the Scientist position at the Risk an d Reliability Group\, Brookhaven National Laboratory\, Upton\, NY. From 19 98 to 2019 he was the George and Rolande Willis Endowed Professor at the U niversity of Florida\, and the director of the Risk Management and Financi al Engineering Lab. \n\nHis research and teaching interests include quanti tative finance\, risk management\, stochastic optimization\, machine learn ing\, and military operations research. See Google Scholar for the list of the most cited publications\, https://scholar.google.com/citations?hl=en& user=Uwg1zpkAAAAJ. Here is the full list of publications. \n\nHis joint pa per with Prof. Rockafellar on Optimization of Conditional Value-At-Risk in The Journal of Risk\, Vol. 2\, No. 3\, 2000 is among the 100 most cited p apers in Finance. Many risk management/optimization packages implemented t he approach suggested in this paper (MATLAB implemented a toolbox). \n\nTh e important theoretical contribution presenting a unified scheme for portf olio optimization\, statistical estimation\, risk management\, and utility theory: Rockafellar R.T. and S. Uryasev. The Fundamental Risk Quadrangle in Risk Management\, Optimization\, and Statistical Estimation. Surveys in Operations Research and Management Science\, 18\, 2013. \n\nCollaborative research with industry has been documented in the library of Case Studies containing Portfolio Safeguard (PSG) codes\, data\, and calculation resul ts in Text\, MATLAB\, and R environments. See the list of Case Studies in Financial Engineering\, Advanced Statistics and other areas.\n LOCATION:https://researchseminars.org/talk/Quantitative_Finance/5/ END:VEVENT BEGIN:VEVENT SUMMARY:Maxime Bergeron (Riskfuel) DTSTART:20210331T210000Z DTEND:20210331T220000Z DTSTAMP:20260422T212749Z UID:Quantitative_Finance/6 DESCRIPTION:Title: Deeply Learning Derivatives: from Hilbert to Riskfuel\nby Maxime Bergeron (Riskfuel) as part of Quantitative Finance Seminar\n\n\nA bstract\nThe motivation behind Hilbert's 13th problem is often overlooked. In his original statement of the problem\, he opens with: "Nomography dea ls with the problem of solving equations by means of drawing families of c urves depending on an arbitrary parameter". The question he posed sought t o identify the family of functions amenable to such graphical solvers that were essential tools of his time. While the question in its original (alg ebraic) form remains open to this day\, in the continuous realm it turns o ut that there is no such thing as a truly multivariate function. In this t alk\, we will explain how these ideas fit into the modern deep learning fr amework and\, ultimately\, allow us to build networks that replicate the s olutions operator of stochastic differential equations governing the valua tion of high dimensional contingent claims.\n LOCATION:https://researchseminars.org/talk/Quantitative_Finance/6/ END:VEVENT BEGIN:VEVENT SUMMARY:Cody Hyndman (Concordia University) DTSTART:20210929T210000Z DTEND:20210929T220000Z DTSTAMP:20260422T212749Z UID:Quantitative_Finance/7 DESCRIPTION:Title: Arbitrage-free yield curve and bond price forecasting by deep neural networks\nby Cody Hyndman (Concordia University) as part of Qu antitative Finance Seminar\n\nAbstract: TBA\n LOCATION:https://researchseminars.org/talk/Quantitative_Finance/7/ END:VEVENT BEGIN:VEVENT SUMMARY:Samuel Cohen (University of Oxford) DTSTART:20211027T210000Z DTEND:20211027T220000Z DTSTAMP:20260422T212749Z UID:Quantitative_Finance/8 DESCRIPTION:Title: Arbitrage-free neural-SDE market models\nby Samuel Cohen (University of Oxford) as part of Quantitative Finance Seminar\n\n\nAbstra ct\nModelling joint dynamics of liquid vanilla options is crucial for arbi trage-free pricing of illiquid derivatives and managing risks of option tr ade books. This paper develops a nonparametric model for the European opti ons book respecting underlying financial constraints and while being pract ically implementable. In this talk\, we will consider a state space for pr ices which are free from static (or model-independent) arbitrage and study the inference problem where a model is learnt from discrete time series d ata of stock and option prices. We use neural networks as function approxi mators for the drift and diffusion of the modelled SDE system\, and impose constraints on the neural nets such that no-arbitrage conditions are pres erved. In particular\, we give methods to calibrate neural SDE models whic h are guaranteed to satisfy a set of linear inequalities. We validate our approach with numerical experiments using data generated from a Heston sto chastic volatility model\, and with observed market data.\n\nBio: Sam Cohe n is a mathematician based at the Mathematical Institute in Oxford\, and a t the Alan Turing Institute in London. He obtained his PhD in 2011 at the University of Adelaide\, under the supervision of Robert Elliott. He is in terested in the interaction between statistical learning\, decision making and modelling\, with particular applications in finance and economics.\n LOCATION:https://researchseminars.org/talk/Quantitative_Finance/8/ END:VEVENT BEGIN:VEVENT SUMMARY:Beatrice Acciaio (ETH Zürich) DTSTART:20211124T220000Z DTEND:20211124T230000Z DTSTAMP:20260422T212749Z UID:Quantitative_Finance/9 DESCRIPTION:Title: A robust framework for pricing and hedging American options\nby Beatrice Acciaio (ETH Zürich) as part of Quantitative Finance Semi nar\n\n\nAbstract\nIn this talk I will introduce a suitable framework for pricing and hedging American options in a model-independent way. This is b ased on the recently developed concept of adapted Wasserstein distances. B eside recovering super-replication duality\, in such a framework we establ ish existence and a geometric characterization of the extremal pricing mod els.\n\nThis is based on a joint work with D. Bartl\, B. Beiglboeck and G. Pammer.\n\nBio: Beatrice Acciaio is Professor of Mathematics at ETH Zuric h since 2020. Before joining ETH\, Beatrice was associate professor at the London School of Economics\, and prior to that she has been part of sever al research groups\, at the Technical University of Vienna\, the Universit y of Perugia\, and the University of Vienna. Beatrice completed her PhD in 2006 under the supervision of Walter Schachermayer.\n\nBeatrice's main ar eas of research are probability\, mathematical finance\, and optimal trans port.\n\nBeatrice is member of the Council of the Bachelier Finance Societ y\, she is Associate Editor for the SIAM Journal on Financial Mathematics\ , for Finance and Stochastics\, for Mathematical Finance\, and for the Boc coni & Springer Series on Mathematics\, Statistics\, Finance and Economics .\n LOCATION:https://researchseminars.org/talk/Quantitative_Finance/9/ END:VEVENT BEGIN:VEVENT SUMMARY:Matthew Lorig (University of Washington) DTSTART:20220126T220000Z DTEND:20220126T230000Z DTSTAMP:20260422T212749Z UID:Quantitative_Finance/10 DESCRIPTION:by Matthew Lorig (University of Washington) as part of Quantit ative Finance Seminar\n\nAbstract: TBA\n LOCATION:https://researchseminars.org/talk/Quantitative_Finance/10/ END:VEVENT BEGIN:VEVENT SUMMARY:Peter Tankov (ENSAE\, Institute Polytechnique de Paris) DTSTART:20220223T220000Z DTEND:20220223T230000Z DTSTAMP:20260422T212749Z UID:Quantitative_Finance/11 DESCRIPTION:by Peter Tankov (ENSAE\, Institute Polytechnique de Paris) as part of Quantitative Finance Seminar\n\nAbstract: TBA\n LOCATION:https://researchseminars.org/talk/Quantitative_Finance/11/ END:VEVENT BEGIN:VEVENT SUMMARY:Lars Stentoft (Western University) DTSTART:20220330T210000Z DTEND:20220330T220000Z DTSTAMP:20260422T212749Z UID:Quantitative_Finance/12 DESCRIPTION:by Lars Stentoft (Western University) as part of Quantitative Finance Seminar\n\nAbstract: TBA\n LOCATION:https://researchseminars.org/talk/Quantitative_Finance/12/ END:VEVENT END:VCALENDAR