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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;TZID=America/Chicago:20250404T120000
DTEND;TZID=America/Chicago:20250404T130000
DTSTAMP:20260414T011629
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
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20250411T140000
DTEND;TZID=America/Chicago:20250411T150000
DTSTAMP:20260414T011629
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
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20250418T120000
DTEND;TZID=America/Chicago:20250418T130000
DTSTAMP:20260414T011629
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
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20250425T120000
DTEND;TZID=America/Chicago:20250425T130000
DTSTAMP:20260414T011629
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
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