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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:20250309T080000
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DTSTART:20251102T070000
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20251003T120000
DTEND;TZID=America/Chicago:20251003T130000
DTSTAMP:20260417T055722
CREATED:20250701T165239Z
LAST-MODIFIED:20250701T165239Z
UID:2005-1759492800-1759496400@www.math.ttu.edu
SUMMARY:New runs-based approach to testing value at risk forecasts
DESCRIPTION:Speaker: Prof. Marta Malecka\, Dept. of Statistical Methods\, Univ. of Lodz\, Lodz\, Poland \nAbstract: The reformed Basel framework has left value at risk (VaR) as a basic tool of validating risk models. Within this framework\, VaR independence tests have been regarded as critical to ensuring stability during periods of financial turmoil. However\, until now\, there is no consent among researchers regarding the choice of the appropriate test. The available procedures are either inaccurate in finite samples or need to rely on Monte Carlo simulations. To remedy these problems\, we propose a new method for testing VaR models\, based on the distribution of the number of runs. It outperforms the existing methods in two main aspects: First\, it is exact in finite samples and thus allows for perfect control over the Type 1 error; second\, its distribution is available in a closed form\, so it does not require simulations before implementation. We show that it is the most adequate current procedure for testing low-level VaR series\, which corresponds to today’s regulatory standards.
URL:https://www.math.ttu.edu/mathematicalfinance/event/new-runs-based-approach-to-testing-value-at-risk-forecasts/
LOCATION:via Zoom
CATEGORIES:Fall 2025
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2025/07/Malecka.jpeg
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20251010T120000
DTEND;TZID=America/Chicago:20251010T130000
DTSTAMP:20260417T055722
CREATED:20250701T183111Z
LAST-MODIFIED:20250707T150143Z
UID:2015-1760097600-1760101200@www.math.ttu.edu
SUMMARY:Beyond Traditional Models: Assessing the Role of LSTM Networks in Volatility Prediction
DESCRIPTION:Speaker: Prof. Massimo Guidolin\, Baffi Carefin Center\, Bocconi Univ.\, Milan \nAbstract: This paper examines the out-of-sample accuracy of recurrent artificial neural networks (ANNs) compared to traditional econometric models for the prediction of realized volatility. We focus on a horserace between the heterogeneous autoregressive (HAR) model\, its Markov-switching extension (MS-HAR)\, multi-layer perceptrons (MLP)\, and long short-term memory (LSTM) networks across 31 international equity indices. Using high-frequency realized volatility data\, we evaluate predictive performance based on four loss functions and test for equal accuracy using Diebold-Mariano tests. Our results suggest that the HAR and MS-HAR models often deliver the most accurate forecasts\, outperforming LSTMs. However\, differences in out-of-sample accuracy between LSTM\, HAR\, and MS-HAR models are not always statistically significant. We conclude that ANNs do not consistently outperform HAR-based models in equity volatility forecasting.
URL:https://www.math.ttu.edu/mathematicalfinance/event/seminar-date-reserved/
LOCATION:via Zoom
CATEGORIES:Fall 2025
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2023/07/Guidolin-e1751900326458.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20251017T120000
DTEND;TZID=America/Chicago:20251017T130000
DTSTAMP:20260417T055722
CREATED:20250630T214748Z
LAST-MODIFIED:20250630T214748Z
UID:1998-1760702400-1760706000@www.math.ttu.edu
SUMMARY:Multi-hypothesis prediction for portfolio optimization: A structured ensemble learning approach to risk diversification
DESCRIPTION:Speaker: Dr. Alejandro Rodriguez Dominguez\, Director of Quantitative Analysis and Artificial Intelligence\, Miralta Finance Bank\, Madrid. \nAbstract: We introduce a novel framework for portfolio construction\, covering both selection and optimization\, based entirely on ensemble learning theory. A portfolio is modelled as an ensemble in a multi-hypothesis prediction setting\, with each constituent (base learner) focused on a specific hypothesis. This formulation connects the Bias–Variance–Diversity trade-off in ensemble models with out-of-sample portfolio performance. Diversity in the training of base learners translates to out-of-sample diversification\, allowing control over portfolio diversification via model training. \nPortfolio optimization is reframed as a supervised learning task\, where the target portfolio follows an ensemble combiner rule derived from Bregman divergences and ensemble model properties. When using Mean Squared Error\, the equal-weighted portfolio emerges as the target. Also\,it enables the use of alternative loss functions—treating the problem as classification or regression\, where targets follow ensemble combiner rules defined by Bregman divergences. A second source of out-of-sample diversification is also identified: the diversity in return predictions across base learners. Controlling this diversity at decision time further improves performance. When base learners exhibit similar error levels\, portfolios built from asset sets with more diverse return predictions\, but lower average sets forecasts\, outperform those with higher average sets predictions but less diversity. This defines a diversity-quality trade-off in predicted returns. Together\, these two sources of diversification expand the traditional boundaries of portfolio construction\, enabling meaningful diversification even within single-asset portfolios\, benefiting constrained strategies and redefining conventional notions of diversification. Theoretical results are empirically validated using full historical data from all S&P 500 constituents and a globally diversified set of 1\,300 bonds across multiple categories\, spanning over two decades and diverse market regimes. The diversity-quality trade-off is shown to be applicable across a wide range of portfolio optimization methods.
URL:https://www.math.ttu.edu/mathematicalfinance/event/multi-hypothesis-prediction-for-portfolio-optimization-a-structured-ensemble-learning-approach-to-risk-diversification/
LOCATION:via Zoom
CATEGORIES:Fall 2025
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2025/06/Rodriguez-e1751320394901.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20251024T120000
DTEND;TZID=America/Chicago:20251024T130000
DTSTAMP:20260417T055722
CREATED:20250630T160529Z
LAST-MODIFIED:20250630T160529Z
UID:1985-1761307200-1761310800@www.math.ttu.edu
SUMMARY:Option Pricing with a Compound CARMA(p\,q)-Hawkes
DESCRIPTION:Speaker: Prof. Lorenzo Mercuri\, Dept of Economics\, Management and Quantitative Methods\, Univ. of Milan \nAbstract: A self-exciting point process with a continuous-time autoregressive moving average intensity process\, named CARMA(p\,q)-Hawkes model\, has recently been introduced. The model generalizes the Hawkes process by substituting the Ornstein-Uhlenbeck intensity with a CARMA(p\,q) model where the associated state process is driven by the counting process itself. The proposed model preserves the same degree of tractability as the Hawkes process\, but it can reproduce more complex time-dependent structures observed in several market data. The paper presents a new model of asset price dynamics based on the CARMA(p\,q) Hawkes model. It is constructed using a compound version of it with a random jump size that is independent of both the counting and the intensity processes and can be employed as the main block for pure jump and (stochastic volatility) jump-diffusion processes. The numerical results for pricing European options illustrate that the new model can replicate the volatility smile observed in financial markets. Through an empirical analysis\, which is presented as a calibration exercise\, we highlight the role of higher order autoregressive and moving average parameters in pricing options.
URL:https://www.math.ttu.edu/mathematicalfinance/event/option-pricing-with-a-compound-carmapq-hawkes/
LOCATION:via Zoom
CATEGORIES:Fall 2025
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2025/06/Mercuri-e1751298511291.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20251031T120000
DTEND;TZID=America/Chicago:20251031T130000
DTSTAMP:20260417T055722
CREATED:20250707T130048Z
LAST-MODIFIED:20250707T142727Z
UID:2045-1761912000-1761915600@www.math.ttu.edu
SUMMARY:A general framework for pricing and hedging under local viability
DESCRIPTION:Speaker: Prof. Huy Chau\, Dept. Mathematics\, University of Manchester \nAbstract: In this paper\, a new approach for solving the problems of pricing and hedging derivatives is introduced in a general frictionless market setting. The method is applicable even in cases where an equivalent local martingale measure fails to exist. Our main results include a new superhedging duality for American options when wealth processes can be negative and trading strategies are subject to a cone constraint. This answers one of the questions raised by Fernholz\, Karatzas and Kardaras.
URL:https://www.math.ttu.edu/mathematicalfinance/event/seminar-date-reserved-5/
LOCATION:via Zoom
CATEGORIES:Fall 2025
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2025/07/Chau-scaled-e1751898311395.jpg
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