UP1-PROG-02-MIB50A-119 - Master 2 indifférencié Finance technology data
This course will bring students quantitative skills to be deployed at Fintechs, traditional financial entities and/or regulators.
Reviewing recent advances in econometric theory and economic modelling, we will explore how they still apply for most financial applications. We will also discuss their benefits in terms of economic narratives and sensitivity analyses.
Students will be asked to gather data from traditional financial data sources (e.g. Central Banks and other regulators, international institutions providing statistics, stock exchange platforms and private data providers) as well as alternative sources.
Reviewing and building on traditional econometric theory, advanced models will be replicated on alternative data sets and their power discussed. Time series and corporate finance will be used to develop trading strategies and test asset pricing models, extreme value theory will be detailed to review risk modelling. We will approach profitability and default probability with panel and GMM approaches.
No prior knowledge of programming language is necessary.
Course prerequisites: undergrad econometrics and statistics, undergrad mathematics and matrix algebra
Plan
(3 hours) Review of python and R for quantitative methods.
(3 hours) Empirical data import and cleaning (seasonality …).
(3 hours) Traditional econometrics on financial time series.
(3 hours) Multiple regression and test.
(3 hours) Copula and cointegration to develop trading strategies.
(3 hours) Extreme Value Theory and risk measures.
(6 hours) Panel data, GMM.
(3 hours) Asset price model and tests.
(3 hours) Systemic risk measures and their limits.
(3 hours) Macroeconomic data and stress test scenario.
(3 hours) Network study and risk contagion.
Main references:
Marno Verbeek, A guide to modern econometrics, Wiley (2004).
Carol
Alexander, Market
risk analysis, Weiley
(2009).
John Y. Campbell and Robert J. Shiller, Cointegration and tests of present value models, Journal of Political Economy (1987).
John Y. Campbell and Robert J. Shiller, Stock prices, earnings, and expected dividends, Journal of Finance (1988).
Robert J. Shiller, Irrational exuberance, Princeton University Press (2000).
Gunter Loeffler and Peter Raupach, Pitfalls in the Use of Systemic Risk Measures, Journal of Financial and Quantitative Analysis (2018).
Jon Danielsson and Chen Zhou, Why risk is so hard to measure, DnB working paper (2016).
Benoit
Carmichael and Alain Coen , Real
estate as a common risk factor in bank stock returns,
Journal of Banking and Finance (2018).
Francisco
Barillas and Jay Shanken, Comparing
Asset Pricing Models,
Journal of Finance (2018).
Informations sur l'espace de cours
| Nom | Archive année [2019-2020] Master 2 indifférencié Finance technology data - Quantitative Methods in Finance |
| Nom abrégé | [2019-2020] UP1-PROG-ETP-MIB50A-119-02 - Quantitative Methods in Finance |
| Groupes utilisateurs inscrits | Consultation des ressources, participation aux activités :
|
Rattachements à l'offre de formation
| Élément pédagogique | UP1-PROG-ETP-MIB50A-119 - |
| Chemin complet | > Année 2023-2024 > Paris 1 > École d'économie de la Sorbonne > Master 2 indifférencié Finance technology data > UP1-PROG-ETP-MIB50A-119 Référence cassée |