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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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TZID:America/Chicago
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TZOFFSETFROM:-0600
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TZNAME:CDT
DTSTART:20260308T080000
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DTSTART:20261101T070000
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DTSTART;TZID=America/Chicago:20260206T140000
DTEND;TZID=America/Chicago:20260206T150000
DTSTAMP:20260414T143355
CREATED:20251203T183845Z
LAST-MODIFIED:20251203T183845Z
UID:2502-1770386400-1770390000@www.math.ttu.edu
SUMMARY:Marketron Through the Looking Glass: From Equity Dynamics to Option Pricing in Incomplete Markets
DESCRIPTION:Speaker: Prof. Andrey Itkin\, Department of Finance and Risk Engineering\, Tandon School of Engineering\, NYU \nAbstract: The Marketron model\, introduced by [Halperin\, Itkin\, 2025]\, describes price formation in inelastic markets as the nonlinear diffusion of a quasiparticle (the marketron) in a multidimensional space comprising the log-price x\, a memory variable y encoding past money flows\, and unobservable return predictors z. While the original work calibrated the model to S&P 500 time series data\, this paper extends the framework to option markets – a fundamentally distinct challenge due to market incompleteness stemming from non-tradable state variables. We develop a utility-based pricing approach that constructs a risk-adjusted measure via the dual solution of an optimal investment problem. The resulting Hamilton-Jacobi-Bellman (HJB) equation\, though computationally formidable\, is solved using a novel methodology enabling efficient calibration even on standard laptop hardware. Having done that\, we look at the additional question to answer: whether the Marketron model\, calibrated to market option prices\, can simultaneously reproduce the statistical properties of the underlying asset’s log-returns. We discuss our results in view of the long-standing challenge in quantitative finance of developing an unified framework capable of jointly capturing equity returns\, option smile dynamics\, and potentially volatility index behavior.
URL:https://www.math.ttu.edu/mathematicalfinance/event/marketron-through-the-looking-glass-from-equity-dynamics-to-option-pricing-in-incomplete-markets/
LOCATION:via Zoom
CATEGORIES:Spring 2026
ATTACH;FMTTYPE=image/png:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2025/12/Itkin-e1764786999404.png
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DTSTART;TZID=America/Chicago:20260220T120000
DTEND;TZID=America/Chicago:20260220T140000
DTSTAMP:20260414T143355
CREATED:20251230T163452Z
LAST-MODIFIED:20251230T163452Z
UID:2604-1771588800-1771596000@www.math.ttu.edu
SUMMARY:Some general results on risk budgeting portfolios
DESCRIPTION:Speaker: Prof. Pierpaolo Uberti\, Department of Statistics and Quantitative Methods\, University of Milano-Bicocca \nAbstract:>  Given a reference risk measure\, risk budgeting defines a portfolio in which each asset contributes a predetermined amount to the total risk. We propose a novel approach—alternative to those proposed in the literature—for the computation of the risk budgeting portfolio. We define a Cauchy sequence within the simplex of R^n\, whose limit corresponds to the risk budgeting portfolio. This construction allows for the straightforward implementation of an efficient algorithm\, avoiding the need to solve auxiliary\, equivalent optimization problems\, which may be computationally challenging and difficult to interpret in a decision-theoretic context. From a theoretical point of view\, starting from the Cauchy sequence\, we define a function for which the risk budgeting portfolio is a fixed point. Therefore\, sufficient conditions for the existence and uniqueness of the fixed point can be applied. Our methodology is developed for a general risk measure. The implementation is presented in detail for the standard deviation. We compare our algorithm with the standard optimization-based methods proposed in the literature. The computational efficiency of the proposed algorithm is also compared with standard approaches for different risk measures (standard deviation\, value at risk\, and expected shortfall).
URL:https://www.math.ttu.edu/mathematicalfinance/event/some-general-results-on-risk-budgeting-portfolios/
LOCATION:via Zoom
CATEGORIES:Spring 2026
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2025/12/Uberti.jpg
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DTSTART;TZID=America/Chicago:20260227T140000
DTEND;TZID=America/Chicago:20260227T150000
DTSTAMP:20260414T143355
CREATED:20251120T201011Z
LAST-MODIFIED:20251203T183943Z
UID:2420-1772200800-1772204400@www.math.ttu.edu
SUMMARY:Coherent estimation of risk measures
DESCRIPTION:Speaker: Prof. Igor Cialenco\, Dept. of Applied Mathematics\, Illinois Institute of Technology \nAbstract: We develop a statistical framework for risk estimation\, inspired by the axiomatic theory of risk measures. Coherent risk estimators—functionals of P&L samples inheriting the economic properties of risk measures—are defined and characterized through robust representations linked to L-estimators. The framework provides a canonical methodology for constructing estimators with sound financial and statistical properties\, unifying risk measure theory\, principles for capital adequacy\, and practical statistical challenges in market risk. A numerical study illustrates the approach\, focusing on expected shortfall estimation under both i.i.d. and overlapping samples relevant for regulatory FRTB model applications.
URL:https://www.math.ttu.edu/mathematicalfinance/event/coherent-estimation-of-risk-measures/
LOCATION:via Zoom
CATEGORIES:Spring 2026
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2025/11/Cialenco.jpg
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