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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
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DTSTART:20240310T080000
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DTSTART:20241103T070000
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DTSTART;TZID=America/Chicago:20240823T140000
DTEND;TZID=America/Chicago:20240823T150000
DTSTAMP:20260414T065453
CREATED:20240430T211647Z
LAST-MODIFIED:20240430T211647Z
UID:1374-1724421600-1724425200@www.math.ttu.edu
SUMMARY:Quanto Option Pricing on a Multivariate Lévy Process Model with Generative Artificial Intelligence
DESCRIPTION:Speaker: Prof. Aaron YS Kim\, College of Business\, Stony Brook University \nAbstract: In this study\, we discuss a machine learning technique to price exotic options with two underlying assets based on a non-Gaussian Levy process model.  We introduce a new multivariate Levy process model named the generalized normal tempered stable (gNTS) process\, which is defined by time-changed multivariate Brownian motion. Since the gNTS process does not provide a simple analytic formula for the probability density function (PDF)\, we use the conditional real-valued non-volume preserving (CRealNVP) model\, which is a type of flow-based generative network. Then\, we discuss the no-arbitrage pricing on the gNTS model for pricing the quanto option whose underlying assets consist of a foreign index and foreign exchange rate. We present the training of the CRealNVP model to learn the PDF of the gNTS process using a training set generated by Monte Carlo simulation.  Next\, we estimate the parameters of the gNTS model with the trained CRealNVP model using the empirical data observed in the market.  Finally\, we provide a method to find an equivalent martingale measure on the gNTS model and to price the quanto option using the CRealNVP model with the risk-neutral parameters of the gNTS model.
URL:https://www.math.ttu.edu/mathematicalfinance/event/quanto-option-pricing-on-a-multivariate-levy-process-model-with-generative-artificial-intelligence/
LOCATION:via Zoom
CATEGORIES:Fall 2024,Seminars
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2024/04/youngskim.jpg
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DTSTART;TZID=America/Chicago:20240830T140000
DTEND;TZID=America/Chicago:20240830T150000
DTSTAMP:20260414T065453
CREATED:20240501T161202Z
LAST-MODIFIED:20240501T161202Z
UID:1404-1725026400-1725030000@www.math.ttu.edu
SUMMARY:Gold-backed cryptocurrencies: A hedging tool against categorical and regional financial stress
DESCRIPTION:Speaker: Prof. Md. Rayfayet Alam\, Dept. of Finance and Economics\, University of Tennessee at Chattanooga \nAbstract: This study evaluates the potential of gold-backed cryptocurrencies\, such as Tether Gold and PAX Gold\, as a hedge and safe haven against global\, regional\, and categorical financial stresses. Hedge and safe haven properties of gold-backed cryptocurrencies are also compared with those of gold and Bitcoin. For the analyses\, dynamic conditional correlation (DCC) and quantile coherency techniques are applied to daily data from February 2020 to March 2023. The results show that Tether Gold and PAX Gold are strong safe havens against the US and equity-valuation-related financial stress but weak safe havens against global financial stress. Tether Gold is a weak safe haven against credit-related financial stress as well. Tether Gold is a strong hedge against US financial stress but a weak hedge against aggregate financial stress of developed economies and that of emerging economies. In our sample\, gold-backed cryptocurrencies usually outperform gold and Bitcoin as a hedge and safe haven against financial stresses. The Quantile coherency analysis shows that Tether Gold is a hedge against low to moderate financial stress and a safe haven against extreme financial stresses. These findings have important implications for investors\, risk-managers and policy makers.
URL:https://www.math.ttu.edu/mathematicalfinance/event/gold-backed-cryptocurrencies-a-hedging-tool-against-categorical-and-regional-financial-stress-2/
LOCATION:via Zoom
CATEGORIES:Fall 2024,Seminars
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2024/05/alam-scaled.jpg
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