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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:20250309T080000
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DTSTART:20251102T070000
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DTSTART;TZID=America/Chicago:20250314T120000
DTEND;TZID=America/Chicago:20250314T130000
DTSTAMP:20260414T123612
CREATED:20241216T173056Z
LAST-MODIFIED:20241216T173056Z
UID:1675-1741953600-1741957200@www.math.ttu.edu
SUMMARY:Dynamic tail risk forecasting: What do realized skewness and kurtosis add?
DESCRIPTION:Speaker: Prof. Giuseppe Storti\, Dept. Econ & Stat.\, University of Salerno\, Fisciano\, IT \nAbstract:  This paper compares the accuracy of tail risk forecasts with a focus on including realized skewness and kurtosis in ”additive” and ”multiplicative” models. Utilizing a panel of 960 US stocks\, we conduct diagnostic tests\, employ scoring functions\, and implement rolling window forecasting to evaluate the performance of Value at Risk (VaR) and Expected Shortfall (ES) forecasts. Additionally\, we examine the impact of the window length on forecast accuracy. We propose model specifications that incorporate realized skewness and kurtosis for enhanced precision. Our findings provide insights into the importance of considering skewness and kurtosis in tail risk modeling\, contributing to the existing literature and offering practical implications for risk practitioners and researchers.
URL:https://www.math.ttu.edu/mathematicalfinance/event/dynamic-tail-risk-forecasting-what-do-realized-skewness-and-kurtosis-add/
LOCATION:via Zoom
CATEGORIES:Seminars,Spring 2025
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2024/12/storti-scaled.jpg
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20250321
DTEND;VALUE=DATE:20250322
DTSTAMP:20260414T123612
CREATED:20241216T173247Z
LAST-MODIFIED:20241216T173247Z
UID:1678-1742515200-1742601599@www.math.ttu.edu
SUMMARY:No Seminar: Spring Vacation
DESCRIPTION:
URL:https://www.math.ttu.edu/mathematicalfinance/event/no-seminar-spring-vacation/
CATEGORIES:Seminars,Spring 2025
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2023/01/unhappy.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20250328T120000
DTEND;TZID=America/Chicago:20250328T130000
DTSTAMP:20260414T123612
CREATED:20250117T164450Z
LAST-MODIFIED:20250120T154902Z
UID:1798-1743163200-1743166800@www.math.ttu.edu
SUMMARY:Portfolio optimization in deformed time
DESCRIPTION:Speaker: Assoc. Prof. Malick Fall\, Center for Research in Economics and Management\, Univ. of Rennes\, Fr. \nAbstract: The expected return and covariance matrix are commonly calculated on a calendar time scale (e.g. daily or monthly data). In this article\, we assess the relevance of calculating them on a new time scale derived from traded volume. In particular\, we evaluate portfolio optimizations where returns evolve on a data-based rather than calendar time scale. We empirically test the impact of this change of scale by comparing the performance of two well-known portfolio optimizations in an out-of-sample framework. We find that this change leads to gains in both risk-adjusted return and risk. We also find that the degree of deviation from the normal distribution (and independence) of returns is greater with returns calculated in calendar time than in data-based time\, which explains the outperformance of this new approach.
URL:https://www.math.ttu.edu/mathematicalfinance/event/portfolio-optimization-in-deformed-time/
LOCATION:via Zoom
CATEGORIES:Seminars,Spring 2025
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2025/01/fall.jpg
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