Asset price dynamics, volatility, and prediction:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Princeton, NJ [u.a.]
Princeton Univ. Pr.
2007
|
| Edition: | 3. print., 1. paperback print. |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis Klappentext |
| Physical Description: | XV, 525 S. graph. Darst. |
| ISBN: | 9780691134796 |
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MARC
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| 100 | 1 | |a Taylor, Stephen |d 1954- |e Verfasser |0 (DE-588)131549766 |4 aut | |
| 245 | 1 | 0 | |a Asset price dynamics, volatility, and prediction |c Stephen J. Taylor |
| 250 | |a 3. print., 1. paperback print. | ||
| 264 | 1 | |a Princeton, NJ [u.a.] |b Princeton Univ. Pr. |c 2007 | |
| 300 | |a XV, 525 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
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| 650 | 4 | |a Capital assets pricing model | |
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| adam_text | Contents
Introduction
1.1
Asset Price Dynamics
1.2
Volatility
1.3
Prediction
1.4
Information
1.5
Contents
1.6
Software
1.7
Web Resources
Preface
xiii
1
1
1
2
2
3
5
6
1 Foundations
7
2
Prices and Returns
9
2.1
Introduction
. 9
2.2
Two Examples of Price Series
9
2.3
Data-Collection Issues
10
2.4
Two Returns Series
13
2.5
Definitions of Returns
14
2.6
Further Examples of Time Series of Returns
19
3
Stochastic Processes: Definitions and Examples
23
3.1
Introduction
23
3.2
Random Variables
24
3.3
Stationary Stochastic Processes
30
3.4
Uncorrelated Processes
33
3.5
ARMA
Processes
36
3.6
Examples of
ARMA
(1, 1)
Specifications
44
3.7
ARMA
Processes
46
3.8
ARFIMA Processes
46
3.9
Linear Stochastic Processes
48
3.10
Continuous-Time Stochastic Processes
49
3.11
Notation for Random Variables and Observations
50
4
Stylized Facts for Financial Returns
51
4.1
Introduction
51
4.2
Summary Statistics
52
4.3
Average Returns and Risk
Premia
53
4.4
Standard Deviations
57
4.5
Calendar Effects
59
4.6
Skewness and Kurtosis
68
4.7
The Shape of the Returns Distribution
69
4.8
Probability Distributions for Returns
73
4.9
Autocorrelations of Returns
76
4.10
Autocorrelations of Transformed Returns
82
4.11
Nonlinearity of the Returns Process
92
4.12
Concluding Remarks
93
4.13
Appendix: Autocorrelation Caused by Day-of-the-Week Effects
94
4.14
Appendix: Autocorrelations of a Squared Linear Process
95
II Conditional Expected Returns
97
5
The Variance-Ratio Test of the Random Walk Hypothesis
99
5.1
Introduction
99
5.2
The Random Walk Hypothesis
100
5.3
Variance-Ratio Tests
102
5.4
An Example of Variance-Ratio Calculations
105
5.5
Selected Test Results
107
5.6
Sample Autocorrelation Theory
112
5.7
Random Walk Tests Using Rescaled Returns
115
5.8
Summary
120
6
Further Tests of the Random Walk Hypothesis
121
6.1
Introduction
121
6.2
Test Methodology
122
6.3
Further Autocorrelation Tests
126
6.4
Spectral Tests
130
6.5
The Runs Test
133
6.6
Rescaled Range Tests
135
6.7
The BDS Test
136
6.8
Test Results for the Random Walk Hypothesis
138
6.9
The Size and Power of Random Walk Tests
144
6.10
Sources of Minor Dependence in Returns
148
6.11
Concluding Remarks
151
6.12
Appendix: the Correlation between Test Values for Two Correlated Series
153
6.13
Appendix: Autocorrelation Induced by Rescaling Returns
154
7
Trading Rules and Market Efficiency
157
7.1
Introduction
157
7.2
Four Trading Rules
158
7.3
Measures of Return Predictability
163
7.4
Evidence about Equity Return Predictability
166
7.5
Evidence about the Predictability of Currency and Other Returns
168
7.6
An Example of Calculations for the Moving-Average Rule
172
7.7
Efficient Markets: Methodological Issues
175
7.8
Breakeven Costs for Trading Rules Applied to Equities
176
7.9
Trading Rule Performance for Futures Contracts
179
7.10
The Efficiency of Currency Markets
181
7.11
Theoretical Trading Profits for Autocorrelated Return Processes
184
7.12
Concluding Remarks
186
III Volatility Processes
187
8
An Introduction to Volatility
189
8.1
Definitions of Volatility
189
8.2
Explanations of Changes in Volatility
191
8.3
Volatility and Information Arrivals
193
8.4
Volatility and the Stylized Facts for Returns
195
8.5
Concluding Remarks
196
9
ARCH Models: Definitions and Examples
197
9.1
Introduction
197
9.2
ARCHO)
198
9.3
GARCHil,
1) 199
9.4
An Exchange Rate Example of the GARCH
(1,1)
Model
205
9.5
A General ARCH Framework
212
9.6
Nonnormal Conditional Distributions
217
9.7
Asymmetric Volatility Models
220
9.8
Equity Examples of Asymmetric Volatility Models
222
9.9
Summary
233
10
ARCH Models: Selection and Likelihood Methods
235
10.1
Introduction
235
10.2
Asymmetric Volatility: Further Specifications and Evidence
235
10.3
Long Memory ARCH Models
242
10.4
Likelihood Methods
245
10.5
Results from Hypothesis Tests
251
10.6
Model Building
256
10.7
Further Volatility Specifications
261
10.8
Concluding Remarks
264
10.9
Appendix: Formulae for the Score Vector
265
11
Stochastic Volatility Models
267
11.1
Introduction
267
11.2
Motivation and Definitions
268
11.3
Moments of Independent
S V
Processes
270
11.4
Markov Chain Models for Volatility
271
11.5
The Standard Stochastic Volatility Model
278
11.6
Parameter Estimation for the Standard
S V
Model
283
11.7
An Example of
S V
Model Estimation for Exchange Rates
288
11.8
Independent SV Models with Heavy Tails
291
11.9
Asymmetric Stochastic Volatility Models
293
11.10
Long
Memory SV Models 297
11.11
Multivariate
Stochastic Volatility Models
298
11.12
ARCH versus SV
299
11.13
Concluding Remarks
301
11.14
Appendix: Filtering Equations
301
IV High-Frequency Methods
303
12
High-Frequency Data and Models
305
12.1
Introduction
305
12.2
High-Frequency Prices
306
12.3
One Day of High-Frequency Price Data
309
12.4
Stylized Facts for Intraday Returns
310
12.5
Intraday Volatility Patterns
316
12.6
Discrete-Time Intraday Volatility Models
321
12.7
Trading Rules and Intraday Prices
325
12.8
Realized Volatility: Theoretical Results
327
12.9
Realized Volatility: Empirical Results
· 332
12.10
Price Discovery
342
12.11
Durations
343
12.12
Extreme Price Changes
344
12.13
Daily High and Low Prices
346
12.14
Concluding Remarks
348
12.15
Appendix: Formulae for the Variance of the Realized Volatility Estimator
349
V Inferences from Option Prices
351
13
Continuous-Time Stochastic Processes
353
13.1
Introduction
353
13.2
The Wiener Process
354
13.3
Diffusion Processes
355
13.4
Divariate
Diffusion Processes
359
13.5
Jump Processes
361
13.6
Jump-Diffusion Processes
363
13.7
Appendix: a Construction of the Wiener Process
366
14
Option Pricing Formulae
369
14.1
Introduction
369
14.2
Definitions, Notation, and Assumptions
370
14.3
Black-Scholes and Related Formulae
372
14.4
Implied Volatility
378
14.5
Option Prices when Volatility Is Stochastic
383
14.6
Closed-Form Stochastic Volatility Option Prices
388
14.7
Option Prices for ARCH Processes
391
14.8
Summary
394
14.9
Appendix: Heston s Option Pricing Formula
395
15
Forecasting Volatility
397
15.1
Introduction
397
15.2
Forecasting Methodology
398
15.3
Two Measures of Forecast Accuracy
401
15.4
Historical Volatility Forecasts
403
15.5
Forecasts from Implied Volatilities
407
15.6
ARCH Forecasts that Incorporate Implied Volatilities
410
15.7
High-Frequency Forecasting Results
414
15.8
Concluding Remarks
420
16
Density Prediction for Asset Prices
423
16.1
Introduction
423
16.2
Simulated Real-World Densities
424
16.3
Risk-Neutral Density Concepts and Definitions
428
16.4
Estimation of Implied Risk-Neutral Densities
431
16.5
Parametric Risk-Neutral Densities
435
16.6
Risk-Neutral Densities from Implied Volatility Functions
446
16.7
Nonparametric RND Methods
448
16.8
Towards Recommendations
450
16.9
From Risk-Neutral to Real-World Densities
451
16.10
An Excel Spreadsheet for Density Estimation
458
16.11
Risk Aversion and Rational RNDs
461
16.12
Tail Density Estimates
464
16.13
Concluding Remarks
465
Symbols
467
References
473
Author Index
503
Subject Index
513
This book shows how current and recent market prices convey information about the
probability distributions that govern future prices. Moving beyond purely theoretical
models, Stephen Taylor applies methods supported by empirical research of equity and
foreign-exchange markets to show how daily and more frequent asset prices, and the
prices of option contracts, can be used to make and evaluate predictions about future
prices, their volatility, and their probability distributions.
Taylor provides a comprehensive introduction to the dynamic
behavior of asset prices, relying on finance theory and statistical
evidence. He uses stochastic processes to define mathematical
models for price dynamics, but with less mathematics than
in other books. The key topics covered include random-walk
tests, trading rules, ARCH models, stochastic volatility
models, high-frequency
datasets,
and the information that
option prices imply about volatility and distributions.
Asset Price Dynamics.Volatility, and Prediction is ideal for
students of economics, finance, and mathematics who are
studying financial econometrics, and will enable researchers to
identify and apply appropriate models and methods. It will also be a valuable resource for
quantitative analysts, fund and risk managers, and investors who seek realistic expectations
about future asset prices and the risks to which they are exposed.
|
| adam_txt |
Contents
Introduction
1.1
Asset Price Dynamics
1.2
Volatility
1.3
Prediction
1.4
Information
1.5
Contents
1.6
Software
1.7
Web Resources
Preface
xiii
1
1
1
2
2
3
5
6
1 Foundations
7
2
Prices and Returns
9
2.1
Introduction
. 9
2.2
Two Examples of Price Series
9
2.3
Data-Collection Issues
10
2.4
Two Returns Series
13
2.5
Definitions of Returns
14
2.6
Further Examples of Time Series of Returns
19
3
Stochastic Processes: Definitions and Examples
23
3.1
Introduction
23
3.2
Random Variables
24
3.3
Stationary Stochastic Processes
30
3.4
Uncorrelated Processes
33
3.5
ARMA
Processes
36
3.6
Examples of
ARMA
(1, 1)
Specifications
44
3.7
ARMA
Processes
46
3.8
ARFIMA Processes
46
3.9
Linear Stochastic Processes
48
3.10
Continuous-Time Stochastic Processes
49
3.11
Notation for Random Variables and Observations
50
4
Stylized Facts for Financial Returns
51
4.1
Introduction
51
4.2
Summary Statistics
52
4.3
Average Returns and Risk
Premia
53
4.4
Standard Deviations
57
4.5
Calendar Effects
59
4.6
Skewness and Kurtosis
68
4.7
The Shape of the Returns Distribution
69
4.8
Probability Distributions for Returns
73
4.9
Autocorrelations of Returns
76
4.10
Autocorrelations of Transformed Returns
82
4.11
Nonlinearity of the Returns Process
92
4.12
Concluding Remarks
93
4.13
Appendix: Autocorrelation Caused by Day-of-the-Week Effects
94
4.14
Appendix: Autocorrelations of a Squared Linear Process
95
II Conditional Expected Returns
97
5
The Variance-Ratio Test of the Random Walk Hypothesis
99
5.1
Introduction
99
5.2
The Random Walk Hypothesis
100
5.3
Variance-Ratio Tests
102
5.4
An Example of Variance-Ratio Calculations
105
5.5
Selected Test Results
107
5.6
Sample Autocorrelation Theory
112
5.7
Random Walk Tests Using Rescaled Returns
115
5.8
Summary
120
6
Further Tests of the Random Walk Hypothesis
121
6.1
Introduction
121
6.2
Test Methodology
122
6.3
Further Autocorrelation Tests
126
6.4
Spectral Tests
130
6.5
The Runs Test
133
6.6
Rescaled Range Tests
135
6.7
The BDS Test
136
6.8
Test Results for the Random Walk Hypothesis
138
6.9
The Size and Power of Random Walk Tests
144
6.10
Sources of Minor Dependence in Returns
148
6.11
Concluding Remarks
151
6.12
Appendix: the Correlation between Test Values for Two Correlated Series
153
6.13
Appendix: Autocorrelation Induced by Rescaling Returns
154
7
Trading Rules and Market Efficiency
157
7.1
Introduction
157
7.2
Four Trading Rules
158
7.3
Measures of Return Predictability
163
7.4
Evidence about Equity Return Predictability
166
7.5
Evidence about the Predictability of Currency and Other Returns
168
7.6
An Example of Calculations for the Moving-Average Rule
172
7.7
Efficient Markets: Methodological Issues
175
7.8
Breakeven Costs for Trading Rules Applied to Equities
176
7.9
Trading Rule Performance for Futures Contracts
179
7.10
The Efficiency of Currency Markets
181
7.11
Theoretical Trading Profits for Autocorrelated Return Processes
184
7.12
Concluding Remarks
186
III Volatility Processes
187
8
An Introduction to Volatility
189
8.1
Definitions of Volatility
189
8.2
Explanations of Changes in Volatility
191
8.3
Volatility and Information Arrivals
193
8.4
Volatility and the Stylized Facts for Returns
195
8.5
Concluding Remarks
196
9
ARCH Models: Definitions and Examples
197
9.1
Introduction
197
9.2
ARCHO)
198
9.3
GARCHil,
1) 199
9.4
An Exchange Rate Example of the GARCH
(1,1)
Model
205
9.5
A General ARCH Framework
212
9.6
Nonnormal Conditional Distributions
217
9.7
Asymmetric Volatility Models
220
9.8
Equity Examples of Asymmetric Volatility Models
222
9.9
Summary
233
10
ARCH Models: Selection and Likelihood Methods
235
10.1
Introduction
235
10.2
Asymmetric Volatility: Further Specifications and Evidence
235
10.3
Long Memory ARCH Models
242
10.4
Likelihood Methods
245
10.5
Results from Hypothesis Tests
251
10.6
Model Building
256
10.7
Further Volatility Specifications
261
10.8
Concluding Remarks
264
10.9
Appendix: Formulae for the Score Vector
265
11
Stochastic Volatility Models
267
11.1
Introduction
267
11.2
Motivation and Definitions
268
11.3
Moments of Independent
S V
Processes
270
11.4
Markov Chain Models for Volatility
271
11.5
The Standard Stochastic Volatility Model
' 278
11.6
Parameter Estimation for the Standard
S V
Model
283
11.7
An Example of
S V
Model Estimation for Exchange Rates
288
11.8
Independent SV Models with Heavy Tails
291
11.9
Asymmetric Stochastic Volatility Models
293
11.10
Long
Memory SV Models 297
11.11
Multivariate
Stochastic Volatility Models
298
11.12
ARCH versus SV
299
11.13
Concluding Remarks
301
11.14
Appendix: Filtering Equations
301
IV High-Frequency Methods
303
12
High-Frequency Data and Models
305
12.1
Introduction
305
12.2
High-Frequency Prices
306
12.3
One Day of High-Frequency Price Data
309
12.4
Stylized Facts for Intraday Returns
310
12.5
Intraday Volatility Patterns
316
12.6
Discrete-Time Intraday Volatility Models
321
12.7
Trading Rules and Intraday Prices
325
12.8
Realized Volatility: Theoretical Results
327
12.9
Realized Volatility: Empirical Results
· 332
12.10
Price Discovery
342
12.11
Durations
343
12.12
Extreme Price Changes
344
12.13
Daily High and Low Prices
346
12.14
Concluding Remarks
348
12.15
Appendix: Formulae for the Variance of the Realized Volatility Estimator
349
V Inferences from Option Prices
351
13
Continuous-Time Stochastic Processes
353
13.1
Introduction
353
13.2
The Wiener Process
354
13.3
Diffusion Processes
355
13.4
Divariate
Diffusion Processes
359
13.5
Jump Processes
361
13.6
Jump-Diffusion Processes
363
13.7
Appendix: a Construction of the Wiener Process
366
14
Option Pricing Formulae
369
14.1
Introduction
369
14.2
Definitions, Notation, and Assumptions
370
14.3
Black-Scholes and Related Formulae
372
14.4
Implied Volatility
378
14.5
Option Prices when Volatility Is Stochastic
383
14.6
Closed-Form Stochastic Volatility Option Prices
388
14.7
Option Prices for ARCH Processes
391
14.8
Summary
394
14.9
Appendix: Heston's Option Pricing Formula
395
15
Forecasting Volatility
397
15.1
Introduction
397
15.2
Forecasting Methodology
398
15.3
Two Measures of Forecast Accuracy
401
15.4
Historical Volatility Forecasts
403
15.5
Forecasts from Implied Volatilities
407
15.6
ARCH Forecasts that Incorporate Implied Volatilities
410
15.7
High-Frequency Forecasting Results
414
15.8
Concluding Remarks
420
16
Density Prediction for Asset Prices
423
16.1
Introduction
423
16.2
Simulated Real-World Densities
424
16.3
Risk-Neutral Density Concepts and Definitions
428
16.4
Estimation of Implied Risk-Neutral Densities
431
16.5
Parametric Risk-Neutral Densities
435
16.6
Risk-Neutral Densities from Implied Volatility Functions
446
16.7
Nonparametric RND Methods
448
16.8
Towards Recommendations
450
16.9
From Risk-Neutral to Real-World Densities
451
16.10
An Excel Spreadsheet for Density Estimation
458
16.11
Risk Aversion and Rational RNDs
461
16.12
Tail Density Estimates
464
16.13
Concluding Remarks
465
Symbols
467
References
473
Author Index
503
Subject Index
513
This book shows how current and recent market prices convey information about the
probability distributions that govern future prices. Moving beyond purely theoretical
models, Stephen Taylor applies methods supported by empirical research of equity and
foreign-exchange markets to show how daily and more frequent asset prices, and the
prices of option contracts, can be used to make and evaluate predictions about future
prices, their volatility, and their probability distributions.
Taylor provides a comprehensive introduction to the dynamic
behavior of asset prices, relying on finance theory and statistical
evidence. He uses stochastic processes to define mathematical
models for price dynamics, but with less mathematics than
in other books. The key topics covered include random-walk
tests, trading rules, ARCH models, stochastic volatility
models, high-frequency
datasets,
and the information that
option prices imply about volatility and distributions.
Asset Price Dynamics.Volatility, and Prediction is ideal for
students of economics, finance, and mathematics who are
studying financial econometrics, and will enable researchers to
identify and apply appropriate models and methods. It will also be a valuable resource for
quantitative analysts, fund and risk managers, and investors who seek realistic expectations
about future asset prices and the risks to which they are exposed. |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Taylor, Stephen 1954- |
| author_GND | (DE-588)131549766 |
| author_facet | Taylor, Stephen 1954- |
| author_role | aut |
| author_sort | Taylor, Stephen 1954- |
| author_variant | s t st |
| building | Verbundindex |
| bvnumber | BV022761122 |
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| callnumber-subject | HG - Finance |
| classification_rvk | QK 622 QK 800 |
| classification_tum | WIR 175f |
| ctrlnum | (OCoLC)154694663 (DE-599)BVBBV022761122 |
| dewey-full | 332.60151962 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 332 - Financial economics |
| dewey-raw | 332.60151962 |
| dewey-search | 332.60151962 |
| dewey-sort | 3332.60151962 |
| dewey-tens | 330 - Economics |
| discipline | Wirtschaftswissenschaften |
| discipline_str_mv | Wirtschaftswissenschaften |
| edition | 3. print., 1. paperback print. |
| format | Book |
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| id | DE-604.BV022761122 |
| illustrated | Illustrated |
| index_date | 2024-07-02T18:34:36Z |
| indexdate | 2024-07-09T21:05:29Z |
| institution | BVB |
| isbn | 9780691134796 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-015966674 |
| oclc_num | 154694663 |
| open_access_boolean | |
| owner | DE-N2 DE-1047 DE-739 DE-83 DE-11 DE-355 DE-BY-UBR DE-523 DE-Aug4 |
| owner_facet | DE-N2 DE-1047 DE-739 DE-83 DE-11 DE-355 DE-BY-UBR DE-523 DE-Aug4 |
| physical | XV, 525 S. graph. Darst. |
| publishDate | 2007 |
| publishDateSearch | 2007 |
| publishDateSort | 2007 |
| publisher | Princeton Univ. Pr. |
| record_format | marc |
| spelling | Taylor, Stephen 1954- Verfasser (DE-588)131549766 aut Asset price dynamics, volatility, and prediction Stephen J. Taylor 3. print., 1. paperback print. Princeton, NJ [u.a.] Princeton Univ. Pr. 2007 XV, 525 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Mathematisches Modell Capital assets pricing model Finance Mathematical models Capital-Asset-Pricing-Modell (DE-588)4121078-5 gnd rswk-swf Kapitalmarkt (DE-588)4029578-3 gnd rswk-swf Capital-Asset-Pricing-Modell (DE-588)4121078-5 s Kapitalmarkt (DE-588)4029578-3 s DE-604 Digitalisierung UB Passau application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015966674&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Passau application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015966674&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Taylor, Stephen 1954- Asset price dynamics, volatility, and prediction Mathematisches Modell Capital assets pricing model Finance Mathematical models Capital-Asset-Pricing-Modell (DE-588)4121078-5 gnd Kapitalmarkt (DE-588)4029578-3 gnd |
| subject_GND | (DE-588)4121078-5 (DE-588)4029578-3 |
| title | Asset price dynamics, volatility, and prediction |
| title_auth | Asset price dynamics, volatility, and prediction |
| title_exact_search | Asset price dynamics, volatility, and prediction |
| title_exact_search_txtP | Asset price dynamics, volatility, and prediction |
| title_full | Asset price dynamics, volatility, and prediction Stephen J. Taylor |
| title_fullStr | Asset price dynamics, volatility, and prediction Stephen J. Taylor |
| title_full_unstemmed | Asset price dynamics, volatility, and prediction Stephen J. Taylor |
| title_short | Asset price dynamics, volatility, and prediction |
| title_sort | asset price dynamics volatility and prediction |
| topic | Mathematisches Modell Capital assets pricing model Finance Mathematical models Capital-Asset-Pricing-Modell (DE-588)4121078-5 gnd Kapitalmarkt (DE-588)4029578-3 gnd |
| topic_facet | Mathematisches Modell Capital assets pricing model Finance Mathematical models Capital-Asset-Pricing-Modell Kapitalmarkt |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015966674&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015966674&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT taylorstephen assetpricedynamicsvolatilityandprediction |