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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:20250309T080000
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DTSTART:20251102T070000
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DTSTART:20260308T080000
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DTSTART:20261101T070000
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20250829T140000
DTEND;TZID=America/Chicago:20250829T150000
DTSTAMP:20260426T193913
CREATED:20250701T182304Z
LAST-MODIFIED:20250701T182304Z
UID:2010-1756476000-1756479600@www.math.ttu.edu
SUMMARY:Discrete-time hedging\, basis risk\, and covariance-dependent pricing kernels
DESCRIPTION:Speaker: Prof. Maciej Augustyniak\, Dept. of Mathematics & Statistics\, University of Montreal \nAbstract: Basis risk arises when hedging a financial derivative with an instrument different from its underlying asset. This risk can significantly impair hedging effectiveness and must therefore be properly managed. This article develops a discrete-time hedging framework for European-style derivatives that explicitly accounts for basis risk while incorporating key empirical properties of asset returns\, including time-varying volatilities\, leverage effects\, and a flexible dependence structure between assets. Using a covariance-dependent pricing kernel\, we derive semi-closed-form solutions for the optimal risk-minimizing hedge ratio. Empirical analyses using S&P 500 index data\, its futures contracts\, and the VIX demonstrate that our proposed strategy consistently outperforms conventional benchmarks across various maturities and moneyness levels\, providing an effective approach to managing basis risk in derivative hedging.
URL:https://www.math.ttu.edu/mathematicalfinance/event/discrete-time-hedging-basis-risk-and-covariance-dependent-pricing-kernels/
LOCATION:via Zoom
CATEGORIES:Fall 2025
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2025/07/Augustyniak-e1751394026199.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20250905T120000
DTEND;TZID=America/Chicago:20250905T130000
DTSTAMP:20260426T193913
CREATED:20250702T153825Z
LAST-MODIFIED:20250703T171328Z
UID:2029-1757073600-1757077200@www.math.ttu.edu
SUMMARY:The Bachelier implied volatility: A Malliavin calculus approach.
DESCRIPTION:Speaker: Prof. Elisa Alos\, Dept. of Economics and Business\, University of Pompeu Fabra\, Barcelona \nAbstract: We introduce the main tools of Malliavin calculus and show how to use them to study the short-end behavior of skew and curvature of the implied volatility surface. This methodology allows us to obtain general formulas in terms of Malliavin derivatives that can be applied to a wide class of models including local\, stochastic and rough volatilities. Numerical examples are given.
URL:https://www.math.ttu.edu/mathematicalfinance/event/seminar-date-reserved-2/
LOCATION:via Zoom
CATEGORIES:Fall 2025
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2025/07/Alos.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20250912T140000
DTEND;TZID=America/Chicago:20250912T150000
DTSTAMP:20260426T193913
CREATED:20250704T194330Z
LAST-MODIFIED:20250729T162609Z
UID:2040-1757685600-1757689200@www.math.ttu.edu
SUMMARY:Rough Heston model as the scaling limit of bivariate cumulative heavy-tailed INAR( ∞) processes and applications
DESCRIPTION:Speaker: Prof. Zhenyu Cui\, School of Business\, Stevens Institute of Technology\, Hoboken NJ \nAbstract: We establish a novel link between nearly unstable cumulative heavy-tailed integer-valued autoregressive (INAR(∞)) processes and the rough Heston model via discrete scaling limits. We prove that a sequence of bivariate cumulative INAR(∞) processes converge in law to the rough Heston model under appropriate scaling conditions\, providing a rigorous mathematical foundation for understanding how microstructural order flow drives macroscopic prices following rough volatility dynamics. Our theoretical framework extends the scaling limit techniques from Hawkes processes to the INAR(∞) setting.\n     Hence\, we can carry out efficient Monte Carlo simulation of the rough Heston model through simulating the corresponding approximating INAR(∞) processes\, which provides an alternative discrete-time simulation method to the Euler-Maruyama method.  Extensive numerical experiments illustrate the improved accuracy and efficiency of the proposed simulation scheme as compared to the literature\, in the valuation of European options\, and also path-dependent options such as arithmetic Asian options\, lookback options and barrier options.
URL:https://www.math.ttu.edu/mathematicalfinance/event/seminar-date-reserved-4/
LOCATION:via Zoom
CATEGORIES:Fall 2025
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2025/07/Cui.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20250919T140000
DTEND;TZID=America/Chicago:20250919T150000
DTSTAMP:20260426T193913
CREATED:20250630T162129Z
LAST-MODIFIED:20250630T162129Z
UID:1993-1758290400-1758294000@www.math.ttu.edu
SUMMARY:Recent advances in stochastic volatility jump diffusions: Calibration and exotic option pricing
DESCRIPTION:Speaker: Dr. Jean-Phillipe Aguilar\, Head of Pricing Models Audit\, Societe Generale\, Paris La Defense \nAbstract:Stochastic Volatility Jump Diffusion (SVJ) models combine the advantages of both stochastic volatility and jump models\, while addressing some of their well-known limitations; moreover\, they often calibrate well in equity and FX markets. In this talk we will focus on a particular class of SVJ models\, namely the Heston Kou Double Exponential (HKDE) model\, that has recently been investigated in detail and shown to outperform challenger models possessing similar features. We will discuss the main properties of the HKDE model as well has its calibration to single stock and FX option data\, demonstrating a better fit of the volatility smile when compared to several challenger models; we will also discuss the smile sensitivity to the HKDE model parameters as well as their econometrics interpretations. Last\, we will provide application to the pricing and analysis of several generations of exotic contracts by means of advanced Fourier pricing (PROJ method in particular). \nJoint work with Gaetano Agazzotti\, Claudio Aglieri Rinella and Justin Lars Kirkby.
URL:https://www.math.ttu.edu/mathematicalfinance/event/recent-advances-in-stochastic-volatility-jump-diffusions-calibration-and-exotic-option-pricing/
LOCATION:via Zoom
CATEGORIES:Fall 2025
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2025/06/Aquilar-e1751300432196.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20250926T120000
DTEND;TZID=America/Chicago:20250926T130000
DTSTAMP:20260426T193913
CREATED:20250707T130236Z
LAST-MODIFIED:20250707T184623Z
UID:2048-1758888000-1758891600@www.math.ttu.edu
SUMMARY:Risk-aware Trading Portfolio Optimization
DESCRIPTION:Speakers: Dr. Marco Bianchetti\, Head of Market and Counterparty Risk IMA Methodologies\, Intesa Sanpaolo\, Milan\, Italy\n               Dr Fabio Vitale\, Senior Researcher\, CENTAI Institute\, Turin\, Italy \nAbstract: We investigate portfolio optimization in financial markets from a trading and risk management perspective. We term this task Risk-Aware Trading Portfolio Optimization (RATPO)\, formulate the corresponding optimization problem\, and propose an efficient Risk-Aware Trading Swarm (RATS) algorithm to solve it. The key elements of RATPO are a generic initial portfolio P\, a specific set of Unique Eligible Instruments (UEIs)\, their combination into an Eligible Optimization Strategy (EOS)\, an objective function\, and a set of constraints. RATS searches for an optimal EOS that\, added to P\, improves the objective function respecting the constraints. RATS is a specialized Particle Swarm Optimization method that leverages the parameterization of P in terms of UEIs\, enables parallel computation with a large number of particles\, and is fully general with respect to specific choices of the key elements\, which can be customized to encode financial knowledge and needs of traders and risk managers. We showcase two RATPO applications involving a real trading portfolio made of hundreds of different financial instruments\, an objective function combining both market risk (VaR) and profit & loss measures\, constraints on market sensitivities and UEIs trading costs. In the case of small-sized EOS\, RATS successfully identifies the optimal solution and demonstrates robustness with respect to hyper-parameter tuning. In the case of large-sized EOS\, RATS markedly improves the portfolio objective value\, optimizing risk and capital charge while respecting risk limits and preserving expected profits. Our work bridges the gap between the implementation of effective trading strategies and compliance with stringent regulatory and economic capital requirements\, allowing a better alignment of business and risk management objectives.
URL:https://www.math.ttu.edu/mathematicalfinance/event/seminar-date-reserved-6/
LOCATION:via Zoom
CATEGORIES:Fall 2025
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2025/07/Bianchetti_Vitale.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20251003T120000
DTEND;TZID=America/Chicago:20251003T130000
DTSTAMP:20260426T193913
CREATED:20250701T165239Z
LAST-MODIFIED:20250701T165239Z
UID:2005-1759492800-1759496400@www.math.ttu.edu
SUMMARY:New runs-based approach to testing value at risk forecasts
DESCRIPTION:Speaker: Prof. Marta Malecka\, Dept. of Statistical Methods\, Univ. of Lodz\, Lodz\, Poland \nAbstract: The reformed Basel framework has left value at risk (VaR) as a basic tool of validating risk models. Within this framework\, VaR independence tests have been regarded as critical to ensuring stability during periods of financial turmoil. However\, until now\, there is no consent among researchers regarding the choice of the appropriate test. The available procedures are either inaccurate in finite samples or need to rely on Monte Carlo simulations. To remedy these problems\, we propose a new method for testing VaR models\, based on the distribution of the number of runs. It outperforms the existing methods in two main aspects: First\, it is exact in finite samples and thus allows for perfect control over the Type 1 error; second\, its distribution is available in a closed form\, so it does not require simulations before implementation. We show that it is the most adequate current procedure for testing low-level VaR series\, which corresponds to today’s regulatory standards.
URL:https://www.math.ttu.edu/mathematicalfinance/event/new-runs-based-approach-to-testing-value-at-risk-forecasts/
LOCATION:via Zoom
CATEGORIES:Fall 2025
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2025/07/Malecka.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20251010T120000
DTEND;TZID=America/Chicago:20251010T130000
DTSTAMP:20260426T193913
CREATED:20250701T183111Z
LAST-MODIFIED:20250707T150143Z
UID:2015-1760097600-1760101200@www.math.ttu.edu
SUMMARY:Beyond Traditional Models: Assessing the Role of LSTM Networks in Volatility Prediction
DESCRIPTION:Speaker: Prof. Massimo Guidolin\, Baffi Carefin Center\, Bocconi Univ.\, Milan \nAbstract: This paper examines the out-of-sample accuracy of recurrent artificial neural networks (ANNs) compared to traditional econometric models for the prediction of realized volatility. We focus on a horserace between the heterogeneous autoregressive (HAR) model\, its Markov-switching extension (MS-HAR)\, multi-layer perceptrons (MLP)\, and long short-term memory (LSTM) networks across 31 international equity indices. Using high-frequency realized volatility data\, we evaluate predictive performance based on four loss functions and test for equal accuracy using Diebold-Mariano tests. Our results suggest that the HAR and MS-HAR models often deliver the most accurate forecasts\, outperforming LSTMs. However\, differences in out-of-sample accuracy between LSTM\, HAR\, and MS-HAR models are not always statistically significant. We conclude that ANNs do not consistently outperform HAR-based models in equity volatility forecasting.
URL:https://www.math.ttu.edu/mathematicalfinance/event/seminar-date-reserved/
LOCATION:via Zoom
CATEGORIES:Fall 2025
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2023/07/Guidolin-e1751900326458.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20251017T120000
DTEND;TZID=America/Chicago:20251017T130000
DTSTAMP:20260426T193913
CREATED:20250630T214748Z
LAST-MODIFIED:20250630T214748Z
UID:1998-1760702400-1760706000@www.math.ttu.edu
SUMMARY:Multi-hypothesis prediction for portfolio optimization: A structured ensemble learning approach to risk diversification
DESCRIPTION:Speaker: Dr. Alejandro Rodriguez Dominguez\, Director of Quantitative Analysis and Artificial Intelligence\, Miralta Finance Bank\, Madrid. \nAbstract: We introduce a novel framework for portfolio construction\, covering both selection and optimization\, based entirely on ensemble learning theory. A portfolio is modelled as an ensemble in a multi-hypothesis prediction setting\, with each constituent (base learner) focused on a specific hypothesis. This formulation connects the Bias–Variance–Diversity trade-off in ensemble models with out-of-sample portfolio performance. Diversity in the training of base learners translates to out-of-sample diversification\, allowing control over portfolio diversification via model training. \nPortfolio optimization is reframed as a supervised learning task\, where the target portfolio follows an ensemble combiner rule derived from Bregman divergences and ensemble model properties. When using Mean Squared Error\, the equal-weighted portfolio emerges as the target. Also\,it enables the use of alternative loss functions—treating the problem as classification or regression\, where targets follow ensemble combiner rules defined by Bregman divergences. A second source of out-of-sample diversification is also identified: the diversity in return predictions across base learners. Controlling this diversity at decision time further improves performance. When base learners exhibit similar error levels\, portfolios built from asset sets with more diverse return predictions\, but lower average sets forecasts\, outperform those with higher average sets predictions but less diversity. This defines a diversity-quality trade-off in predicted returns. Together\, these two sources of diversification expand the traditional boundaries of portfolio construction\, enabling meaningful diversification even within single-asset portfolios\, benefiting constrained strategies and redefining conventional notions of diversification. Theoretical results are empirically validated using full historical data from all S&P 500 constituents and a globally diversified set of 1\,300 bonds across multiple categories\, spanning over two decades and diverse market regimes. The diversity-quality trade-off is shown to be applicable across a wide range of portfolio optimization methods.
URL:https://www.math.ttu.edu/mathematicalfinance/event/multi-hypothesis-prediction-for-portfolio-optimization-a-structured-ensemble-learning-approach-to-risk-diversification/
LOCATION:via Zoom
CATEGORIES:Fall 2025
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2025/06/Rodriguez-e1751320394901.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20251024T120000
DTEND;TZID=America/Chicago:20251024T130000
DTSTAMP:20260426T193913
CREATED:20250630T160529Z
LAST-MODIFIED:20250630T160529Z
UID:1985-1761307200-1761310800@www.math.ttu.edu
SUMMARY:Option Pricing with a Compound CARMA(p\,q)-Hawkes
DESCRIPTION:Speaker: Prof. Lorenzo Mercuri\, Dept of Economics\, Management and Quantitative Methods\, Univ. of Milan \nAbstract: A self-exciting point process with a continuous-time autoregressive moving average intensity process\, named CARMA(p\,q)-Hawkes model\, has recently been introduced. The model generalizes the Hawkes process by substituting the Ornstein-Uhlenbeck intensity with a CARMA(p\,q) model where the associated state process is driven by the counting process itself. The proposed model preserves the same degree of tractability as the Hawkes process\, but it can reproduce more complex time-dependent structures observed in several market data. The paper presents a new model of asset price dynamics based on the CARMA(p\,q) Hawkes model. It is constructed using a compound version of it with a random jump size that is independent of both the counting and the intensity processes and can be employed as the main block for pure jump and (stochastic volatility) jump-diffusion processes. The numerical results for pricing European options illustrate that the new model can replicate the volatility smile observed in financial markets. Through an empirical analysis\, which is presented as a calibration exercise\, we highlight the role of higher order autoregressive and moving average parameters in pricing options.
URL:https://www.math.ttu.edu/mathematicalfinance/event/option-pricing-with-a-compound-carmapq-hawkes/
LOCATION:via Zoom
CATEGORIES:Fall 2025
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2025/06/Mercuri-e1751298511291.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20251031T120000
DTEND;TZID=America/Chicago:20251031T130000
DTSTAMP:20260426T193913
CREATED:20250707T130048Z
LAST-MODIFIED:20250707T142727Z
UID:2045-1761912000-1761915600@www.math.ttu.edu
SUMMARY:A general framework for pricing and hedging under local viability
DESCRIPTION:Speaker: Prof. Huy Chau\, Dept. Mathematics\, University of Manchester \nAbstract: In this paper\, a new approach for solving the problems of pricing and hedging derivatives is introduced in a general frictionless market setting. The method is applicable even in cases where an equivalent local martingale measure fails to exist. Our main results include a new superhedging duality for American options when wealth processes can be negative and trading strategies are subject to a cone constraint. This answers one of the questions raised by Fernholz\, Karatzas and Kardaras.
URL:https://www.math.ttu.edu/mathematicalfinance/event/seminar-date-reserved-5/
LOCATION:via Zoom
CATEGORIES:Fall 2025
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2025/07/Chau-scaled-e1751898311395.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20251107T140000
DTEND;TZID=America/Chicago:20251107T150000
DTSTAMP:20260426T193913
CREATED:20250701T183255Z
LAST-MODIFIED:20250707T141727Z
UID:2019-1762524000-1762527600@www.math.ttu.edu
SUMMARY:Robust Bayesian Portfolio Optimization
DESCRIPTION:Speaker: Dr. Carlos Andres Zapata Quimbayo\, ODEON\, Universidad Externado de Colombia\, Bogota \nAbstract: We implement a robust Bayesian framework for portfolio optimization that integrates Bayesian inference with robust optimization techniques. The model considers parameter uncertainty in expected returns and covariances by combining normal-inverse-Wishart and gamma distributions through ellipsoidal uncertainty sets. We apply this methodology to a stock portfolio from the Dow Jones Industrial Average and compare its performance with traditional mean-variance and robust portfolio models.
URL:https://www.math.ttu.edu/mathematicalfinance/event/seminar-date-reserved-3/
LOCATION:via Zoom
CATEGORIES:Fall 2025
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2025/07/Quimbayo-e1751897818959.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20251114T120000
DTEND;TZID=America/Chicago:20251114T130000
DTSTAMP:20260426T193913
CREATED:20250701T203028Z
LAST-MODIFIED:20250701T203028Z
UID:2023-1763121600-1763125200@www.math.ttu.edu
SUMMARY:Decomposition of the option pricing formula for infinite activity jump-diffusion stochastic volatility models
DESCRIPTION:Speaker: Prof. Josep Vives\, Department of Economical\, Financial and Actuarial Mathematics\, University of Barcelona \nAbstract: Let the log returns of an asset X(t) = log(S(t)) be defined on a risk neutral filtered probability space (Ω\, F\, (F(t); t ∈ [0\,T])\, P) for some 0 < T < ∞. Assume that X(t) is a stochastic volatility jump-diffusion model with infinite activity jumps. In this paper\, we obtain an Alós-type decomposition of the plain vanilla option price under a jump-diffusion model with stochastic volatility and infinite activity jumps via two approaches. Firstly\, we obtain a closed-form approximate option price formula. The obtained formula is compared with some previous results available in the literature. In the infinite activity but finite variation case jumps of absolute size smaller than a given threshold ɛ are approximated by their mean while larger jumps are modeled by a suitable compound Poisson process. A general decomposition is derived as well as a corresponding approximate version. Lastly\, numerical approximations of option prices for some examples of Tempered Stable jump processes are obtained. In particular\, for the Variance Gamma one\, where the approximate price performs well at the money.
URL:https://www.math.ttu.edu/mathematicalfinance/event/decomposition-of-the-option-pricing-formula-for-infinite-activity-jump-diffusion-stochastic-volatility-models/
LOCATION:via Zoom
CATEGORIES:Fall 2025
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2025/07/vives-e1751401644916.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20251121T140000
DTEND;TZID=America/Chicago:20251121T150000
DTSTAMP:20260426T193913
CREATED:20250709T164803Z
LAST-MODIFIED:20250709T211916Z
UID:2076-1763733600-1763737200@www.math.ttu.edu
SUMMARY:Multivariate affine GARCH in portfolio optimization. Analytical solutions and applications
DESCRIPTION:Speaker: Prof. Marcos Escobar-Anel\, Dept. of Statistics & Actuarial Sciences\, University of Western Ontario\, London \nAbstract: Abstract: This paper develops an optimal portfolio allocation formula for multi-assets where the covariance structure follows a multivariate affine GARCH(1\,1) process. We work under an expected utility framework\, considering an investor with constant relative risk aversion (CRRA) utility who wants to maximize the expected utility from terminal wealth. After approximating the self-financing condition\, we derive closed-form expressions for all the quantities of interest to investors: optimal allocations\, optimal wealth process\, and value function. Such a complete analytical solution is a first in the GARCH multivariate literature. Our empirical analyses show a significant impact of multidimensional heteroscedasticity in portfolio decisions compared to a setting of constant covariance as per Merton’s embedded solution.
URL:https://www.math.ttu.edu/mathematicalfinance/event/seminar-date-reserved-7/
LOCATION:via Zoom
CATEGORIES:Fall 2025
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2025/07/Escobar-Anel-scaled-e1752095775466.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20251128T140000
DTEND;TZID=America/Chicago:20251128T150000
DTSTAMP:20260426T193913
CREATED:20250701T182602Z
LAST-MODIFIED:20250701T182602Z
UID:2013-1764338400-1764342000@www.math.ttu.edu
SUMMARY:No Seminar - Thanksgiving Break
DESCRIPTION:
URL:https://www.math.ttu.edu/mathematicalfinance/event/no-seminar-thanksgiving-break/
CATEGORIES:Fall 2025
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2023/01/unhappy.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260130T140000
DTEND;TZID=America/Chicago:20260130T150000
DTSTAMP:20260426T193913
CREATED:20251119T230500Z
LAST-MODIFIED:20251119T230500Z
UID:2414-1769781600-1769785200@www.math.ttu.edu
SUMMARY:Lambda Value-at-Risk under ambiguity and risk sharing
DESCRIPTION:Speaker: Alexander Schied\, Professor and Munich Re Chair in Stochastic Finance\, Dept. of Statistics and Actuarial Science\, University of Waterloo \nAbstract: We investigate Lambda Value-at-Risk (ΛVaR) under ambiguity\, where the ambiguity is represented by a family of probability measures. We establish that for increasing Lambda functions\, the robust (i.e.\, worst-case) ΛVaR under such an ambiguity set is equivalent to ΛVaR computed with respect to a capacity\, a novel extension in the literature. This framework unifies and extends both traditional ΛVaR and Choquet quantiles (Value-at-Risk under ambiguity). We analyze the fundamental properties of this extended risk measure and establish a novel equivalent representation for ΛVaR under capacities with monotone Lambda functions in terms of families of downsets. Moreover\, explicit formulas are derived for robust ΛVaR when ambiguity sets are characterized by ϕ-divergence and the likelihood ratio constraints\, respectively. We further explore the applications in risk sharing among multiple agents. We demonstrate that the family of risk measures induced by families of downsets is closed under inf-convolution. In particular\, we prove that the inf-convolution of ΛVaR with capacities and monotone Lambda functions is another ΛVaR under a capacity. The explicit forms of optimal allocations are also derived. Moreover\, we obtain more explicit results for risk sharing under ambiguity sets characterized by ϕ-divergence and likelihood ratio constraints.
URL:https://www.math.ttu.edu/mathematicalfinance/event/lambda-value-at-risk-under-ambiguity-and-risk-sharing/
LOCATION:via Zoom
CATEGORIES:Spring 2026
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2025/11/Shied-e1763593334799.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260206T140000
DTEND;TZID=America/Chicago:20260206T150000
DTSTAMP:20260426T193913
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260220T120000
DTEND;TZID=America/Chicago:20260220T140000
DTSTAMP:20260426T193913
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260227T140000
DTEND;TZID=America/Chicago:20260227T150000
DTSTAMP:20260426T193913
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260306T140000
DTEND;TZID=America/Chicago:20260306T150000
DTSTAMP:20260426T193913
CREATED:20251202T222739Z
LAST-MODIFIED:20251202T222739Z
UID:2495-1772805600-1772809200@www.math.ttu.edu
SUMMARY:Mean-CVaR portfolio optimization under ESG disagreement
DESCRIPTION:Speaker: Prof. Davide Lauria\, Department of Management\, University of Bergamo \nAbstract: The ESG score of a company is a measure of its commitment to environmental\, social and governance investing standards. ESG scores are produced by rating agencies using unique and proprietary methodologies. The complexity of measurement and the lack of widely accepted standards contribute to inconsistencies across agencies. Discrepancies in ratings issued by multiple data providers are particularly relevant in portfolio optimization problems that integrate ESG objectives into the classical risk-reward framework. In this work\, we specifically study the impact on portfolio composition by examining Mean-CVaR-ESG optimal portfolios\, where the objective function incorporates the portfolio’s ESG score. To address ESG score discrepancies\, we introduce a Distributionally Robust Optimization (DRO) reformulation of the Mean-CVaR-ESG model and assess its potential benefits. Our findings reveal a persistent divergence in optimal strategies across the investment horizon when ESG values from different rating agencies are used. We then apply the DRO approach by replacing a single provider’s ESG score with a statistic derived from the scores of five different agencies. Our results show that\, in this case\, the DRO approach effectively mitigates score discrepancies by significantly reducing optimal portfolio concentration while enhancing the ESG evaluation of optimal portfolios across all rating agencies.
URL:https://www.math.ttu.edu/mathematicalfinance/event/mean-cvar-portfolio-optimization-under-esg-disagreement/
LOCATION:via Zoom
CATEGORIES:Spring 2026
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2021/06/Screen-Shot-2021-06-29-at-10.17.32-PM-e1764695697225.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260313T140000
DTEND;TZID=America/Chicago:20260313T150000
DTSTAMP:20260426T193913
CREATED:20251201T181134Z
LAST-MODIFIED:20251201T181158Z
UID:2482-1773410400-1773414000@www.math.ttu.edu
SUMMARY:Portfolio optimization in a market with hidden Gaussian drift and expert opinions
DESCRIPTION:Speaker: Prof. Ralf Wunderlich\, Institute of Mathematics\, Brandenburg University of Technology Cottbus-Sentenberg\, Germany \nAbstract: This paper investigates the optimal selection of portfolios for power utility maximizing investors in a financial market where stock returns depend on a hidden Gaussian mean reverting drift process. Information on the drift is obtained from returns and expert opinions in the form of noisy signals about the current state of the drift arriving at fixed and known dates or randomly over time.  Applying Kalman filter techniques we derive estimates of the hidden drift which are described by the conditional mean and covariance of the drift given the observations. The utility maximization problem is solved with dynamic programming methods. \nFor expert opinions that arrive on fixed dates\, the corresponding dynamic programming equation (DPE) can be solved in closed form\, and the value function and\nthe optimal trading strategy for an investor can be derived. They make it possible to quantify the monetary value of the information provided by the expert opinions. We illustrate our theoretical findings with results from numerical experiments. \nIf the arrival dates are random and modeled as the jump times of a homogeneous Poisson process\, the DPE is a partial integro-differential equation and degenerate in the diffusion part of the differential operator. We therefore adopt a regularization approach and add a Brownian perturbation to the state process\, scaled by a small parameter that approaches zero. We prove that the value functions of the regularized problems converge to the value function of the original problem. This enables the construction of ε-optimal strategies.
URL:https://www.math.ttu.edu/mathematicalfinance/event/portfolio-optimization-in-a-market-with-hidden-gaussian-drift-and-expert-opinions/
LOCATION:via Zoom
CATEGORIES:Spring 2026
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2025/12/Wunderlich-e1764612535510.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260320T140000
DTEND;TZID=America/Chicago:20260320T150000
DTSTAMP:20260426T193913
CREATED:20251124T233111Z
LAST-MODIFIED:20251124T233212Z
UID:2451-1774015200-1774018800@www.math.ttu.edu
SUMMARY:No Seminar - Spring Break
DESCRIPTION:
URL:https://www.math.ttu.edu/mathematicalfinance/event/no-seminar-thanksgiving-break-2/
CATEGORIES:Spring 2026
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2023/01/unhappy.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260327T120000
DTEND;TZID=America/Chicago:20260327T130000
DTSTAMP:20260426T193913
CREATED:20251215T170856Z
LAST-MODIFIED:20251215T170925Z
UID:2554-1774612800-1774616400@www.math.ttu.edu
SUMMARY:A time-stepping deep gradient flow method for option pricing in (rough) diffusion models
DESCRIPTION:Speaker: Professor Antonis Papapantoleon\, Delft Institute of Applied Mathematics\, EEMCS\, Delft University of Technology \nAbstract: We develop a novel deep learning approach for pricing European options in diffusion models\, that can efficiently handle high-dimensional problems resulting from Markovian approximations of rough volatility models. The option pricing partial differential equation is reformulated as an energy minimization problem\, which is approximated in a time-stepping fashion by deep artificial neural networks. The proposed scheme respects the asymptotic behavior of option prices for large levels of moneyness and adheres to a priori known bounds for option prices. The accuracy and efficiency of the proposed method is assessed in a series of numerical examples\, with particular focus in the lifted Heston model.
URL:https://www.math.ttu.edu/mathematicalfinance/event/a-time-stepping-deep-gradient-flow-method-for-option-pricing-in-rough-diffusion-models/
LOCATION:via Zoom
CATEGORIES:Spring 2026
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2025/12/Papapantoleon-e1765818384977.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260403T120000
DTEND;TZID=America/Chicago:20260403T130000
DTSTAMP:20260426T193913
CREATED:20251121T173246Z
LAST-MODIFIED:20251121T173854Z
UID:2430-1775217600-1775221200@www.math.ttu.edu
SUMMARY:ABIDES-MARL: A Multi-Agent Reinforcement Learning Environment for Endogenous Price Formation and Execution in a Limit Order Book
DESCRIPTION:Speaker: Dr. Jean-Loup Dupret\, Department of Mathematics\, ETH Zurich \nAbstract: We present ABIDES-MARL\, a framework that combines a new multi-agent reinforcement learning (MARL) methodology with a new realistic limit-order-book (LOB) simulation system to study equilibrium behavior in complex financial market games. The system extends ABIDES-Gym by decoupling state collection from kernel interruption\, enabling synchronized learning and decision-making for multiple adaptive agents while maintaining compatibility with standard RL libraries. It preserves key market features such as price–time priority and discrete tick sizes. Methodologically\, we use MARL to approximate equilibrium-like behavior in multi-period trading games with a finite number of heterogeneous agents—an informed trader\, a liquidity trader\, noise traders\, and competing market makers—all with individual price impacts. This setting bridges optimal execution and market microstructure by embed ding the liquidity trader’s optimization problem within a strategic trading environment. We validate the approach by solving an extended Kyle model within the simulation system\, recovering the gradual price discovery phenomenon. We then extend the analysis to a liquidity trader’s problem where market liquidity arises endogenously and show that\, at equilibrium\, execution strategies shape market-maker behavior and price dynamics. ABIDES-MARL provides a reproducible foundation for analyzing equilibrium and strategic adaptation in realistic markets and contributes toward building economically interpretable agentic AI systems for finance.
URL:https://www.math.ttu.edu/mathematicalfinance/event/abides-marl-a-multi-agent-reinforcement-learning-environment-for-endogenous-price-formation-and-execution-in-a-limit-order-book/
LOCATION:via Zoom
CATEGORIES:Spring 2026
ATTACH;FMTTYPE=image/png:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2025/11/Dupret-e1763746714889.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260417T140000
DTEND;TZID=America/Chicago:20260417T150000
DTSTAMP:20260426T193913
CREATED:20251210T203233Z
LAST-MODIFIED:20260109T214820Z
UID:2528-1776434400-1776438000@www.math.ttu.edu
SUMMARY:Corruption via Mean Field Games
DESCRIPTION:Speaker: Dr. Kirill Golubnichiy\, Department of Mathematics & Statistics\, Texas Tech University \nAbstract: A new mathematical model describing the evolution of a corrupted hierarchy is derived. This model is based on mean field games theory. We consider a retrospective (inverse) problem for this model. From an applied standpoint\, this problem amounts to reconstructing the past activity of the corrupted hierarchy using present-time data for this community. We derive three new Carleman estimates. These estimates yield Hölder stability and uniqueness results for both the retrospective problem and its generalized version. The Hölder stability estimates characterize how the error in the solution of the retrospective problem depends on the error in the input data.
URL:https://www.math.ttu.edu/mathematicalfinance/event/corruption-via-mean-field-games/
LOCATION:via Zoom
CATEGORIES:Spring 2026
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2024/04/Golubnichiy.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260424T090000
DTEND;TZID=America/Chicago:20260424T100000
DTSTAMP:20260426T193913
CREATED:20260311T203054Z
LAST-MODIFIED:20260421T153933Z
UID:2826-1777021200-1777024800@www.math.ttu.edu
SUMMARY:Dynamic asymmetric tail dependence structure among multi-asset classes for portfolio management: Dynamic skew- copula approach
DESCRIPTION:Speaker: Dr. Kakeru Ito\, Senior Portfolio Manager (Multi-Asset / Quants)\, Mizuho Securities Co.\, Ltd.\nand Visiting Researcher\, Graduate School of Management\, Tokyo Metropolitan University  \nAbstract:  This study proposes AC dynamic skew-𝑡 copula with cDCC model to capture the dynamic asymmetric tail dependence structure among multi-asset classes (government bonds\, corporate bonds\, equities\, and REITs). We provide new evidence that lower tail dependence coefficients increased compared to upper ones for all pairs in the COVID-19 crash and the recent high inflation period\, indicating that the diversification effect through multi-asset investment decreased. Our empirical analysis also shows that in terms of AIC and BIC\, dynamic AC skew-𝑡 copula fits data of multi-asset classes better than other dynamic elliptical copulas because it can consider the above dependence structure characteristics. Furthermore\, out-of-sample analysis reveals that considering an asymmetry of tail dependence structure at each point with an AC dynamic skew-𝑡 copula enhances expected shortfall (ES) estimation accuracy and the performance of a minimum ES portfolio. These results indicate that capturing dynamic asymmetric tail dependence is crucial for multi-asset portfolio management.
URL:https://www.math.ttu.edu/mathematicalfinance/event/dynamic-asymmetric-tail-dependence-structure-among-multi-asset-classes-for-portfolio-management-dynamic-skew-copula-approach/
LOCATION:via Zoom
CATEGORIES:Seminars,Spring 2026
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2026/04/ito-e1776785927134.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260501T140000
DTEND;TZID=America/Chicago:20260501T150000
DTSTAMP:20260426T193913
CREATED:20251229T180758Z
LAST-MODIFIED:20251229T180758Z
UID:2595-1777644000-1777647600@www.math.ttu.edu
SUMMARY:Differential Beliefs in Financial Markets Under Information Constraints: A Modeling Perspective
DESCRIPTION:Speaker: Prof. Karen Grigorian\, Department of Statistics and Applied Probability\, UC Santa Barbara \nAbstract: We apply the theory of McKean-Vlasov-type SDEs to study several problems related to market efficiency in the context of partial information and partially observable financial markets: (i) convergence of reduced-information market price processes to the true price process under an increasing information flow; (ii) a specific mechanism of shrinking biases under increasing information flows; (iii) optimal aggregation of expert opinions by a trader seeking a positive alpha. All these problems are studied by means of (conditional) McKean-Vlasov-type SDEs\, Wasserstein barycenters\, KL divergence and relevant tools from convex optimization\, optimal control and nonlinear filtering. We supply the theoretical results in (i)-(iii) with concrete simulations demonstrating how the proposed models can be applied in practice to model financial markets under information constraints and the arbitrage-seeking behavior of traders with differential beliefs.
URL:https://www.math.ttu.edu/mathematicalfinance/event/differential-beliefs-in-financial-markets-under-information-constraints-a-modeling-perspective/
LOCATION:via Zoom
CATEGORIES:Spring 2026
END:VEVENT
END:VCALENDAR