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PRODID:-//Mathematical Finance - ECPv5.7.0//NONSGML v1.0//EN
CALSCALE:GREGORIAN
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:20240310T080000
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DTSTART:20241103T070000
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20240920T120000
DTEND;TZID=America/Chicago:20240920T130000
DTSTAMP:20260520T204629
CREATED:20240430T213543Z
LAST-MODIFIED:20240508T155354Z
UID:1385-1726833600-1726837200@www.math.ttu.edu
SUMMARY:To hedge or not to hedge? Cryptocurrencies\, gold and oil against stock market risk
DESCRIPTION:Speaker: Prof. Agata Kliber\, Dept of Applied Mathematics\, Poznan University of Economics & Business\nco-Authors: Prof. Krzysztof Echaust\, Dept. of Operations Research & Mathematical Economics\, Poznan University of Economics & Business\nProf. Małgorzata Just\, Dept. of Finance & Accounting\, Poznan University of Life Sciences \nAbstract: The article aims to determine whether any hedging strategy against stock market risk\, performed using instruments popular in the literature (gold\, cryptocurrencies and oil)\, can beat index futures. As a hedging strategy\, we understand a pair-wise portfolio consisting of a long position in stocks and a short position in a hedging instrument put together to minimise the portfolio variance. As a benchmark\, we analyse optimal and naive hedging strategies with futures contracts. We demonstrate that\, regardless of the stock market\, the best hedging strategy focused on variance minimisation requires using index futures. Both strategies: the optimisation-based one and the naive one\, beat the dynamic strategies utilising the remaining hedging assets. Therefore\, from a risk-minimisation point of view\, investors have no motivation to implement cryptocurrencies\, gold or oil in hedging strategy against stock market risk. The results are robust with respect to hedging against tail risk.
URL:https://www.math.ttu.edu/mathematicalfinance/event/to-hedge-or-not-to-hedge-cryptocurrencies-gold-and-oil-against-stock-market-risk/
LOCATION:via Zoom
CATEGORIES:Fall 2024,Seminars
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2024/04/Kliber.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20240927T120000
DTEND;TZID=America/Chicago:20240927T130000
DTSTAMP:20260520T204629
CREATED:20240501T152309Z
LAST-MODIFIED:20240501T152309Z
UID:1396-1727438400-1727442000@www.math.ttu.edu
SUMMARY:ESG performance and investment efficiency: The impact of information asymmetry
DESCRIPTION:Speaker: Prof. Seda Erdogan\, Dept. International Trade & Finance\, Kadir Has University \nAbstract: This paper investigates the relationship between firms’ engagement in environmental\, social\, and governance (ESG) activities and corporate investment efficiency\, using 1\,094 firms from 21 countries in Europe\, covering the years 2002–2019. We conduct our estimations using fixed effects panel data techniques and address potential endogeneity with instrumental variables (IV) estimations. We provide evidence that overall ESG engagement is positively and significantly associated with investment efficiency. Analyzing overinvestment and underinvestment scenarios shows that ESG engagement decreases only overinvestment problems. Within the underinvestment scenario\, we observe that ESG engagement is beneficial only for firms with higher information asymmetries. Thus\, information asymmetry matters in the underinvestment case. We next show that four firm-level channels—information asymmetry\, financial constraints\, cash flows\, and risk—link ESG performance to investment inefficiency. Additional analysis shows that firms with extreme ESG scores (i.e.\, very low and very high) do not experience significant reductions in investment inefficiency. Altogether\, our findings draw attention to the critical role of ESG performance and information asymmetry in determining corporate investment efficiency.
URL:https://www.math.ttu.edu/mathematicalfinance/event/esg-performance-and-investment-efficiency-the-impact-of-information-asymmetry/
LOCATION:via Zoom
CATEGORIES:Fall 2024,Seminars
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2024/05/erdogan.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20241004T140000
DTEND;TZID=America/Chicago:20241004T150000
DTSTAMP:20260520T204629
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20241011T140000
DTEND;TZID=America/Chicago:20241011T150000
DTSTAMP:20260520T204629
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:20260520T204629
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:20260520T204629
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20241104T120000
DTEND;TZID=America/Chicago:20241104T130000
DTSTAMP:20260520T204629
CREATED:20240729T184309Z
LAST-MODIFIED:20240729T195746Z
UID:1491-1730721600-1730725200@www.math.ttu.edu
SUMMARY:Pricing options with a new hybrid neural network model
DESCRIPTION:Speaker: Dr. Yossi Shvimer\, Research Associate\, School of Finance and Management\, SOAS University of London \nAbstract: A novel hybrid option pricing model using a deep learning neural network has been developed. The hybrid model keeps the traditional option pricing model with the same input parameters while simultaneously adjusting the model with neural network methods to improve accuracy when applied to real market data\, especially in OTM options. The new hybrid model demonstrates superior accuracy compared to both traditional parametric and non-parametric option pricing models for both Call and Put options across all moneyness levels. The empirical results of the hybrid model provide an explanation for the deviation from the Put-Call parity observed in real market data.
URL:https://www.math.ttu.edu/mathematicalfinance/event/pricing-options-with-a-new-hybrid-neural-network-model/
LOCATION:via Zoom
CATEGORIES:Fall 2024,Seminars
ATTACH;FMTTYPE=image/png:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2024/07/Yossi_Shvimer.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20241115T140000
DTEND;TZID=America/Chicago:20241115T150000
DTSTAMP:20260520T204629
CREATED:20240729T184547Z
LAST-MODIFIED:20240816T214112Z
UID:1493-1731679200-1731682800@www.math.ttu.edu
SUMMARY:Stochastic dominance\, stochastic volatility\, and jump risk: new theory interprets old results
DESCRIPTION:Speaker: Prof Stylianos Perrakis\, John Molson School of Business\, Concordia Univ\, Montreal \n Abstract: The stochastic dominance (SD) approach is applied to the valuation of index options in frictionless markets for a wide class of stochastic volatility (SV) processes. SD allows for the derivation of a unique\, exponential option pricing kernel based on the physical underlying return and volatility dynamics. A lower bound and an upper bound on option prices are obtained\, for a wide class of stochastic volatility jump (SVJ) processes that feature jumps in addition to diffusion. Using parameter estimates for the physical process from high-profile studies\, the bounds are shown to be remarkably tight\, especially for the empirically important class of short-term near-the-money options. The bounds are in many cases inconsistent with separate parameter estimates for the risk-neutral process that are extracted from observed option prices: for many option series\, the risk-neutral value exceeds the SD upper bound. This inconsistency points at the possibility that the distributional shape of the risk-neutral process is mis-specified or that the parameters are estimated without properly taking the option bid-ask spread into account.
URL:https://www.math.ttu.edu/mathematicalfinance/event/stochastic-dominance-stochastic-volatility-and-jump-risk-new-theory-interprets-old-results/
LOCATION:via Zoom
CATEGORIES:Fall 2024,Seminars
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2024/07/perrakis.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20241122T140000
DTEND;TZID=America/Chicago:20241122T150000
DTSTAMP:20260520T204629
CREATED:20240430T212747Z
LAST-MODIFIED:20240903T172510Z
UID:1379-1732284000-1732287600@www.math.ttu.edu
SUMMARY:Inverse Problem for Forecasting Stock Options Prices
DESCRIPTION:Speaker: Dr. Kirill Golubnichiy\, Dept of Math & Statistics\, Texas Tech University \nAbstract: We present a new heuristic mathematical model for accurate forecasting of prices of stock options for 1-2 trading days ahead of the present one. This new technique uses the Black-Scholes equation supplied by new intervals for the underlying stock and new initial and boundary conditions for option prices. The Black-Scholes equation was solved in the positive direction of the time variable\, This ill-posed initial boundary value problem was solved by the so-called Quasi-Reversibility Method (QRM). This approach with an added trading strategy was tested on the market data for 368 stock options and good forecasting results were demonstrated. In the current paper\, we use the geometric Brownian motion to provide an explanation of that effectivity using computationally simulated data for European call options. We also provide a convergence analysis for QRM. The key tool of that analysis is a Carleman estimate.
URL:https://www.math.ttu.edu/mathematicalfinance/event/inverse-problem-for-forecasting-stock-options-prices/
LOCATION:via Zoom
CATEGORIES:Fall 2024,Seminars
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2024/04/Golubnichiy.jpeg
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