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Risk Econometrics - 1st Edition - ISBN: 9780128178645

Risk Econometrics

1st Edition

A Practical Guide to Bayesian and Frequentist Methods

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Author: Elena Goldman
Paperback ISBN: 9780128178645
Imprint: Academic Press
Published Date: 1st December 2021
Page Count: 250
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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.

Key Features

  • 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

Table of Contents

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 GJR-GARCH Threshold GARCH 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


7.2 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


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© Academic Press 2021
1st December 2021
Academic Press
Paperback ISBN:

About the Author

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

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