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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:20220313T080000
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DTSTART:20221106T070000
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DTSTART;TZID=America/Chicago:20221104T140000
DTEND;TZID=America/Chicago:20221104T150000
DTSTAMP:20260405T002126
CREATED:20220920T200640Z
LAST-MODIFIED:20230109T144555Z
UID:887-1667570400-1667574000@www.math.ttu.edu
SUMMARY:A unified Bayesian framework for pricing catastrophe bond derivatives
DESCRIPTION:Speaker: Prof. Matthew Dixon\, Applied Mathematics\, Illinois Institute of Technology \nAbstract: Catastrophe (CAT) bond markets are incomplete and hence carry uncertainty in instrument pricing. As such various pricing approaches have been proposed\, but none treat the uncertainty in catastrophe occurrences and interest rates in a sufficiently flexible and statistically reliable way within a unifying asset pricing framework. Consequently\, little is known empirically about the expected risk-premia of CAT bonds. The primary contribution of this paper is to present a unified Bayesian CAT bond pricing framework based on uncertainty quantification of catastrophes and interest rates. Our framework allows for complex beliefs about catastrophe risks to capture the distinct and common patterns in catastrophe occurrences\, and when combined with stochastic interest rates\, yields a unified asset pricing approach with informative expected risk premia. Specifically\, using a modified collective risk model — Dirichlet Prior-Hierarchical Bayesian Collective Risk Model (DP-HBCRM) framework — we model catastrophe risk via a model-based clustering approach. Interest rate risk is modeled as a CIR process under the Bayesian approach. As a consequence of casting CAT pricing models into our framework\, we evaluate the price and expected risk premia of various CAT bond contracts corresponding to clustering of catastrophe risk profiles. Numerical experiments show how these clusters reveal how CAT bond prices and expected risk premia relate to claim frequency and loss severity.\nThis is joint work with Dixon Domfeh and Arpita Chatterjee.
URL:https://www.math.ttu.edu/mathematicalfinance/event/a-unified-bayesian-framework-for-pricing-catastrophe-bond-derivatives/
LOCATION:via Zoom
CATEGORIES:Colloquia,Fall 2022
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2023/01/dixon.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20221111T140000
DTEND;TZID=America/Chicago:20221111T150000
DTSTAMP:20260405T002126
CREATED:20220920T200835Z
LAST-MODIFIED:20230109T144626Z
UID:889-1668175200-1668178800@www.math.ttu.edu
SUMMARY:The economic impact of ESG ratings
DESCRIPTION:Speaker: Prof. Julian Koelbel\, School of Finance\, University of St. Gallen \nAbstract: This study examines the impact of ESG ratings on mutual fund holdings\, stock returns\, corporate investment\, and corporate ESG practices\, using panel event studies. Looking specifically at changes in the MSCI ESG rating\, we document that rating downgrades reduce ownership by mutual funds with a dedicated ESG strategy\, while upgrades increase it. We find a negative long-term response of stock returns to downgrades and a slower and weaker positive response to upgrades. Regarding firm responses\, we find no significant effect of up- or downgrades on capital expenditure. We find that firms adjust their ESG practices following rating changes\, but only in the governance dimension. These results suggest that ESG rating changes matter in financial markets\, but so far have only a limited impact on the real economy.
URL:https://www.math.ttu.edu/mathematicalfinance/event/the-economic-impact-of-esg-ratings/
LOCATION:via Zoom
CATEGORIES:Colloquia,Fall 2022
ATTACH;FMTTYPE=image/png:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2023/01/koelbel.png
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20221118T140000
DTEND;TZID=America/Chicago:20221118T150000
DTSTAMP:20260405T002126
CREATED:20220920T201044Z
LAST-MODIFIED:20230109T144718Z
UID:891-1668780000-1668783600@www.math.ttu.edu
SUMMARY:Statistical analysis and stochastic interest rate modelling for valuing the future with implications in climate change mitigation
DESCRIPTION:Speaker: Prof. John Geanakoplos\, Economics\, Yale University \nAbstract: Statistical analysis and stochastic interest rate modelling for valuing the future with implications in climate change mitigation High future discounting rates favor inaction on present expending while lower rates advise for a more immediate political action. A possible approach to this key issue in global economy is to take historical time series for nominal interest rates and inflation\, and to construct then real interest rates and finally obtaining the resulting discount rate according to a specific stochastic model. Extended periods of negative real interest rates\, in which inflation dominates over nominal rates\, are commonly observed\, occurring in many epochs and in all countries. This feature leads us to choose a well-known model in statistical physics\, the Ornstein-Uhlenbeck model\, as a basic dynamical tool in which real interest rates randomly fluctuate and can become negative\, even if they tend to revert to a positive mean value. By covering 14 countries over hundreds of years we suggest different scenarios and include an error analysis in order to consider the impact of statistical uncertainty in our results. We find that only 4 of the countries have positive long-run discount rates while the other ten countries have negative rates. Even if one rejects the countries where hyperinflation has occurred\, our results support the need to consider low discounting rates. The results provided by these fourteen countries significantly increase the priority of confronting global actions such as climate change mitigation. We finally extend the analysis by first allowing for fluctuations of the mean level in the Ornstein-Uhlenbeck model and secondly by considering modified versions of the Feller and lognormal models. In both cases\, results remain basically unchanged thus demonstrating the robustness of the results presented.
URL:https://www.math.ttu.edu/mathematicalfinance/event/statistical-analysis-and-stochastic-interest-rate-modelling-for-valuing-the-future-with-implications-in-climate-change-mitigation/
LOCATION:via Zoom
CATEGORIES:Colloquia,Fall 2022
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2023/01/geanakoplos.jpg
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