Candlestick Forecasting for Investments: Applications, Models and Properties

Cover
Half Title
Series Page
Title Page
Copyright Page
Contents
List of figures
List of tables
About the authors
Acknowledgements
Preface
Part I Introduction and outline
1 Introduction
1.1 Technical analysis before the 1970s
1.2 Technical analysis during 1990s–2000s
1.3 Recent advances in technical analysis
1.4 Summary
2 Outline of this book
Part II Candlestick
3 Basic concepts
4 Statistical properties
4.1 Propositions
4.2 Simulations
4.3 Empirical evidence
4.4 Summary
Part III Statistical models
5 DVAR model
5.1 The model
5.2 Statistical foundation
5.3 Simulations
5.4 Empirical results
5.5 Summary
6 Shadows in DVAR
6.1 Simulations
6.2 Theoretical explanation
6.3 Empirical evidence
6.4 Summary
Part IV Applications
7 Market volatility timing
7.1 Introduction
7.2 GARCH@CARR model
7.3 Economic value of volatility timing
7.4 Empirical results
7.4.1 The data
7.4.2 In-sample volatility timing
7.4.3 Out-of-sample volatility timing
7.5 Summary
8 Technical range forecasting
8.1 Introduction
8.2 Econometric methods
8.2.1 The model
8.2.2 Out-of-sample forecast evaluation
8.3 An empirical study
8.3.1 The data
8.3.2 In-sample estimation
8.3.3 Out-of-sample forecast
8.4 Summary
9 Technical range spillover
9.1 Introduction
9.2 Econometric method
9.3 An empirical study: DAX and CAC40
9.3.1 The data
9.3.2 Estimation
9.4 Summary
10 Stock return forecasting: U.S. S&P500
10.1 Introduction
10.2 Econometric methods
10.2.1 The model
10.2.2 Out-of-sample evaluation
10.3 Statistical evidence
10.3.1 The data
10.3.2 In-sample estimation
10.3.3 Out-of-sample forecast
10.4 Economic evidence
10.5 More details
10.6 Summary
11 Oil price forecasting: WTI crude oil
11.1 Introduction
11.2 Econometric method
11.2.1 DVAR model
11.2.2 Forecast evaluation
11.3 Empirical results
11.3.1 The data
11.3.2 In-sample model estimation
11.3.3 Out-of-sample performance
11.4 Summary
Part V Conclusions and future studies
12 Main conclusions
13 Future studies
Bibliography
Index