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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:20240419T120000
DTEND;TZID=America/Chicago:20240419T130000
DTSTAMP:20260805T082949
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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DTSTART;TZID=America/Chicago:20240419T140000
DTEND;TZID=America/Chicago:20240419T140000
DTSTAMP:20260805T082949
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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