LECTURES THEORY & APPLN MODERN FINANCE WITH R & CHATGPT

These lecture notes are thought for Master courses in Finance, Fintech and Quantitative Finance programmes. We fully subscribe to the philosophy that post-graduate students should be offered courses that are really at the cutting edge of the technologies and advances that are disrupting the financial industry and delve deep into topics such as A.I., machine learning, and their importance for Asset Management. In these notes, the illustration of the theory of Finance is paired with practical applications to real-life asset allocation problems. A hands-on approach is proposed to construct and manipulate databases to build portfolios, assess their performance and manage their risk. The course begins with a section on the fundamentals on individual choice to market valuation, covering the traditional Markowitz mean-variance approach, market-based asset pricing and Arbitrage-based pricing theory. Empirical modelling in finance is then introduced by illustrating its working and its historical evolution. The translation of financial theory into action on data is driven by building predictive models for asset prices and returns. Basic models are explored, and programming emerges as an essential prerequisite for data manipulation. Readers can acquaint themselves with the statistical software R and exhibit the application of theoretical concepts to financial data, illustrated by sample programs, exercises, and corresponding solutions. Contents: Individual Choice: Different Paradigms for Decision Theory A Paradigm for Rationality Decision Theory Under Uncertainty Risk Aversion Insurance Pricing Principle The Portfolio Problem Subjective Valuation Expected Utility Reloaded Deviations from Expected Utility From Individual Choice to Market Pricing: The Markowitz Mean–Variance Approach Market-Based Asset Pricing A Structural Interpretation Arbitrage-Based Pricing Theory APT and Risk-Neutral Valuation Empirical Tests of Asset Pricing Models: The View from the 1960s: Efficient Markets and Constant Expected Returns The Cross-Sectional Evidence: CAPM Verification The Frazzini and Pedersen (2014) BAB Factor Construction Empirical Tests of APT, Anomalies and Fama and French (1993) Direct Construction of Traded Portfolios Time-Series Analysis of Returns Time Series Anomalies Returns at Different Horizons and the Dynamic Dividend Growth Model of Shiller (1981) Conditional Asset Pricing with Predictable Returns Predictive Models in Finance From Theory to Practice: The Econometric Modelling Process The Challenges for Financial Econometrics Returns Stock and Bond Returns Going to the Data with R Appendix: The Data The Constant Expected Return Model: Model Specification Model Estimation Model Simulation The CER Model at Work with R Factor Models: Time-Series Representation Cross-Sectional Representation Factor-Based Portfolios and Factor Exposures Asset Allocation with the CER and the CAPM in R Validating Factor Models Factor Models with Predictability Models for Risk Measurement: Risk Measurement VaR without Predictability The Evidence from High-Frequency Data A General Model for High-Frequency Data Estimation of GARCH Models From GARCH to VaR Measuring Risk: An Illustration with R Backtesting VaR Readership: Researchers, graduates, undergraduate students of economics, business management and finance. Carlo Favero holds a PhD from Oxford University, where he was a member of the Oxford Econometrics Research Centre. He has been a professor of Econometrics at Bocconi University from 1994 to 2001 and professor of Economics since 2002. In 2009, he joined the newly formed Department of Finance at Bocconi University, where he taught Econometrics. Since 2023, he holds a dual affiliation with the Department of Economics and the Department of Finance. He has published in scholarly journals on the econometric modelling of bond and stock prices, applied econometrics, monetary and fiscal policy and time-series models for macroeconomics and finance. He is a...