Risk Econometrics

Risk Econometrics: A Practical Guide to Bayesian and Frequentist Methods serves as a guide to mastering a growing number of applications in network analysis, environmental science, and healthcare. By avoiding a focus either on time series or cross-sectional/panel data methods and adopting either Frequentist (Classical) or Bayesian approaches, it trains readers to recognize the most important aspects of applied Frequentist and Bayesian statistics, emphasizing methods, insights, and popular advances widely used during the last ten years. Risk Econometrics dives deeply into the assumptions and the pros and cons of statistical methods. Based on R and Python, and accompanied by both exercises and research projects, it reinforces a balance between theory and practice that other books, wedded to only one statistical method, cannot match.

Combines Frequentist and Bayesian methods in time series, cross sectional, and panel data settings with emphasis in risk modelling using R and PythonProvides practical guidelines to questions such as knowing when to use various model selection criteria, how to backtest models, and how to spot limitations of various estimation and testing methods Includes exercises and applications in new industry projects such as: Risk and return of environmental funds; Systemic risk measures using Bayesian and Frequentist methods; Initial margin setting for Central Clearing Counterparties (CCPs); Measuring overall risk associated with a security relative to the market using MSCI Barra Factor Models