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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:20250309T080000
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DTSTART;TZID=America/Chicago:20250117T090000
DTEND;TZID=America/Chicago:20250117T100000
DTSTAMP:20260412T135947
CREATED:20241216T170525Z
LAST-MODIFIED:20250110T185141Z
UID:1643-1737104400-1737108000@www.math.ttu.edu
SUMMARY:Deep learning-based portfolio optimization with transaction costs
DESCRIPTION:Speaker: Prof. Aihua (Eva) Zhang\, College of Science\, Math & Tech.\, Wenzhou-Kean University\, Wenzhou China \nAbstract: In order to obtain the optimal portfolio strategy maximizing the accumulated terminal wealth with transaction costs\, in this paper\, we propose a new prediction-based portfolio method combining with a long short-term memory (in short\, LSTM) network which is an extended type of recurrent neural networks in deep learning. Our proposed method\, named as LSTM-Prediction-based Portfolio (LSTM-PbP) with transaction costs\, consists of two technical steps: finding the optimal portfolio strategy and predicting the future relative prices. For the price prediction\, we use multi-layer LSTM; while for the optimal portfolio strategy\, we solve the constraint maximization problem via relative entropy. We then update the future portfolio weights using the predicted prices and past portfolio weights. We iterate the process until the final investment period. Numerical experiments are also provided to show the accumulated wealth by following the obtained optimal portfolio strategy in comparison with the accumulated wealth under buy-and-hold strategy. The numerical results show that our model consistently outperforms the buy-and-hold strategy.
URL:https://www.math.ttu.edu/mathematicalfinance/event/to-be-provided/
LOCATION:via Zoom
CATEGORIES:Seminars,Spring 2025
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2024/12/zhang.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20250124T130000
DTEND;TZID=America/Chicago:20250124T140000
DTSTAMP:20260412T135947
CREATED:20241216T170921Z
LAST-MODIFIED:20241216T172221Z
UID:1651-1737723600-1737727200@www.math.ttu.edu
SUMMARY:Robust estimation of the range-based GARCH model: Forecasting volatility\, value at risk and expected shortfall of cryptocurrencies
DESCRIPTION:Speaker: Prof. Piotr Fiszeder\, Dept. Econ. & Stat.\, Nicolaus Copernicus Univ.\, Torun\, Poland \nAbstract: Traditional volatility models do not work well when volatility changes rapidly and in the presence of outliers. Therefore\, two lines of improvements have been developed separately in the existing literature. Range-based models benefit from efficient volatility estimates based on low and high prices\, while robust methods deal with outliers. We propose a range-based GARCH model with a bounded M-estimator\, which combines these two improvements with a third new improvement: a modified robust method\, which adds elasticity in treating the outliers. We apply this model to Bitcoin\, Ethereum Classic\, Ethereum\, and Litecoin and find that it forecasts variances\, value at risk\, and expected shortfall more accurately than the standard GARCH model\, the standard range-based GARCH model\, and the GARCH model with the robust estimation. Utilization of high and low prices joined with a novel treatment of outliers makes our model perform well during extreme periods when traditional volatility models fail. \nThis work is joint with\nProf. Marta Malecka\, Faculty of Economics and Sociology\, University of Łódź\, Łódź\, Poland\nand\nPeter Molnár\, UiS Business School\, University of Stavanger\, Stavanger\, Norway.
URL:https://www.math.ttu.edu/mathematicalfinance/event/robust-estimation-of-the-range-based-garch-model-forecasting-volatility-value-at-risk-and-expected-shortfall-of-cryptocurrencies/
LOCATION:via Zoom
CATEGORIES:Seminars,Spring 2025
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2024/12/fiszeder.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20250131T140000
DTEND;TZID=America/Chicago:20250131T150000
DTSTAMP:20260412T135947
CREATED:20241216T171218Z
LAST-MODIFIED:20241216T171717Z
UID:1653-1738332000-1738335600@www.math.ttu.edu
SUMMARY:Hedonic Models Incorporating Environmental\, Social\, and Governance Factors for Time Series of Average Annual Home Prices
DESCRIPTION:Speaker: Jason Bailey\, Dept. of Mathematics & Statistics\, Texas Tech University \nAbstract:
URL:https://www.math.ttu.edu/mathematicalfinance/event/hedonic-models-incorporating-environmental-social-and-governance-factors-for-time-series-of-average-annual-home-prices/
LOCATION:via Zoom
CATEGORIES:Seminars,Spring 2025
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2021/06/Screen-Shot-2021-06-29-at-10.18.41-PM.jpg
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