Causality in Finance

From Theory to Practice
1. Auflage 2027
ISBN: 978-1-032-93693-2
Verlag: Taylor & Francis

Buch, Englisch, 400 Seiten, Format (B × H): 156 mm x 234 mm

Reihe: Chapman and Hall/CRC Financial Mathematics Series

Causality in Finance: From Theory to Practice offers a comprehensive exploration of how causal relationships influence market dynamics, risk management strategies, and investment decisions. It is designed for an academic audience at the graduate level, including finance professionals, researchers, and students seeking an in-depth understanding of causal inference and discovery concepts, techniques, and their applications in finance.

Features

- Comprehensive Coverage: A thorough exploration of fundamental concepts of causality, from correlation versus causation to advanced causal inference methodologies

- Real-World Finance Applications: Practical examples and case studies illustrating how causality influences financial decision-making and market dynamics

- Python Code: Included as practical examples and case studies are covered

- Methodological Insights: Detailed explanations of causal discovery algorithms, causal inference techniques and financial market theories

- Cutting-Edge Techniques: Integration of modern approaches such as machine learning in causal inference, Large Language Models for causal discovery and their applications in financial analysis.

Zielgruppe

Postgraduate, Professional Practice & Development, and Professional Reference

Autoren/Hrsg.

Korbosli, Leila

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Wirtschaftsstatistik, Demographie
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
• Wirtschaftswissenschaften Finanzsektor & Finanzdienstleistungen Finanzsektor & Finanzdienstleistungen: Allgemeines

Inhaltsverzeichnis

1. Introduction to Causality 2. Introduction to Causal Inference and Methods 3. Introduction to Causal Discovery and Methods 4. Causality Techniques in Quantitative Investment Management 5. Causal Inference in Market Microstructure 6. Stress Testing with Causal Models 7. Causal Event Attribution in Climate Finance 8. Causality in Machine Learning Techniques for Finance

Über Autor(innen)

Leila Korbosli is a quantitative finance professional with extensive experience in derivatives, cross-asset modelling, and quantitative research and trading. Since beginning her career at Lehman Brothers in 2007, she has worked across Rates and FX, Credit, and XVA, combining quantitative modelling expertise with practical experience in financial markets. Her background spans the development of mathematical models and analytical tools for complex financial problems, providing a practitioner’s perspective on the application of quantitative methods to finance.

Leila is actively involved in the quantitative finance community. She served as an Advisory Board member of the QuantMinds conference in 2023 and 2024 and spoke at QuantMinds in 2022 and 2023, the WBS Quant Conference in Valencia in 2023, and Quant Strats in London in 2023.

She holds an engineering degree in Applied Mathematics and Computer Science from ENSIMAG (2006) and a Master’s degree in Probability and Finance from Paris VI and École Polytechnique (2007). She is the co-author of Global Derivatives: Products, Theory and Practice.