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Úvodní seminář
- Autor:
- Miloš Kopa
- Datum:
- 06.03.2025
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Seminář se nekoná
- Autor:
- Seminář se nekoná
- Datum:
- 13.03.2025
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Optimality and Fairness: application to reimbursement of COVID-19 vaccines
- Autor:
- Takaki Hayashi
- Datum:
- 20.03.2025
- Abstract:
- We intend to explore low latency, lead-lag relationships between stock prices using tick data. A salient feature of tick data is its irregularity of intervals between observation times, stemming from records being timestamped at the exact moments when trades or orders occur. A natural way to handle with is to model tick data as discrete observations from continuous-time stochastic processes. A pioneering work is done by Hoffmann, Rosenbaum and Yoshida (2013), who propose a method to estimate the unknown lead-lag times based cross-correlations in a semimartigale framework. Alternatively, Dobrev and Schaumburg (2016) propose a method based on counts of co-arrival times of point processes, which can capture low latency, cross-market activities by high-freqeuncy traders. In this talk, we briefly review these approaches, followed by our stochastic model and wavelet-based estimation method for lead-lag times. It is specifically designed to capture multiple lead-lag relationships that may coexist within two time series. We present two empirical analyses conducted using those approaches. First, we investigate lead-lag relationships between quotes on the NASDAQ and BATS exchanges for each stock of the NASDAQ 100 Index. Second, we investigate lead-lag relationships between quotes of Nikkei 225 index-linked ETFs on the Tokyo Stock Exchange (TSE) and examine whether/how the ETF market-making system introduced by TSE in 2018 and 2019, as well as the system upgrade of its *arrowhead* (the trading system for cash products) implemented in November 2019, have influenced lead-lag relationships among those ETFs. Our approach is shown to have capacity to capture complex nature of these lead-lag relationships in a signle model.
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Uncertainty in real-world vehicle routing
- Autor:
- Václav Sobotka
- Datum:
- 27.03.2025
- Abstrakt:
- The motivation for our research arises from the limitations of traditional deterministic heuristic solvers for vehicle routing problems (VRP) observed in industrial practice. In general, the quantities provided to the solvers as inputs, e.g., loads or service times, are typically estimates or simplifying reflections of reality. Moreover, the input information may be incomplete as new customers may request service while the vehicles are already on the road. Current state-of-the-art solvers are applicable to complex VRP variants at scale. However, their inability to reason about such uncertainties limits their usefulness in real-world applications. Despite stochastic VRPs being a widely studied topic, related approaches are typically centered around the uncertainty in the problem rather than extending successful and generic deterministic methods. Moreover, uncertainty-related methodologies and models are often strongly linked to computationally expensive sampling or exact algorithms making their scaling problematic. In this talk, we will discuss how to naturally extend the state-of-the-art methods to account for uncertainties without sacrificing their crucial qualities - generality and scalability.
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TBA
- Autor:
- Martin Branda
- Datum:
- 03.04.2025
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TBA
- Autor:
- Jana Junová
- Datum:
- 10.04.2025
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Seminář se nekoná
- Autor:
- Seminář se nekoná
- Datum:
- 17.04.2025
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TBA
- Autor:
- Monika Matoušková
- Datum:
- 24.04.2025
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Seminář se nekoná
- Autor:
- Seminář se nekoná
- Datum:
- 01.05.2025
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Seminář se nekoná
- Autor:
- Seminář se nekoná
- Datum:
- 08.05.2025
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TBA
- Autor:
- Jamie Fairbrother
- Datum:
- 15.05.2025
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TBA
- Autor:
- TBA
- Datum:
- 22.05.2025