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PRODID:-//Mathematical Finance - ECPv5.7.0//NONSGML v1.0//EN
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X-WR-CALNAME:Mathematical Finance
X-ORIGINAL-URL:https://www.math.ttu.edu/mathematicalfinance
X-WR-CALDESC:Events for Mathematical Finance
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TZID:America/Chicago
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TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20240310T080000
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TZNAME:CST
DTSTART:20241103T070000
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20241004T140000
DTEND;TZID=America/Chicago:20241004T150000
DTSTAMP:20260414T011604
CREATED:20240430T214605Z
LAST-MODIFIED:20240430T214605Z
UID:1391-1728050400-1728054000@www.math.ttu.edu
SUMMARY:Option Pricing under a Generalized Black–Scholes Model with Stochastic Interest Rates\, Stochastic Strings\, and Lévy Jumps
DESCRIPTION:Speaker: Prof. Steven P. Clark\, Dept. of Finance\, UNC Charlotte \nAbstract: We introduce a novel option pricing model that features stochastic interest rates along with an underlying price process driven by stochastic string shocks combined with pure jump Lévy processes. Substituting the Brownian motion in the Black–Scholes model with a stochastic string leads to a class of option pricing models with expiration-dependent volatility. Further extending this Generalized Black–Scholes (GBS) model by adding Lévy jumps to the returns generating processes results in a new framework generalizing all exponential Lévy models. We derive four distinct versions of the model\, with each case featuring a different jump process: the finite activity lognormal and double–exponential jump diffusions\, as well as the infinite activity CGMY process and generalized hyperbolic Lévy motion. In each case\, we obtain closed or semi-closed form expressions for European call option prices which generalize the results obtained for the original models. Empirically\, we evaluate the performance of our model against the skews of S&P 500 call options\, considering three distinct volatility regimes. Our findings indicate that: (a) model performance is enhanced with the inclusion of jumps; (b) the GBS plus jumps model outperform the alternative models with the same jumps; (c) the GBS-CGMY jump model offers the best fit across volatility regimes.
URL:https://www.math.ttu.edu/mathematicalfinance/event/option-pricing-under-a-generalized-black-scholes-model-with-stochastic-interest-rates-stochastic-strings-and-levy-jumps/
LOCATION:via Zoom
CATEGORIES:Fall 2024,Seminars
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2024/04/SClark.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20241011T140000
DTEND;TZID=America/Chicago:20241011T150000
DTSTAMP:20260414T011604
CREATED:20240501T154246Z
LAST-MODIFIED:20240920T182126Z
UID:1399-1728655200-1728658800@www.math.ttu.edu
SUMMARY:Seminar Cancelled
DESCRIPTION:Title: Time changes\, Fourier transforms and the joint calibration to the S&P500/VIX smiles \nSpeaker: Prof. Laura Ballotta\, Bayes Business School\, City University of London \nAbstract: We develop a model based on time changed Lévy processes and study its ability of reproducing the joint S&P500/VIX implied volatility smiles and the VIX futures prices – a problem known in the literature as the `joint calibration problem’. The model admits semi-analytical characteristic functions for the key quantities\, and therefore efficient Fourier based pricing schemes can be deployed. We focus on a specification of the proposed general setting which uses purely discontinuous processes. Results from the application to market data show satisfactory performances in solving the joint calibration problem\, and therefore demonstrate that the class of affine processes can provide a workable fit.
URL:https://www.math.ttu.edu/mathematicalfinance/event/time-changes-fourier-transforms-and-the-joint-calibration-to-the-sp500-vix-smiles/
LOCATION:via Zoom
CATEGORIES:Fall 2024,Seminars
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2024/05/Ballotta.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20241018T130000
DTEND;TZID=America/Chicago:20241018T140000
DTSTAMP:20260414T011604
CREATED:20240502T142834Z
LAST-MODIFIED:20240502T142834Z
UID:1410-1729256400-1729260000@www.math.ttu.edu
SUMMARY:Elicitability and identifiability of tail risk measures
DESCRIPTION:Speaker: Dr. Tobias Fissler\, Department of Mathematics\, ETH Zurich \nAbstract: Tail risk measures are fully determined by the distribution of the underlying loss beyond its quantile at a certain level\, with Value-at-Risk and Expected Shortfall being prime examples. They are induced by law-based risk measures\, called their generators\, evaluated on the tail distribution.  This talk establishes joint identifiability and elicitability results of tail risk measures together with the corresponding quantile\, provided that their generators are identifiable and elicitable\, respectively. As an example\, we establish the joint identifiability and elicitability of the tail expectile together with the quantile. The corresponding consistent scores constitute a novel class of weighted scores\, nesting the known class of scores of Fissler and Ziegel for the Expected Shortfall together with the quantile. For statistical purposes\, our results pave the way to easier model fitting for tail risk measures via regression and the generalized method of moments\, but also model comparison and model validation in terms of established backtesting procedures. \nThe talk is based on joint work with Ruodu Wang\, Fangda Liu and Linxiao Wei.
URL:https://www.math.ttu.edu/mathematicalfinance/event/elicitability-and-identifiability-of-tail-risk-measures/
LOCATION:via Zoom
CATEGORIES:Fall 2024,Seminars
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2024/05/fissler.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20241025T100000
DTEND;TZID=America/Chicago:20241025T110000
DTSTAMP:20260414T011604
CREATED:20240801T143537Z
LAST-MODIFIED:20240801T143642Z
UID:1512-1729850400-1729854000@www.math.ttu.edu
SUMMARY:Estimation and backtesting of risk measures with emphasis on distortion risk measures
DESCRIPTION:Speaker: Prof. Hideatsu Tsukahara\, Dept.. of Economics\, Seijo University\, Tokyo \nAbstract: Statistical methodology has an important role to play in risk measurement. In this paper\, we will review and discuss some statistical issues on risk measures. Examples we consider are value-at-risk\, expected shortfall\, expectiles\, and distortion risk measures. Several methods of estimating these risk measures based on time series data have been proposed\, and we will try to explain in some detail. Another main issue we would like to address is a problem of backtesting: the evaluation of risk measurement procedures using historical data\, by comparing ex ante estimates of loss distributions or risk measures with the ex post realized losses. There have been several suggestions concerning backtestability of risk measures\, which will be discuss in detail. We also examine and suggest backtesting procedures for predictive distributions\, expected shortfall and distortion risk measures.
URL:https://www.math.ttu.edu/mathematicalfinance/event/estimation-and-backtesting-of-risk-measures-with-emphasis-on-distortion-risk-measures/
LOCATION:via Zoom
CATEGORIES:Fall 2024,Seminars
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2024/08/Tsukahara.jpg
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