Produktbild: Handbook of High-Frequency Tra

Handbook of High-Frequency Tra

189,99 €

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

15.04.2016

Herausgeber

Ionut Florescu + weitere

Verlag

John Wiley & Sons

Seitenzahl

456

Maße (L/B/H)

24/16,1/2,9 cm

Gewicht

844 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-118-44398-9

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

15.04.2016

Herausgeber

Verlag

John Wiley & Sons

Seitenzahl

456

Maße (L/B/H)

24/16,1/2,9 cm

Gewicht

844 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-118-44398-9

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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  • Produktbild: Handbook of High-Frequency Tra
  • Notes on Contributors xiii
     
    Preface xv
     
    1 Trends and Trades 1
    Michael Carlisle, Olympia Hadjiliadis, and Ioannis Stamos
     
    1.1 Introduction 1
     
    1.2 A trend-based trading strategy 3
     
    1.2.1 Signaling and trends 3
     
    1.2.2 Gain over a subperiod 5
     
    1.3 CUSUM timing 7
     
    1.3.1 Cusum process and stopping time 7
     
    1.3.2 A CUSUM timing scheme 10
     
    1.3.3 US treasury notes, CUSUM timing 11
     
    1.4 Example: Random walk on ticks 12
     
    1.4.1 Random walk expected gain over a subperiod 15
     
    1.4.2 Simple random walk, CUSUM timing 18
     
    1.4.3 Lazy simple random walk, cusum timing 21
     
    1.5 CUSUM strategy Monte Carlo 24
     
    1.6 The effect of the threshold parameter 27
     
    1.7 Conclusions and future work 39
     
    Appendix: Tables 40
     
    References 47
     
    2 Gaussian Inequalities and Tranche Sensitivities 51
    Claas Becker and Ambar N. Sengupta
     
    2.1 Introduction 51
     
    2.2 The tranche loss function 52
     
    2.3 A sensitivity identity 54
     
    2.4 Correlation sensitivities 55
     
    Acknowledgment 58
     
    References 58
     
    3 A Nonlinear Lead Lag Dependence Analysis of Energy Futures: Oil, Coal, and Natural Gas 61
    Germán G. Creamer and Bernardo Creamer
     
    3.1 Introduction 61
     
    3.1.1 Causality analysis 62
     
    3.2 Data 64
     
    3.3 Estimation techniques 64
     
    3.4 Results 65
     
    3.5 Discussion 67
     
    3.6 Conclusions 69
     
    Acknowledgments 69
     
    References 70
     
    4 Portfolio Optimization: Applications in Quantum Computing 73
    Michael Marzec
     
    4.1 Introduction 73
     
    4.2 Background 75
     
    4.2.1 Portfolios and optimization 76
     
    4.2.2 Algorithmic complexity 77
     
    4.2.3 Performance 78
     
    4.2.4 Ising model 79
     
    4.2.5 Adiabatic quantum computing 79
     
    4.3 The models 80
     
    4.3.1 Financial model 81
     
    4.3.2 Graph-theoretic combinatorial optimization models 82
     
    4.3.3 Ising and Qubo models 83
     
    4.3.4 Mixed models 84
     
    4.4 Methods 84
     
    4.4.1 Model implementation 85
     
    4.4.2 Input data 85
     
    4.4.3 Mean-variance calculations 85
     
    4.4.4 Implementing the risk measure 86
     
    4.4.5 Implementation mapping 86
     
    4.5 Results 88
     
    4.5.1 The simple correlation model 88
     
    4.5.2 The restricted minimum-risk model 91
     
    4.5.3 The WMIS minimum-risk, max return model 94
     
    4.6 Discussion 95
     
    4.6.1 Hardware limitations 97
     
    4.6.2 Model limitations 97
     
    4.6.3 Implementation limitations 98
     
    4.6.4 Future research 98
     
    4.7 Conclusion 100
     
    Acknowledgments 100
     
    Appendix 4.A: WMIS Matlab Code 100
     
    References 103
     
    5 Estimation Procedure for Regime Switching Stochastic Volatility Model and Its Applications 107
    Ionut Florescu and Forrest Levin
     
    5.1 Introduction 107
     
    5.1.1 The original motivation 108
     
    5.1.2 The model and the problem 108
     
    5.1.3 A brief historical note 109
     
    5.2 The methodology 110
     
    5.2.1 Obtaining filtered empirical distributions at t1,..., tT 110
     
    5.2.2 Obtaining the parameters of the Markov chain 112
     
    5.3 Results obtained applying the model to real data 113
     
    5.3.1 Part i: financial applications 113
     
    5.3.2 Part ii: physical data application. temperature data 119
     
    5.3.3 Part iii: analysis of seismometer readings during an earthquake 121
     
    5.3.4 Analysis of the earthquake signal: beginning 123
     
    5.3.5 Analysis: during the earthquak