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METHOD:PUBLISH
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:20250309T080000
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TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:20251102T070000
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TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20260308T080000
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TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:20261101T070000
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20250117T090000
DTEND;TZID=America/Chicago:20250117T100000
DTSTAMP:20260429T045946
CREATED:20241216T170525Z
LAST-MODIFIED:20250110T185141Z
UID:1643-1737104400-1737108000@www.math.ttu.edu
SUMMARY:Deep learning-based portfolio optimization with transaction costs
DESCRIPTION:Speaker: Prof. Aihua (Eva) Zhang\, College of Science\, Math & Tech.\, Wenzhou-Kean University\, Wenzhou China \nAbstract: In order to obtain the optimal portfolio strategy maximizing the accumulated terminal wealth with transaction costs\, in this paper\, we propose a new prediction-based portfolio method combining with a long short-term memory (in short\, LSTM) network which is an extended type of recurrent neural networks in deep learning. Our proposed method\, named as LSTM-Prediction-based Portfolio (LSTM-PbP) with transaction costs\, consists of two technical steps: finding the optimal portfolio strategy and predicting the future relative prices. For the price prediction\, we use multi-layer LSTM; while for the optimal portfolio strategy\, we solve the constraint maximization problem via relative entropy. We then update the future portfolio weights using the predicted prices and past portfolio weights. We iterate the process until the final investment period. Numerical experiments are also provided to show the accumulated wealth by following the obtained optimal portfolio strategy in comparison with the accumulated wealth under buy-and-hold strategy. The numerical results show that our model consistently outperforms the buy-and-hold strategy.
URL:https://www.math.ttu.edu/mathematicalfinance/event/to-be-provided/
LOCATION:via Zoom
CATEGORIES:Seminars,Spring 2025
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2024/12/zhang.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20250124T130000
DTEND;TZID=America/Chicago:20250124T140000
DTSTAMP:20260429T045946
CREATED:20241216T170921Z
LAST-MODIFIED:20241216T172221Z
UID:1651-1737723600-1737727200@www.math.ttu.edu
SUMMARY:Robust estimation of the range-based GARCH model: Forecasting volatility\, value at risk and expected shortfall of cryptocurrencies
DESCRIPTION:Speaker: Prof. Piotr Fiszeder\, Dept. Econ. & Stat.\, Nicolaus Copernicus Univ.\, Torun\, Poland \nAbstract: Traditional volatility models do not work well when volatility changes rapidly and in the presence of outliers. Therefore\, two lines of improvements have been developed separately in the existing literature. Range-based models benefit from efficient volatility estimates based on low and high prices\, while robust methods deal with outliers. We propose a range-based GARCH model with a bounded M-estimator\, which combines these two improvements with a third new improvement: a modified robust method\, which adds elasticity in treating the outliers. We apply this model to Bitcoin\, Ethereum Classic\, Ethereum\, and Litecoin and find that it forecasts variances\, value at risk\, and expected shortfall more accurately than the standard GARCH model\, the standard range-based GARCH model\, and the GARCH model with the robust estimation. Utilization of high and low prices joined with a novel treatment of outliers makes our model perform well during extreme periods when traditional volatility models fail. \nThis work is joint with\nProf. Marta Malecka\, Faculty of Economics and Sociology\, University of Łódź\, Łódź\, Poland\nand\nPeter Molnár\, UiS Business School\, University of Stavanger\, Stavanger\, Norway.
URL:https://www.math.ttu.edu/mathematicalfinance/event/robust-estimation-of-the-range-based-garch-model-forecasting-volatility-value-at-risk-and-expected-shortfall-of-cryptocurrencies/
LOCATION:via Zoom
CATEGORIES:Seminars,Spring 2025
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2024/12/fiszeder.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20250131T140000
DTEND;TZID=America/Chicago:20250131T150000
DTSTAMP:20260429T045946
CREATED:20241216T171218Z
LAST-MODIFIED:20241216T171717Z
UID:1653-1738332000-1738335600@www.math.ttu.edu
SUMMARY:Hedonic Models Incorporating Environmental\, Social\, and Governance Factors for Time Series of Average Annual Home Prices
DESCRIPTION:Speaker: Jason Bailey\, Dept. of Mathematics & Statistics\, Texas Tech University \nAbstract:
URL:https://www.math.ttu.edu/mathematicalfinance/event/hedonic-models-incorporating-environmental-social-and-governance-factors-for-time-series-of-average-annual-home-prices/
LOCATION:via Zoom
CATEGORIES:Seminars,Spring 2025
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2021/06/Screen-Shot-2021-06-29-at-10.18.41-PM.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20250207T120000
DTEND;TZID=America/Chicago:20250207T130000
DTSTAMP:20260429T045946
CREATED:20241216T171540Z
LAST-MODIFIED:20241216T171630Z
UID:1655-1738929600-1738933200@www.math.ttu.edu
SUMMARY:Do online attention and sentiment affect cryptocurrencies’ correlations?
DESCRIPTION:Speaker: Prof. Aurelio Bariviera\, Dept.  of Business\, Universitat Rovira i Virgili\, Reus\, Spain \nAbstract: This paper adopts a versatile conditional correlation approach to explore daily seasonality in the major cryptocurrencies. Given the lack of clear fundamental value in this market and the active online profile of investors\, the study also relates cryptocurrency cross-correlations to online market attention and sentiment. Our results highlight that while investor attention has a positive effect\, sentiment has a much stronger negative impact on the correlations. These findings can offer interesting insights for investors and regulators\, as the influence of market attention and sentiment on the correlations has important implications for portfolio diversification and market stability.
URL:https://www.math.ttu.edu/mathematicalfinance/event/do-online-attention-and-sentiment-affect-cryptocurrencies-correlations/
LOCATION:via Zoom
CATEGORIES:Seminars,Spring 2025
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2024/12/bariviero-scaled.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20250221T120000
DTEND;TZID=America/Chicago:20250221T130000
DTSTAMP:20260429T045946
CREATED:20241216T171945Z
LAST-MODIFIED:20241216T172028Z
UID:1662-1740139200-1740142800@www.math.ttu.edu
SUMMARY:Multi-asset return risk measures
DESCRIPTION:Speaker:
URL:https://www.math.ttu.edu/mathematicalfinance/event/multi-asset-return-risk-measures/
LOCATION:via Zoom
CATEGORIES:Seminars,Spring 2025
ATTACH;FMTTYPE=image/png:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2024/12/Laudage.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20250228T140000
DTEND;TZID=America/Chicago:20250228T150000
DTSTAMP:20260429T045946
CREATED:20241216T172519Z
LAST-MODIFIED:20241216T172544Z
UID:1669-1740751200-1740754800@www.math.ttu.edu
SUMMARY:Water as a commodity in hydropower generation
DESCRIPTION:Speaker: Prof. Eduardo Schwartz\, Beedie School of Business\, Simon Fraser Univ. \nAbstract: The increased impact of extreme weather events and draughts has prompted the rapid growth of the water market. This paper analyzes the optimal operation of a reservoir that generates electricity and manages the water by trading water rights. We extend the framework introduced in a recent paper by Figuerola-Ferretti\, Schwartz\, and Segarra (2024) to include the water price process in a model that accounts for water inventory and the electricity price as two independent price processes. The model is implemented under the stochastic optimal control approach and calibrated using monthly data for a reservoir in the estate of California. The water price process and the dynamics of inflow into the reservoir include their dependence on the California Drought Severity Index. Results show that the underlying water and electricity price dynamics exhibit a high degree of uncertainty and seasonality. They also demonstrate that\, under the calibrated model parameters on average for the parameters used\, around 25% of the revenue generated by the reservoir arises from the revenue obtained from selling water rights. The water contribution to revenue generation would increase under enhanced severity of climate change.
URL:https://www.math.ttu.edu/mathematicalfinance/event/water-as-a-commodity-in-hydropower-generation/
LOCATION:via Zoom
CATEGORIES:Seminars,Spring 2025
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2024/12/schwartz.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20250314T120000
DTEND;TZID=America/Chicago:20250314T130000
DTSTAMP:20260429T045946
CREATED:20241216T173056Z
LAST-MODIFIED:20241216T173056Z
UID:1675-1741953600-1741957200@www.math.ttu.edu
SUMMARY:Dynamic tail risk forecasting: What do realized skewness and kurtosis add?
DESCRIPTION:Speaker: Prof. Giuseppe Storti\, Dept. Econ & Stat.\, University of Salerno\, Fisciano\, IT \nAbstract:  This paper compares the accuracy of tail risk forecasts with a focus on including realized skewness and kurtosis in ”additive” and ”multiplicative” models. Utilizing a panel of 960 US stocks\, we conduct diagnostic tests\, employ scoring functions\, and implement rolling window forecasting to evaluate the performance of Value at Risk (VaR) and Expected Shortfall (ES) forecasts. Additionally\, we examine the impact of the window length on forecast accuracy. We propose model specifications that incorporate realized skewness and kurtosis for enhanced precision. Our findings provide insights into the importance of considering skewness and kurtosis in tail risk modeling\, contributing to the existing literature and offering practical implications for risk practitioners and researchers.
URL:https://www.math.ttu.edu/mathematicalfinance/event/dynamic-tail-risk-forecasting-what-do-realized-skewness-and-kurtosis-add/
LOCATION:via Zoom
CATEGORIES:Seminars,Spring 2025
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2024/12/storti-scaled.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250321
DTEND;VALUE=DATE:20250322
DTSTAMP:20260429T045946
CREATED:20241216T173247Z
LAST-MODIFIED:20241216T173247Z
UID:1678-1742515200-1742601599@www.math.ttu.edu
SUMMARY:No Seminar: Spring Vacation
DESCRIPTION:
URL:https://www.math.ttu.edu/mathematicalfinance/event/no-seminar-spring-vacation/
CATEGORIES:Seminars,Spring 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:20250328T120000
DTEND;TZID=America/Chicago:20250328T130000
DTSTAMP:20260429T045946
CREATED:20250117T164450Z
LAST-MODIFIED:20250120T154902Z
UID:1798-1743163200-1743166800@www.math.ttu.edu
SUMMARY:Portfolio optimization in deformed time
DESCRIPTION:Speaker: Assoc. Prof. Malick Fall\, Center for Research in Economics and Management\, Univ. of Rennes\, Fr. \nAbstract: The expected return and covariance matrix are commonly calculated on a calendar time scale (e.g. daily or monthly data). In this article\, we assess the relevance of calculating them on a new time scale derived from traded volume. In particular\, we evaluate portfolio optimizations where returns evolve on a data-based rather than calendar time scale. We empirically test the impact of this change of scale by comparing the performance of two well-known portfolio optimizations in an out-of-sample framework. We find that this change leads to gains in both risk-adjusted return and risk. We also find that the degree of deviation from the normal distribution (and independence) of returns is greater with returns calculated in calendar time than in data-based time\, which explains the outperformance of this new approach.
URL:https://www.math.ttu.edu/mathematicalfinance/event/portfolio-optimization-in-deformed-time/
LOCATION:via Zoom
CATEGORIES:Seminars,Spring 2025
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2025/01/fall.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20250404T120000
DTEND;TZID=America/Chicago:20250404T130000
DTSTAMP:20260429T045946
CREATED:20241216T173557Z
LAST-MODIFIED:20241216T173557Z
UID:1682-1743768000-1743771600@www.math.ttu.edu
SUMMARY:Measures of stochastic non-dominance in portfolio optimization
DESCRIPTION:Speaker: Prof. Miloš Kopa\, Dept. Probability & Mathematics\, Charles University\, Prague \nAbstract: Stochastic dominance rules are well-characterized and widely used. This work aims to describe and better understand the situations when they do not hold by developing measures of stochastic non-dominance. They quantify the error caused by assuming that one random variable dominates another one when it does not. To calculate them\, we search for a hypothetical random variable that satisfies the stochastic dominance relationship and is as close to the original one as possible. The Wasserstein distance between the optimal hypothetical random variable and the original one is considered as the measure of stochastic non-dominance. Depending on the conditions imposed on the probability distribution of the hypothetical random variable\, we distinguish between general and specific measures of stochastic non-dominance. We derive their exact values for random variables with uniform\, normal\, and exponential distributions. We present relations to almost first-order stochastic dominance and to tractable almost stochastic dominance. Using monthly returns of twelve assets captured by the German stock index DAX\, we solve portfolio optimization problems with the first-order and second-order stochastic dominance constraints. The measures of stochastic non-dominance allow us to compare the optimal portfolios with respect to different orders of stochastic dominance from a new angle. We also defined the closest dominating and closest approximately dominating portfolios. They brought a better understanding of the relationship between the two types of optimal portfolios. Using moving window analysis\, the relationship of the in-sample measure of stochastic non-dominance to out-of-sample performance was studied\, too.
URL:https://www.math.ttu.edu/mathematicalfinance/event/measures-of-stochastic-non-dominance-in-portfolio-optimization/
LOCATION:via Zoom
CATEGORIES:Seminars,Spring 2025
ATTACH;FMTTYPE=image/png:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2024/12/kopa.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20250411T140000
DTEND;TZID=America/Chicago:20250411T150000
DTSTAMP:20260429T045946
CREATED:20241216T173924Z
LAST-MODIFIED:20241216T173924Z
UID:1686-1744380000-1744383600@www.math.ttu.edu
SUMMARY:Custom ESG Indexing: How Direct ESG Indexing Can Solve Many Responsible Investing Problems
DESCRIPTION:Speaker: Prof. Dr. Dirk Soehnholz\, CEO: Soehnholz ESG GmbH and Soehnholz Asset Management GmbH; Prof. of Asset Management\, Leipzig University \nAbstract: Responsible investments are booming\, but criticism has been growing\, too. I start by outlining the different dimensions and characteristics of responsible investments. I also describe a free tool to develop bespoke\, responsible investment policies that could serve as the basis to select appropriate funds. \nThere are now many standard active and passive\, so called responsible mutual funds available. I use three ambitious sustainable funds to show sustainability limits. In summary\, since responsible investments can vary pretty much by investor\, most standard funds may not be ideal for investors. \nMuch of the legitimate criticism of responsible investing can be avoided with bespoke portfolios. I outline simple ways to customize responsible portfolios with direct investments\, not limited to equity investments. There are many arguments for rather concentrated direct rule-based ESG investments. These (self-indexed) solutions could be efficiently delivered by wealth managers or used by self-directed investors. An (over-)diversification focus of mutual fund providers and investment advisors may be the biggest limitation for the growth of direct or custom ESG indexing. Most of the arguments apply to private and institutional investors alike\, although institutional investors will probably diversify more than private investors.
URL:https://www.math.ttu.edu/mathematicalfinance/event/custom-esg-indexing-how-direct-esg-indexing-can-solve-many-responsible-investing-problems/
LOCATION:via Zoom
CATEGORIES:Seminars,Spring 2025
ATTACH;FMTTYPE=image/png:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2024/12/soehnholz.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20250418T120000
DTEND;TZID=America/Chicago:20250418T130000
DTSTAMP:20260429T045946
CREATED:20241216T174226Z
LAST-MODIFIED:20241216T174226Z
UID:1690-1744977600-1744981200@www.math.ttu.edu
SUMMARY:Option pricing in a stochastic delay volatility model
DESCRIPTION:Speaker: Prof. Álvaro Guinea Julia\, Dept. Industrial Org.\, Comillas Pontifical University ICADE-ICAI\, Madrid \nAbstract: This work introduces a new stochastic volatility model with delay parameters in the volatility process\, extending the Barndorff–Nielsen and Shephard model. It establishes an analytical expression for the log price characteristic function\, which can be applied to price European options. Empirical analysis on S&P500 European call options shows that adding delay parameters reduces mean squared error. This is the first instance of providing an analytical formula for the log price characteristic function in a stochastic volatility model with multiple delay parameters. We also provide a Monte Carlo scheme that can be used to simulate the model.
URL:https://www.math.ttu.edu/mathematicalfinance/event/option-pricing-in-a-stochastic-delay-volatility-model/
LOCATION:via Zoom
CATEGORIES:Seminars,Spring 2025
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2024/12/julia.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20250425T120000
DTEND;TZID=America/Chicago:20250425T130000
DTSTAMP:20260429T045946
CREATED:20241216T174606Z
LAST-MODIFIED:20250425T213005Z
UID:1694-1745582400-1745586000@www.math.ttu.edu
SUMMARY:Drought parametric insurances by a Two-Step machine learning approach under climate change scenarios
DESCRIPTION:Speaker: Dr. Hirbod Assa\, Founding team | Quantitative researcher\, Edge Technologies; Director and founder\, Model Library Ltd. \nAbstract: In this paper\, we utilize data from the IPCC to develop a predictive model for the Palmer Drought Severity Index (PDSI). Our approach involves a two-step modeling process: initially applying a random forest regression\, followed by a linear regression correction\, achieving a forecasting accuracy exceeding 94%. This method aims to predict the drought index using a minimal set of climate indices\, with projections extending to the year 2100 based on CORDEX CMIP5 models. The analysis focuses on selected U.S. states\, assessing the impacts of different climate scenarios and climate change under various RCP pathways. On that basis we design a parametric insurance on drought index. If time permits\, we will also explore the implications of these drought projections on supply chain risks in the beef commodity market.
URL:https://www.math.ttu.edu/mathematicalfinance/event/title-to-be-provided/
LOCATION:via Zoom
CATEGORIES:Seminars,Spring 2025
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2024/12/hirbod.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20250502T140000
DTEND;TZID=America/Chicago:20250502T150000
DTSTAMP:20260429T045946
CREATED:20241216T174853Z
LAST-MODIFIED:20241216T174853Z
UID:1698-1746194400-1746198000@www.math.ttu.edu
SUMMARY:ESG Mania and Institutional Trading
DESCRIPTION:Speaker: Prof. Riza Demirer\,  Dept. Economics & Finance\, School of Business\, Southern Illinois University\, Edwardsville\, IL \nAbstract: Recent years have seen that institutional investors simultaneously crowd in (buy) the ESG stock market. The aim of this study is to investigate the underlying motivations and their economic consequences. The empirical results are consistent with the hypothesis of ESG washing rather than that of impact investing\, that is ESG-washing investors dominate the market. Specifically\, institutional herding exists widely in the ESG market; small\, dedicated\, and ill-performed institutions exhibit strong intensity to herd into the ESG stock market; herding institutions experienced outflows recently and attract more flows when they herd.  Herding institutions neither outperform nor that their trading significantly impacts stock prices (except in bad times). Our study highlights the mixed and complicated motivations of ESG investors from an institutional perspective\, which helps reconcile the opposite findings of sincere ESG investing and washing behavior documented in the growing literature on sustainable investments.
URL:https://www.math.ttu.edu/mathematicalfinance/event/esg-mania-and-institutional-trading/
LOCATION:via Zoom
CATEGORIES:Seminars,Spring 2025
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2024/12/demirer.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20250829T140000
DTEND;TZID=America/Chicago:20250829T150000
DTSTAMP:20260429T045946
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:20260429T045946
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:20260429T045946
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:20260429T045946
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:20260429T045946
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20251003T120000
DTEND;TZID=America/Chicago:20251003T130000
DTSTAMP:20260429T045946
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:20260429T045946
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20251017T120000
DTEND;TZID=America/Chicago:20251017T130000
DTSTAMP:20260429T045946
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20251024T120000
DTEND;TZID=America/Chicago:20251024T130000
DTSTAMP:20260429T045946
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20251031T120000
DTEND;TZID=America/Chicago:20251031T130000
DTSTAMP:20260429T045946
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20251107T140000
DTEND;TZID=America/Chicago:20251107T150000
DTSTAMP:20260429T045946
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20251114T120000
DTEND;TZID=America/Chicago:20251114T130000
DTSTAMP:20260429T045946
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20251121T140000
DTEND;TZID=America/Chicago:20251121T150000
DTSTAMP:20260429T045946
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20251128T140000
DTEND;TZID=America/Chicago:20251128T150000
DTSTAMP:20260429T045946
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260130T140000
DTEND;TZID=America/Chicago:20260130T150000
DTSTAMP:20260429T045946
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260206T140000
DTEND;TZID=America/Chicago:20260206T150000
DTSTAMP:20260429T045946
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
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END:VEVENT
END:VCALENDAR