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X-WR-CALNAME:Mathematical Finance
X-ORIGINAL-URL:https://www.math.ttu.edu/mathematicalfinance
X-WR-CALDESC:Events for Mathematical Finance
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DTSTART:20240310T080000
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DTSTART:20241103T070000
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DTSTART;TZID=America/Chicago:20240412T140000
DTEND;TZID=America/Chicago:20240412T150000
DTSTAMP:20260410T113059
CREATED:20231114T173107Z
LAST-MODIFIED:20240305T220124Z
UID:1209-1712930400-1712934000@www.math.ttu.edu
SUMMARY:Supermartingale Brenier's Theorem with full-marginals constraint
DESCRIPTION:Speaker: Prof. Dominykas Norgilas\, Department of Mathematics\, North Carolina State University \nAbstract: We explicitly construct the supermartingale version of the Fréchet-Hoeffding coupling in the setting with infinitely many marginal constraints. This extends the results of Henry-Labordere et al. obtained in the martingale setting. Our construction is based on the Markovian iteration of one-period optimal supermartingale couplings. In the limit\, as the number of iterations goes to infinity\, we obtain a pure jump process that belongs to a family of local Lévy models introduced by Carr et al. We show that the constructed processes solve the continuous-time supermartingale optimal transport problem for a particular family of path-dependent cost functions. The explicit computations are provided in the following three cases: the uniform case\, the Bachelier model and the Geometric Brownian Motion case.
URL:https://www.math.ttu.edu/mathematicalfinance/event/supermartingale-breniers-theorem-with-full-marginals-constraint/
LOCATION:via Zoom
CATEGORIES:Seminars,Spring 2024
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2023/11/norgilas-1.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20240419T120000
DTEND;TZID=America/Chicago:20240419T130000
DTSTAMP:20260410T113059
CREATED:20231129T190232Z
LAST-MODIFIED:20231130T170131Z
UID:1263-1713528000-1713531600@www.math.ttu.edu
SUMMARY:Semi-analytic pricing of American options in some time-dependent jump-diffusion models
DESCRIPTION:Speaker: Prof. Andrey Itkin\, Department of Risk and Financial Engineering\, Tandon School of Engineering\, NYU \nAbstract: In this paper we propose a semi-analytic approach to pricing American options for some time-dependent jump-diffusions models. The idea of the method is to further generalize our approach developed for pricing barrier\, [Itkin et al.\, 2021]\, and American\, [Carr and Itkin\, 2021; Itkin and Muravey\, 2023]\, options in various time-dependent one factor and even stochastic volatility models. Our approach i) allows arbitrary dependencies of the model parameters on time; ii) reduces solution of the pricing problem for American options to a simpler problem of solving an algebraic nonlinear equation for the exercise boundary and a linear Fredholm-Volterra equation for the the option price; iii) the options Greeks solve a similar Fredholm-Volterra linear equation obtained by just differentiating Eq. (25) by the required parameter.
URL:https://www.math.ttu.edu/mathematicalfinance/event/semi-analytic-pricing-of-american-options-in-some-time-dependent-jump-diffusion-models/
LOCATION:via Zoom
CATEGORIES:Seminars,Spring 2024
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2023/11/itkin.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20240419T140000
DTEND;TZID=America/Chicago:20240419T140000
DTSTAMP:20260410T113059
CREATED:20231115T154042Z
LAST-MODIFIED:20240408T172908Z
UID:1241-1713535200-1713535200@www.math.ttu.edu
SUMMARY:Portfolio selection under non-gaussianity and systemic risk: A machine learning based forecasting approach
DESCRIPTION:Speaker: Prof. Abderrahim Taamouti\, Management School\, University of Liverpool \nAbstract: The Sharpe-ratio-maximizing portfolio becomes questionable under non-Gaussian returns\, and it rules out\, by construction\, systemic risk\, which can negatively affect its out-of-sample performance. In the present work\, we develop a new performance ratio that simultaneously addresses these two problems when building optimal portfolios. To robustify the portfolio optimization and better represent extreme market scenarios\, we simulate a large number of returns via a Monte Carlo method. This is done by obtaining probabilistic return forecasts through a distributional machine learning approach in a big data setting and then combining them with a fitted copula to generate return scenarios. Based on a large-scale comparative analysis conducted on the US market\, the backtesting results demonstrate the superiority of our proposed portfolio selection approach against several popular benchmark strategies in terms of both profitability and minimizing systemic risk. This outperformance is robust to the inclusion of transaction costs.
URL:https://www.math.ttu.edu/mathematicalfinance/event/portfolio-selection-under-non-gaussianity-and-systemic-risk-a-machine-learning-based-forecasting-approach/
LOCATION:via Zoom
CATEGORIES:Seminars,Spring 2024
ATTACH;FMTTYPE=image/png:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2023/11/taamouti.png
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20240426T120000
DTEND;TZID=America/Chicago:20240426T130000
DTSTAMP:20260410T113059
CREATED:20231212T161814Z
LAST-MODIFIED:20231212T161836Z
UID:1278-1714132800-1714136400@www.math.ttu.edu
SUMMARY:Hedging with temporary price impact
DESCRIPTION:Speaker: Prof. Peter Bank\, Department of Mathematics\, Technical University of Berlin \nAbstract: We consider the problem of hedging a European contingent claim in a Bachelier model with temporary price impact as proposed by Almgren and Chriss (J Risk 3:5–39\, 2001). Following the approach of Rogers and Singh (Math Financ 20:597–615\, 2010) and Naujokat and Westray (Math Financ Econ 4(4):299–335\, 2011)\, the hedging problem can be regarded as a cost optimal tracking problem of the frictionless hedging strategy. We solve this problem explicitly for general predictable target hedging strategies. It turns out that\, rather than towards the current target position\, the optimal policy trades towards a weighted average of expected future target positions. This generalizes an observation of Gârleanu and Pedersen (Dynamic portfolio choice with frictions. Preprint\, 2013b) from their homogenous Markovian optimal investment problem to a general hedging problem. Our findings complement a number of previous studies in the literature on optimal strategies in illiquid markets as\, e.g.\, Gârleanu and Pedersen (Dynamic portfolio choice with frictions. Preprint\, 2013b)\, Naujokat and Westray (Math Financ Econ 4(4):299–335\, 2011)\, Rogers and Singh (Math Financ 20:597–615\, 2010)\, Almgren and Li (Option hedging with smooth market impact. Preprint\, 2015)\, Moreau et al. (Math Financ. doi:10.1111/mafi.12098\, 2015)\, Kallsen and Muhle-Karbe (High-resilience limits of block-shaped order books. Preprint\, 2014)\, Guasoni and Weber (Mathematical Financ. doi:10.1111/mafi.12099\, 2015a; Nonlinear price impact and portfolio choice. Preprint\, 2015b)\, where the frictionless hedging strategy is confined to diffusions. The consideration of general predictable reference strategies is made possible by the use of a convex analysis approach instead of the more common dynamic programming methods.
URL:https://www.math.ttu.edu/mathematicalfinance/event/hedging-with-temporary-price-impact/
LOCATION:via Zoom
CATEGORIES:Seminars,Spring 2024
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2023/12/bank.jpg
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