‘Macroeconometric Models for Portfolio Management’ begins by outlining a portfolio management framework into which macroeconometric models and backtesting investment strategies are integrated. It is followed by a discussion on the theoretical backgrounds of both small and global large macroeconometric models, including data selection, estimation, and applications. Other practical concerns essential to managing a portfolio with decisions driven by macro models are also covered: model validation, forecast combination, and evaluation. The author then focuses on applying these models and their results on managing the portfolio, including making trading rules and asset allocation across different assets and risk management. The book finishes by showing portfolio examples where different investment strategies are used and illustrate how the framework can be applied from the beginning of collecting data, model estimation, and generating forecasts to how to manage portfolios accordingly.
This book aims to bridge the gap between academia and practising professionals. Readers will attain a rigorous understanding of the theory and how to apply these models to their portfolios. Therefore, ‘Macroeconometric Models for Portfolio Management’ will be of interest to academics and scholars working in macroeconomics and finance; to industry professionals working in financial economics and asset management; to asset managers and investors who prefer systematic investing over discretionary investing; and to investors who have a strong interest in macroeconomic influences on their portfolio.
List of acronyms
List of Figures and Tables
Part I: Overall Framework and Financial Theories
Chapter 1 Introduction
Chapter 2 Portfolio Theory and CAPM
Chapter 3 Asset Return Predictability
Part II: Macroeconometric Models
Chapter 4 Historical Perspectives on Macroeconometrics
Chapter 5 Methodology of Macroeconometric Models - Small Models
Chapter 6 Methodology of Modelling Volatility - GARCH Models
Chapter 7 Methodology of Macroeconometric Models - Global Large Models
Chapter 8 GVAR Model Validation and Forecasting
Chapter 9 Testing Forecasting Ability of GVAR Model
Part III: Portfolio Management and Backtesting
Chapter 10 Portfolio Management: Forecasts, Risk and Position Size
Chapter 11 Trading Rules and Backtesting
Chapter 12 Backtesting with the Excel Workbook
Chapter 13 Example 1 - Backtesting Strategy - Single Position Long-Short
Chapter 14 Example 2 - Backtesting Strategy - Multiple Positions Long-Short
Jeremy Kwok is a doctoral researcher at University of Westminster, fully funded for investigating the application of the Global Autoregressive (GVAR) Models in economics and finance. His main interests are in macroeconomic modelling, forecasting, and quantitative methods for investing and portfolio management. Before this project, he worked in central banking and trading technology in the City of London; he has also worked in oil and gas and civil engineering. He holds a BSc in Civil Engineering Surveying (UEL), an MSc in Hydrographic Surveying (UCL) and an MSc in Economics (Greenwich). He is a member of the Royal Economic Society and fellow of the Royal Society of Arts.
Econometric models , forecasting models, global economy models, asset allocation, portfolio management, systematic trading, quantitative investing, DSGE models, GVAR models, large scale models, time-series models