Degiannakis / Floros Modelling and Forecasting High Frequency Financial Data

1. Auflage 2015
ISBN: 978-1-137-39649-5
Verlag: Palgrave Macmillan UK
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 301 Seiten

The global financial crisis has reopened discussion surrounding the use of appropriate theoretical financial frameworks to reflect the current economic climate. There is a need for more sophisticated analytical concepts which take into account current quantitative changes and unprecedented turbulence in the financial markets. This book provides a comprehensive guide to the quantitative analysis of high frequency financial data in the light of current events and contemporary issues, using the latest empirical research and theory. It highlights and explains the shortcomings of theoretical frameworks and provides an explanation of high-frequency theory, emphasising ways in which to critically apply this knowledge within a financial context. Modelling and Forecasting High Frequency Financial Data combines traditional and updated theories and applies them to real-world financial market situations. It will be a valuable and accessible resource for anyone wishing to understand quantitative analysis and modelling in current financial markets.

Dr. Christos Floros (Crete, Greece) is Professor of Finance at the Technological Educational Institute of Crete and Hellenic Open University (Greece). His main research interests include behavioural finance, financial derivatives (futures and options markets), financial econometrics (forecasting realized volatility, VaR modelling) and empirical banking (efficiency, competition and profitability). He has published extensively in academic journals and is the Editor-in-Chief of the International Journal of Financial Markets and Derivatives (IJFMD) and Editor of the International Journal of Computational Economics and Econometrics (IJCEE). He has been involved in a number of research funded projects including a Marie Curie project on 'Volatility forecasting evaluation based on loss function with well-defined multivariate distributional form and ultra-high frequency datasets (as co-ordinator). Christos has presented several papers at international academic conferences in the UK, Greece, Portugal, Italy, France, Ireland, and Spain, and is a Fellow of the Higher Education Academy (UK). Dr. Stavros Degiannakis is Assistant Professor in the Department of Economic and Regional Development of Panteion University of Social and Political Sciences. He has taught at various Universities including the Athens University of Economics and Business and the Hellenic Open University, Greece, in subjects such as statistics, econometrics, time series, data analysis and quantitative techniques. He has also served as econometrician for companies in the private and public sector (the Bank of Greece, the University of Portsmouth, the Economic Chamber of Greece, Inventive, the Hellenic Parliament, and Profile). He has served as a referee in more than 30 international journals, such as the Journal of Applied Econometrics, the Journal of Banking and Finance, and the Journal of Applied Statistics. His research interests are in the areas of applied and theoretical financial econometrics (ultra-high frequency data analysis, macro-finance modelling, option pricing, risk modelling) and statistics (marketing metrics, multivariate distributions, forecasting ability, time series analysis). Dr. Stavros Degiannakis received his PhD in Statistics from Athens University of Economics and Business. He graduated from the Athens University of Economics and Business, where he completed his studies in Statistics, and holds a M.Sc. degree in Econometrics from the University of Essex.

Autoren/Hrsg.

Degiannakis, Stavros

Floros, Christos

Inhaltsverzeichnis

1;Cover;1
2;Half-Title;2
3;Title;4
4;Copyright;5
5;Dedication;6
6;Contents;8
7;List of Figures;12
8;List of Tables;15
9;Acknowledgments;18
10;List of Symbols and Operators;19
11;1 Introduction to High Frequency Financial Modelling;24
11.1;1 The role of high frequency trading;25
11.2;2 Modelling volatility;33
11.3;3 Realized volatility;34
11.4;4 Volatility forecasting using high frequency data;37
11.5;5 Volatility evidence;37
11.6;6 Market microstructure;38
12;2 Intraday Realized Volatility Measures;47
12.1;1 The theoretical framework behind the realized volatility;47
12.2;2 Theory of ultra-high frequency volatility modelling;50
12.3;3 Equidistant price observations;54
12.3.1;3.1 Linear interpolation method;54
12.3.2;3.2 Previous tick method;55
12.4;4 Methods of measuring realized volatility;55
12.4.1;4.1 Conditional – inter-day –Variance;55
12.4.2;4.2 Realized variance;57
12.4.3;4.3 Price range;58
12.4.4;4.4 Model-based duration.;60
12.4.5;4.5 Multiple grids;60
12.4.6;4.6 Scaled realized range;60
12.4.7;4.7 Price jumps;60
12.4.8;4.8 Microstructure frictions;60
12.4.9;4.9 Autocorrelation of intraday returns;61
12.4.10;4.10 Interday adjustments;61
12.5;5 Simulating the realized volatility;65
12.6;6 Optimal sampling frequency;70
13;3 Methods of Volatility Estimation and Forecasting;81
13.1;1 Daily volatilitymodels – review;81
13.1.1;1.1 ARCH(q)model;82
13.1.2;1.2 GARCH(p,q)model;82
13.1.3;1.3 APARCH(p,q)model;83
13.1.4;1.4 FIGARCH(p,d,q)model;83
13.1.5;1.5 FIAPARCH(p,d,q)model;83
13.1.6;1.6 Other methods of interday volatility modelling;84
13.2;2 Intraday volatility models: review;84
13.2.1;2.1 ARFIMA(k,d', l)model;84
13.2.2;2.2 ARFIMA(k,d', l)- GARCH(p,q)model;85
13.2.3;2.3 HAR-RVmodel;85
13.2.4;2.4 HAR-sqRVmodel;86
13.2.5;2.5 HAR-GARCH(p,q)model;86
13.2.6;2.6 Other methods of intraday volatility modelling;87
13.3;3 Volatility forecasting;87
13.3.1;3.1 One-step-ahead volatility forecasting: Interday volatility models;87
13.3.2;3.2 Daily volatility models: program construction;90
13.3.3;3.3 One-step-ahead volatility forecasting: intraday volatility models;90
13.3.4;3.4 Intraday volatility models: program construction;93
13.4;4 The construction of loss functions;93
13.4.1;4.1 Evaluation or loss functions;93
13.4.2;4.2 Information criteria;95
13.4.3;4.3 Loss functions depend on the aim of a specific application;96
14;4 Multiple Model Comparison and Hypothesis Framework Construction;133
14.1;1 Statistical methods of comparing the forecasting ability of models;133
14.1.1;1.1 Diebold and Mariano test of equal forecast accuracy;134
14.1.2;1.2 Reality check for data snooping;134
14.1.3;1.3 Superior Predictive Ability test;135
14.1.4;1.4 SPEC model selection method;135
14.2;2 Theoretical framework: distribution functions;136
14.3;3 A framework to compare the predictive ability of two competing models;138
14.4;4 A framework to compare the predictive ability of n competing models;142
14.4.1;4.1 Generic model;142
14.4.2;4.2 Regression model;144
14.4.3;4.3 Regression model with time varying conditional variance;144
14.4.4;4.4 Fractionally integrated ARMA model with time varying conditional variance;145
14.5;5 Intraday realized volatility application.;146
14.6;6 Simulate the SPEC criterion;151
14.6.1;6.1 ARMA(1,0) simulation;151
14.6.2;6.2 Repeat the simulation;153
14.6.3;6.3 Intraday simulated process;156
15;5 Realized Volatility Forecasting: Applications;184
15.1;1 Measuring realized volatility;184
15.1.1;1.1 Volatility signature plot;185
15.1.2;1.2 Interday adjustment of the realized volatility;188
15.1.3;1.3 Distributional properties of realized volatility;197
15.2;2 Forecasting realized volatility;199
15.3;3 Programs construction;201
15.4;4 Realized volatility forecasts comparison: SPEC criterion;213
15.5;5 Logarithmic realized volatility forecasts comparison: SPA and DM Tests;223
15.5.1;5.1 SPA test;223
15.5.2;5.2 DM test;225
16;6 Recent Methods: A Review;240
16.1;1 Modelling jumps;240
16.1.1;1.1 Jump volatility measure and jump tests;241
16.1.2;1.2 Daily jump tests;242
16.1.3;1.3 Intraday jump tests;243
16.1.4;1.4 Using OxMetrics (Re@lized under G@RCH 6.1);244
16.2;2 The RealGARCH model;253
16.2.1;2.1 Realized GARCH forecasting;255
16.2.2;2.2 Leverage effect;257
16.2.3;2.3 Realized EGARCH;257
16.3;3 Volatility forecasting with HAR-RV-J and HEAVY models;258
16.3.1;3.1 The HAR-RV-J model;258
16.3.2;3.2 The HEAVY model;259
16.4;4 Financial risk measurements;261
16.4.1;4.1 The method;261
17;7 Intraday Hedge Ratios and Option Pricing;266
17.1;1 Introduction to intraday hedge ratios;266
17.2;2 Definition of hedge ratios;269
17.2.1;2.1 BEKKmodel;271
17.2.2;2.2 Asymmetric BEKKmodel;271
17.2.3;2.3 Constant Conditional Correlation (CCC) model;272
17.2.4;2.4 DynamicConditionalCorrelation (DCC)model;273
17.2.5;2.5 Estimation of themodels;274
17.3;3 Data;274
17.4;4 Estimated hedge ratios;276
17.5;5 Hedging effectiveness;279
17.6;6 Other models for intraday hedge ratios;282
17.7;7 Introduction to intraday option pricings;282
17.8;8 Price movement models;283
17.8.1;8.1 The approach of Merton;284
17.8.2;8.2 The approach of Scalas and Politi;284
17.8.3;8.3 Relation between the distributions of the epochs and durations;285
17.8.4;8.4 Price movement;286
17.9;9 Option pricing;288
17.9.1;9.1 The approach ofMerton;288
17.9.2;9.2 The approach of Scalas and Politi;288
17.9.3;9.3 Time t is an epoch;289
17.9.4;9.4 Time t is not an epoch;290
17.9.5;9.5 Other models for intraday option pricing;292
18;Index;297