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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:20230312T080000
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DTSTART:20231105T070000
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DTSTART;TZID=America/Chicago:20231108T140000
DTEND;TZID=America/Chicago:20231108T150000
DTSTAMP:20260521T014854
CREATED:20231027T153049Z
LAST-MODIFIED:20231031T180108Z
UID:1197-1699452000-1699455600@www.math.ttu.edu
SUMMARY:Modeling Bitcoin Volatility: A Dual Perspective Analysis
DESCRIPTION:Speaker: Prof. Abootaleb Shirvani\, Dept. of Mathematical Sciences\, Kean University \nAbstract: Understanding the volatility of speculative assets is critical for investment decisions. Given that Bitcoin is considered\, at least by some\, a potential alternative to fiat money\, its volatility characteristics are of particular concern. It is\, therefore\, essential to comprehend and appropriately model the process governing Bitcoin’s volatility. \nIn this presentation\, we offer two perspectives for analyzing Bitcoin’s volatility. First\, we introduce a doubly subordinated Levy process called the Normal Double Inverse Gaussian to model the time series properties of the cryptocurrency Bitcoin. We also developed an arbitrage-free option pricing model based on the NDIG process\, providing a fresh perspective on Bitcoin valuation. \nWithin this model\, we derive two distinct measures of Bitcoin volatility. The first measure combines NDIG option pricing with the Chicago Board Options Exchange VIX model to compute an implied volatility measure that reflects the viewpoints of options traders. The second measure investigates implied volatility in the real world\, considering the perspectives of spot traders and utilizing an intrinsic time formulation. \nBoth volatility measures are compared to a historical standard deviation-based volatility. With appropriate linear scaling\, the NDIG process perfectly captures the observed in-sample volatility.
URL:https://www.math.ttu.edu/mathematicalfinance/event/title-tba/
LOCATION:via Zoom
CATEGORIES:Fall 2023,Seminars
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