Книга "An Introduction to High-Frequency Finance" - это первый и единственный источник, где собрано все, что известно о высокочастотных данных. Изложенный в книге материал позволяет осуществлять анализ, моделирование и получение выводов на основе высокочастотных финансовых временных рядов. При рассмотрении рынков валют, процентных ставок и фьючерсов на облигации, а, в особенности, рынка обменных курсов иностранных валют, обобщенный взгляд на методы изучения высокочастотных вренных рядов позволяет исследовать процесс формирования цен. В заключении сделан обзор приемов для построения моделей систематической торговли финансовыми активами.

List of Figures

List of Tables

Preface

Acknowledgments

 1. Introduction

1.1 Markets: The Source of High-Frequency Data

1.2 Methodology of High-Frequency Research

1.3 Data Frequency and Market Information

1.4 New Levels of Significance

1.5 Interrelating Different Time Scales

 2. Markets and Data

2.1 General Remarks on Markets and Data Types

 2.1.1 Spot Markets

 2.1.2 Futures Markets

 2.1.3 Option Markets

2.2 Methodology of High-Frequency Research

 2.2.1 Structure of the Foreign Exchange Spot Market

 2.2.2 Synthetic Cross Rates

 2.2.3 Multiple Contributor Effects

2.3 Over-the-Counter Interest Rate Markets

 2.3.1 Spot Interest Rates

 2.3.2 Foreign Exchange Forward Rates

2.4 Interest Rate Futures

 2.4.1 General Description of Interest Rate Futures

 2.4.2 Implied Forward Interest Rates and Yield Curves

2.5 Bond Futures Markets

 2.5.1 Bonds and Bond Futures

 2.5.2 Rollover Schemes

2.6 Commodity Futures

2.7 Equity Markets

 3. Time Series of Interest

3.1 Time Series and Operators

3.2 Variables in Homogeneous Time Series

 3.2.1 Interpolation

 3.2.2 Price

 3.2.3 Return

 3.2.4 Realized Volatility

 3.2.5 Bid-Ask Spread

 3.2.6 Tick Frequency

 3.2.7 Other Variables

 3.2.8 Overlapping Returns

3.3 Convolution Operators

 3.3.1 Notattion Used for Time Series Operators

 3.3.2 Linear Operator and Kernels

 3.3.3 Build-Up Time Interval

 3.3.4 Homogeneous Operators and Robustness

 3.3.5 Exponential Moving Average (EMA)

 3.3.6 The Iterated EMA Operator

 3.3.7 Moving Average (MA)

 3.3.8 Moving Norm, Variance, and Standard Deviation

 3.3.9 Differential

3.3.10 Derivative and -Derivative

3.3.11 Volatility

3.3.12 Standardized Time Series, Moving Skewness, and Kurtosis

3.3.13 Moving Correlation

3.3.14 Windowed Fourier Transform

3.4 Microscopic Operators

 3.4.1 Backward Shift and Time Translation Operators

 3.4.2 Regular Time Series Operator

 3.4.3 Microscopic Return, Difference, and Derivative

 3.4.4 Microscopic Volatility

 3.4.5 Tick Frequency and Activity

 4. Adaptive Data Cleaning

4.1 Introduction: Using a Filter to Clean the Data

4.2 Data and Data Errors

 4.2.1 Time Series of Ticks

 4.2.2 Data Error Types

4.3 General Overview of the Filter

 4.3.1 The Functionality of the Filter

 4.3.2 Overview of the Filtering Algorithm and Its Structure

4.4 Basic Filtering Elements and Operations

 4.4.1 Credibility and Trust Capital

 4.4.2 Filtering of Single Scalar Quotes: The Level Filter

 4.4.3 Pair Filtering: The Credibility of Returns

 4.4.4 Computing the Expected Volatility

 4.4.5 Pair Filtering: Comparing Quote Origins

 4.4.6 A Time Scale for Filtering

4.5 The Scalar Filtering Window

 4.5.1 Entering a New Quote in the Scalar Filtering Window

 4.5.2 The Trust Capital of a New Scalar Quote

 4.5.3 Updating the Scalar Window

 4.5.4 Dismissing Quotes from the Scalar Window

 4.5.5 Updating the Statistics with Credible Scalar Quotes

 4.5.6 A Second Scalar Window for Old Valid Quotes

4.6 The Full-Quote Filtering Window

 4.6.1 Quote Splitting Depending on the Instrument Type

 4.6.2 The Basic Validity Test

 4.6.3 Transforming the Filtered Variable

4.7 Unvariate Filtering

 4.7.1 The Results of Univariate Filtering

 4.7.2 Filtering in Historical and Real-Time Modes

 4.7.3 Choosing the Filter Parameters

4.8 Special Filter Elements

 4.8.1 Multivariate Filtering: Filtering Sparse Data

4.9 Behavior and Effects of the Data Filter

 5. Basic Stylized Facts

5.1 Introduction

5.2 Price Formation Process

 5.2.1 Negative First-Order Autocorrelation of Returns

 5.2.2 Discreteness of Quoted Spreads

 5.2.3 Short-Term Triangular Arbitrage

5.3 Institutional Structure and Exogeneous Impacts

 5.3.1 Institutional Framework

 5.3.2 Positive Impact of Official Interventions

 5.3.3 Mixed Effect of News

5.4 Distributional Properties of Return

 5.4.1 Finite Variance, Symmetry and Decreasing Fat-Tailedness

 5.4.2 The Tail Index of Return Distributions

 5.4.3 Extreme Risks in Financial Markets

5.5 Scaling Laws

 5.5.1 Empirical Evidence

 5.5.2 Distributions and Scaling Laws

 5.5.3 A Simple Model of the Market Maker Bias

 5.5.4 Limitations of the Scaling Laws

5.6 Autocorrelation and Seasonality

 5.6.1 Autocorrelations of Returns and Volatility

 5.6.2 Seasonal Volatility: Across Markets for OTC Instruments

 5.6.3 Seasonal Volatility: U-Shaped for Exchange Traded Instruments

 5.6.4 Deterministic Volatility in Eurofutures Contracts

 5.6.5 Bid-Ask Spreads

 6. Modeling Seasonal Volatility

6.1 Introduction

6.2 A Model of Market Activity

 6.2.1 Seasonal Patterns of the Volatility and Presence of Markets

 6.2.2 Modeling the Volatility Patterns with an Alternative Time Scale and an Activity Variable

 6.2.3 Market Activity and Scaling Law

 6.2.4 Geographical Components of Market Activity

 6.2.5 A Model of Intraweek Market Activity

 6.2.6 Interpretation of the Activity Modeling Results

6.3 A New Business Time Scale (-Scale)

 6.3.1 Definition of the -Scale

 6.3.2 Adjustments of the -Scale Definition

 6.3.3 A Ratio Test for the -Scale Quality

6.4 Filtering Intraday Seasonalities with Wavelets

 7. Realized Volatility Dynamics

7.1 Introduction

7.2 The Bias of Realized Volatility and Its Correction

7.3 Conditional Heteroskedasticity

 7.3.1 Autocorrelation of Volatility in -Time

 7.3.2 Short and Long Memory

7.4 The Heterogeneous Market Hypothesis

 7.4.1 Volatilities of Different Time Resolution

 7.4.2 Asymmetric Lead-Lag Correlation of Volatilities

 7.4.3 Conditional Predictability

 8. Volatility Processes

8.1 Introduction

8.2 Intraday Volatility and GARCH Models

 8.2.1 Parameter Estimation of GARCH Models

 8.2.2 Temporal Aggregation of GARCH Models

 8.2.3 Estimates of GARCH(1,1) for Various Frequencies

8.3 Modeling Heterogeneous Volatilities

 8.3.1 The HARCH Model

 8.3.2 HARCH and Market Components

 8.3.3 Generalization of the Process Equation

 8.3.4 EMA-HARCH Model

 8.3.5 Estimating HARCH and EMA-Harch Models

 8.3.6 HARCH in Interest Rate Modeling

8.4 Forecasting Short-Term Volatility

 8.4.1 A Framework to Measure the Forecasting Performance

 8.4.2 Performance of ARCH-Type Models

 9. Forecasting Risk and Return

9.1 Introduction to Forecasting

9.2 Forecasting Volatility for Value-at-Risk

 9.2.1 Three Simple Volatility Forecasting Models

 9.2.2 Choosing the Best Volatility Forecasting Model

9.3 Forecasting Returns over Multiple Time Horizons

 9.3.1 Intrinsic Time

 9.3.2 Model Structure

 9.3.3 A Linear Combination of Nonlinear Indicators

 9.3.4 Moving Averages, Momenta, and Indicators

 9.3.5 Continuous Coefficient Update

9.4 Measuring Forecast Quality

 9.4.1 Appropriate Measures of Forecast Accuracy

 9.4.2 Empirical Results for the Multi-Horizon Model

 9.4.3 Forecast Effectiveness in Intraday Horizons

 10. Correlation and Multivariate Risk

10.1 Introduction

10.2 Estimating the Dependence of Financial Time Series

10.3 Covolatility Weighting

 10.3.1 Formulation of an Adjusted Correlation Measure

 10.3.2 Monte Carlo and Empirical Tests

10.4 Stability of Return Correlations

 10.4.1 Correlation Variations over Time

 10.4.2 The Exponential Memory of Return Correlations

10.5 Correlation Behavior at High Data Frequencies

10.6 Conclusions

 11. Trading Models

11.1 Introduction

11.2 Real-Time Trading Strategies

 11.2.1 The Trading Model and Its Data-Processing Environment

 11.2.2 Simulated Trader

11.3 Risk Sensitive Performance Measures

 11.3.1 X_eff: A Symmetric Effective Returns Measure

 11.3.2 R_eff: An Asymmetric Effective Returns Measure

11.4 Trading Model Algorithms

 11.4.1 An Example of a Trading Model

 11.4.2 Model Design with Genetic Programming

11.5 Optimization and Testing Procedures

 11.5.1 Robust Optimization with Genetic Algorithms

 11.5.2 Testing Procedures

11.6 Statistical Study of a Trading Model

 11.6.1 Heterogeneous Real-Time Trading Strategies

 11.6.2 Price-Generation Processes and Trading Models

11.7 Trading Model Portfolios

11.8 Currency Risk Hedging

 11.8.1 The Hedging Ratio and the "Neutral Point"

 11.8.2 Risk/Return of an Overlay with Static and Dynamic Positions

 11.8.3 Dynamic Hedging with Exposure Constraints

 11.8.4 Concluding Remarks

 12. Toward a Theory of Heterogeneous Markets

12.1 Definition of Efficient Markets

12.2 Dynamic Markets and Relativistic Effects

12.3 Impact of the New Technology

12.4 Zero-Sum Game or Perpetuum Mobile?

12.5 Discussion of the Conventional Definition

12.6 An Improved Definition of "Efficien Markets"

Bibliography

Index