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Dacorogna M.M., Gencay R., Mueller U.A. An Introduction to High-Frequency Finance (2001)Dacorogna M.M., Gencay R., Mueller U.A. 
CONTENTS LIST OF FIGURES xv LIST OF TABLES xix PREFACE xxi ACKNOWLEDGMENTS xxiii I INTRODUCTION I.+ Markets: The Source of High-Frequency Data 1 1.2 Methodology of High-Frequency Research 2 1.3 Data Frequency and Market Information 3 1.4 New Levels of Significance 6 1.5 Interrelating Different Time Scales 8 2 MARKETS AND DATA 2.1 General Remarks on Markets and Data Types 10 2.1.1 Spot Markets 11 2.1.2 Futures Markets 12 2.1.3 Option Markets 13 2.2 Foreign Exchange Markets 13 2.2.1 Structure of the Foreign Exchange Spot Market 15 2.2.2 Synthetic Cross Rates 19 2.2.3 Multiple Contributor Effects 19 2.3 Over-the-Counter Interest Rate Markets 20 2.3.1 Spot Interest Rates 21 2.3.2 Foreign Exchange Forward Rates 22 2.4 Interest Rate Futures 23 2.4.1 General Description of Interest Rate Futures 23 2.4.2 Implied Forward Interest Rates and Yield Curves 25 2.5 Bond Futures Markets 28 2.5.1 Bonds and Bond Futures 28 2.5.2 Rollover Schemes 29 2.6 Commodity Futures . 3(f\ 2.7 Equity Markets 32 TIME SERIES OF INTEREST 3.1 Time Series and Operators 34 3.2 Variables in Homogeneous Time Series 37 3.2.1 Interpolation 37 3.2.2 Price 38 3.2.3 Return 40 3.2.4 Realized Volatility 41 3.2.5 Bid-Ask Spread 45 3.2.6 Tick Frequency 46 3.2.7 Other Variables 46 3.2.8 Overlapping Returns 47 3.3 Convolution Operators 51 3.3.1 Notation Used for Time Series Operators 53 3.3.2 Linear Operator and Kernels 54 3.3.3 Build-Up Time Interval V 56 3.3.4 Homogeneous Operators and Robustness 58 3.3.5 Exponential Moving Average (EMA) 59 3.3.6 The Iterated EMA Operator 59 3.3.7 Moving Average (MA) 61 3.3.8 Moving Norm, Variance, and Standard Deviation 63 3.3.9 Differential 64 3.3.10 Derivative and y-Derivative 66 3.3.11 Volatility 68 3.3.12 Standardized Time Series, Moving Skewness, and Kurtosis 71 3.3.13 Moving Correlation 71 3.3.14 Windowed Fourier Transform 74 3.4 Microscopic Operators 76 3.4.1 Backward Shift and Time Translation Operators 77 3.4.2 Regular Time Series Operator 77 3.4.3 Microscopic Return, Difference, and Derivative 78 3.4.4 Microscopic Volatility 79 3.4.5 Tick Frequency and Activity 79 ADAPTIVE DATA CLEANING 4.1 Introduction: Using a Filter to Clean the Data 82 4.2 Data and Data Errors 84 4.2.1 Time Series of Ticks 84 4.2.2 Data Error Types 85 4.3 General Overview of the Filter 86 4.3.1 The Functionality of the Filter 86 4.3.2 Overview of the Filtering Algorithm and Tts Structure 88 4.4 Basic Filtering Elements and Operations 88 4.4.1 Credibility and Trust Capital 89 4.4.2 Filtering of Single Scalar Quotes: The Level Filter 91 4.4.3 Pair Filtering: The Credibility of Returns 93 4.4.4 Computing the Expected Volatility 96 4.4.5 Pair Filtering: Comparing Quote Origins 98 4.4.6 A Time Scale for Filtering 100 4.5 The Scalar Filtering Window 103 4.5.1 Entering a New Quote in the Scalar Filtering Window 104 4.5.2 The Trust Capital of a New Scalar Quote 104 4.5.3 Updating the Scalar Window 106 4.5.4 Dismissing Quotes from the Scalar Window 107 4.5.5 Updating the Statistics with Credible Scalar Quotes 108 4.5.6 A Second Scalar Window for Old Valid Quotes 108 4.6 The Full-Quote Filtering Window 109 4.6.1 Quote Splitting Depending on the Instrument Type 110 4.6.2 The Basic Validity Test 110 4.6.3 Transforming the Filtered Variable 112 4.7 Univariate Filtering 113 4.7.1 The Results of Univariate Filtering 114 4.7.2 Filtering in Historical and Real-Time Modes 115 4.7.3 Choosing the Filter Parameters 116 4.8 Special Filter Elements 116 4.8.1 Multivariate Filtering: Filtering Sparse Data 116 4.9 Behavior and Effects of the Data Filter 118 * \ BASIC STYLIZED FACTS 5.1 Introduction 121 5.2 Price Formation Process 123 5.2.1 Negative First-Order Autocorrelation of Returns 123 5.2.2 Discreteness of Quoted Spreads 125 5.2.3 Short-Term Triangular Arbitrage 127 5.3 Institutional Structure and Exogeneous Impacts 127 5.3.1 Institutional Framework 127 5.3.2 Positive Impact of Official Interventions 129 5.3.3 Mixed Effect of News 129 5.4 Distributional Properties of Returns 132 , 5.4.1 Finite Variance, Symmetry and Decreasing Fat-Tailedness 132 5.4.2 The Tail Index of Return Distributions 135 5.4.3 Extreme Risks in Financial Markets 144 5.5 Scaling Laws 147 5.5.1 Empirical Evidence 147 5.5.2 Distributions and Scaling Laws 151 5.5.3 A Simple Model of the Market Maker Bias 154 5.5.4 Limitations of the Scaling Laws 158 5.6 Autocorrelation and Seasonality 160 5.6.1 Autocorrelations of Returns and Volatility 161 5.6.2 Seasonal Volatility: Across Markets for OTC Instruments 163 5.6.3 Seasonal Volatility: U-Shaped for Exchange Traded Instruments 167 5.6.4 Deterministic Volatility in Eurofutures Contracts 169 5.6.5 Bid-Ask Spreads 170 6 MODELING SEASONAL VOLATILITY 6.1 Introduction 174 6.2 A Model of Market Activity 175 6.2.1 Seasonal Patterns of the Volatility and Presence of Markets 175 6.2.2 Modeling the Volatility Patterns with an Alternative Time Scale and an Activity Variable 176 6.2.3 Market Activity and Scaling Law 177 6.2.4 Geographical Components of Market Activity 178 6.2.5 A Model of Intraweek Market Activity 179 6.2.6 Interpretation of the Activity Modeling Results 183 6.3 A New Business Time Scale (#-Scale) 188 6.3.1 Definition of the &-Scale 188 6.3.2 Adjustments of the #-Scale Definition 189 6.3.3 A Ratio Test for the #-Scale Quality 192 6.4 Filtering Intraday Seasonalities with Wavelets 193 I 7 REALIZED VOLATILITY DYNAMICS 7.1 Introduction 197 7.2 The Bias of Realized Volatility and Its Correction 198 7.3 Conditional Heteroskedasticity 204 7.3.1 Autocorrelation of Volatility in г?-Time 204 7.3.2 Short and Long Memory 207 7.4 The Heterogeneous Market Hypothesis 209 7.4.1 Volatilities of Different Time Resolutions 210 7.4.2 Asymmetric Lead-Lag Correlation of Volatilities 211 7.4.3 Conditional Predictability 215 8 ) VOLATILITY PROCESSES 8.1 Introduction 219 8.2 Intraday Volatility and GARCH Models 221 8.2.1 Parameter Estimation of GARCH Models 222 8.2.2 Temporal Aggregation of GARCH Models 224 8.2.3 Estimates of GARCH( 1,1) for Various Frequencies 226 8.3 Modeling Heterogeneous Volatilities 231 8.3.1 The HARCH Model 231 8.3.2 HARCH and Market Components 234 8.3.3 Generalization of the Process Equation 237 8.3.4 EMA-HARCH Model 237 8.3.5 Estimating HARCH and EMA-HARCH Models 239 8.3.6 HARCH in Interest Rate Modeling 242 8.4 Forecasting Short-Term Volatility 243 8.4.1 A Framework to Measure the Forecasting Performance 243 8.4.2 Performance of ARCH-Type Models 246 9 FORECASTING RISK AND RETURN 9.1 Introduction to Forecasting 248 9.2 Forecasting Volatility for Value-at-Risk 250 9.2.1 Three Simple Volatility Forecasting Models 250 9.2.2 Choosing the Best Volatility Forecasting Model 254 9.3 Forecasting Returns over Multiple Time Horizons 255 9.3.1 Intrinsic Time 255 9.3.2 Model Structure 256 9.3.3 A Linear Combination of Nonlinear Indicators 256 9.3.4 Moving Averages, Momenta, and Indicators 257 9.3.5 Continuous Coefficient Update 259 9.4 Measuring Forecast Quality 261 9.4.1 Appropriate Measures of Forecast Accuracy 262 9.4.2 Empirical Results for the Multi-Horizon Model 263 9.4.3 Forecast Effectiveness in Intraday Horizons 264 10 У CORRELATION AND MULTIVARIATE RISK 10.1 Introduction 268 10.2 Estimating the Dependence of Financial Time Series 269 10.3 Covolatility Weighting 270 10.3.1 Formulation of an Adjusted Correlation Measure 272 10.3.2 Monte Carlo and Empirical Tests 274 10.4 Stability of Return Correlations 277 10.4.1 Correlation Variations over Time 278 10.4.2 The Exponential Memory of Return Correlations 282 10.5 Correlation Behavior at High Data Frequencies 287 10.6 Conclusions 293 I I TRADING MODELS 11.1 Introduction 295 11.2 Real-Time Trading Strategies 297 11.2.1 The Trading Model and Its Data-Processing Environment 299 11.2.2 Simulated Trader 303 11.3 Risk Sensitive Performance Measures 304 11.3.1 Xejf. A Symmetric Effective Returns Measure 305 11.3.2 Reff: An Asymmetric Effective Returns Measure 307 11.4 Trading Model Algorithms 309 11.4.1 An Example of a Trading Model 310 11.4.2 Model Design with Genetic Programming 311 11.5 Optimization and Testing Procedures 317 11.5.1 Robust Optimization with Genetic Algorithms 317 11.5.2 Testing Procedures 321 11.6 Statistical Study of a Trading Model 323 11.6.1 Heterogeneous Real-Time Trading Strategies 323 11.6.2 Price-Generation Processes and Trading Models 328 11.7 Trading Model Portfolios 338 11.8 Currency Risk Hedging 340 11.8.1 The Hedging Ratio and the "Neutral Point" 343 11.8.2 Risk/Return of an Overlay with Static and Dynamic Positions 344 11.8.3 Dynamic Hedging with Exposure Constraints 345 11.8.4 Concluding Remarks 346 12 TOWARD A THEORY OF HETEROGENEOUS MARKETS 12.1 Definition of Efficient Markets 349 12.2 Dynamic Markets and Relativistic Effects 350 12.3 Impact of the New Technology 352 12.4 Zero-Sum Game or Perpetuum Mobile? 353 12.5 Discussion of the Conventional Definition 354 12.6 An Improved Definition of "Efficient Markets" 354 BIBLIOGRAPHY 356 INDEX 376 |
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An Introduction to High-Frequency Finance (2001)