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Risk Econometrics
A Practical Guide to Bayesian and Frequentist Methods
1st Edition - June 1, 2022
Author: Elena Goldman
Paperback ISBN:
9 7 8 - 0 - 1 2 - 8 1 7 8 6 4 - 5
eBook ISBN:
9 7 8 - 0 - 1 2 - 8 1 7 8 6 5 - 2
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… Read more
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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. Sections dive deeply into the assumptions and pros and cons of statistical methods.
Based on R and Python, and accompanied by both exercises and research projects, this book 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 an emphasis on risk modeling using R and Python
- 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), and Measuring overall risk associated with a security relative to the market using MSCI Barra Factor Models
Upper level undergraduate and masters students, practitioners, and researchers in risk management, business analytics, and climate change science as well as economists and statisticians interested in applied work for various global risks
Chapter 1. Introduction to Risk Econometrics, Data and Software
1.1 Introduction
1.2 Types of Data
1.3 Data Sources
1.4 Introduction to R
1.5 Introduction to Python
Chapter 2. Review of Statistics, Frequentist and Bayesian Methods
2.1 Review of Probability and Statistics
2.1.1 Basic Probability Rules
2.1.2 Statistical Distribution
2.1.3 Moments
2.1.4 Testing Normality
2.1.5 Testing Sample Mean Hypotheses in One and Two Groups
2.2 Copulas
2.3 Likelihood Function and Maximum Likelihood Estimation
2.4 Generalized Method of Moments
2.5 Bayesian Methods, Prior and Posterior
2.6 Introduction to Markov Chain Monte Carlo (MCMC) Methods
Chapter 3. Financial Returns and Volatility
3.1 Definition of Financial Returns and Volatility
3.2. Basic Statistics for Returns
3.2.1 Testing for Excessive Extremes and Asymmetry
3.2.2 Testing for Autocorrelation and Efficient Market Hypothesis
3.2.3 Testing for Volatility Clustering
3.3 Asset Class Volatility in VLAB
Chapter 4. Linear Regression and Factor Models
4.1 Estimation of Linear Regression
4.2 Factor Models
4.1.1 CAPM
4.1.2 Fama-French Model
4.1.3 MSCI Barra
4.3 Seasonality
4.4 Multicollinearity
4.5 Heteroscedasticity
4.6 Principal Components Analysis
4.7 Event Studies
Chapter 5. Univariate Time Series Modeling and Forecasting
5.1 AR, MA, ARMA
5.2 Random Walks and Spurious Regression
5.3 Unit Root Tests
5.4 HAC Standard Errors
5.5 Long Memory ARFIMA Model
5.6 Model Choice using Information Criteria
5.7 Forecast Evaluation
Chapter 6. Univariate Volatility Models
6.1 Historical Moving Average Volatility
6.2 Exponentially Weighted Moving Average (EWMA)
6.3 GARCH Models with Extensions
6.3.1 GARCH with Normal distribution
6.3.2 GARCH with t distribution
6.3.3 Asymmetric GARCH Models
6.3.3.1 GJR-GARCH
6.3.3.2 Threshold GARCH
6.3.3.3 EGARCH
6.4 Stochastic Volatility
6.5 Options Implied Volatility
Chapter 7. Multivariate Time Series Modeling and Forecasting
7.1 Multivariate Volatility Modelling
7.1.1 Historical
7.1.2 EWMA
7.1.3 DCC-GARCH
7.2 Vector Autoregression Model
7.3 Cointegration and Vector Error Correction Model
7.4 Granger Causality
7.5 Dynamic Relationship between Global Financial Markets and Signals
Chapter 8. Downside Risk
8.1 Market Risk, VaR, ES
8.2 Historical Quantile
8.3 Volatility based Methods
8.4 Monte Carlo Simulations
8.5 Model Validation
8.5.1 Back-testing
8.5.2 Stress-testing
Chapter 9. Credit Risk
9.1 Credit and Bankruptcy Risk Models
9.2 Applications with Limited Dependent Variables Models
Chapter 10. Systemic Risk and Financial Stability
10.1 CoVaR
10.2 SRISK
10.3 Central Counterparty Margin Modeling
Chapter 11. Climate Risk and ESG Investment
11.1 Time Series Analysis for Climate Risk
11.2 Performance of ESG Funds
Chapter 12. High Frequency Data Analysis
12.1 Measures of Intraday Volatility
12.2 Algorithmic Trading
Chapter 13. Sate Space and Regime Switching Models
13.1 State Space Model
13.2 Markov-Switching Model
13.3 Threshold Autoregressive Model
Chapter 14. Corporate Financial Policies
14.1 Panel Data Methods
14.2 Heteroscedasticity Clustered Standard Errors
14.3 Application for Dividend Models
Chapter 15. Big Data and Machine Learning
15.1 Many Predictors and Dimension Reduction
15.2 Penalized Regression
15.2.1 Ridge Regression
15.2.2 Lasso
15.3 Neural Networks
Appendix 1. Common Probability Distributions
Appendix 2. Matrix Algebra
Appendix 3. R Codes
Appendix 4. Python Codes
- No. of pages: 250
- Language: English
- Edition: 1
- Published: June 1, 2022
- Imprint: Academic Press
- Paperback ISBN: 9780128178645
- eBook ISBN: 9780128178652
EG
Elena Goldman
Elena Goldman is an Associate Professor at Pace University, where she teaches courses in Financial Econometrics, International Finance and Financial Management. She has published in the Journal of Financial Research, Studies in Nonlinear Dynamics and Econometrics, Empirical Economics, Communications in Statistics, Journal of Trade and Global Markets, Economics Letters, Bayesian Statistics and its Applications volume among other, and she received her Ph.D. from Rutgers University.
Affiliations and expertise
Pace University, New York, NY, USA