Del i serien Routledge Advances in Risk Management

Candlestick Forecasting for Investments

Applications, Models and Properties

Häftad, Engelska, 2022

AvHaibin Xie,Kuikui Fan,Shouyang Wang

809 kr

Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt för medlemmar vid köp för minst 249 kr.

Finns i fler format (1)

Inbunden

2 759 kr

Skickas inom 10-15 vardagar

Candlestick charts are often used in speculative markets to describe and forecast asset price movements. This book is the first of its kind to investigate candlestick charts and their statistical properties. It provides an empirical evaluation of candlestick forecasting. The book proposes a novel technique to obtain the statistical properties of candlestick charts. The technique, which is known as the range decomposition technique, shows how security price is approximately logged into two ranges, i.e. technical range and Parkinson range.Through decomposition-based modeling techniques and empirical datasets, the book investigates the power of, and establishes the statistical foundation of, candlestick forecasting.

Produktinformation

Utgivningsdatum2022-09-26
Mått156 x 234 x 13 mm
Vikt140 g
FormatHäftad
SpråkEngelska
SerieRoutledge Advances in Risk Management
Antal sidor116
FörlagTaylor & Francis Ltd
ISBN9780367703394

Tillhör följande kategorier

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 7.5 Summary 8. Technical range forecasting 8.1 Introduction 8.2 Econometric methods 8.3 An empirical study 8.4 Summary 9. Technical range spillover 9.1 Introduction 9.2 Econometric method 9.3 An empirical study: DAX and CAC40 9.4 Summary 10. Stock return forecasting: U.S. S&P500 10.1 Introduction 10.2 Econometric methods 10.3 Statistical evidence 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 11.3 Empirical results 11.4 Summary PART V CONCLUSIONS AND FUTURE STUDIES 12. Main conclusions 13. Future studies