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E-raamat: Modelling Financial Time Series 2nd Revised edition [World Scientific e-raamat]

Edited by (Lancaster Univ, Uk)
  • Formaat: 296 pages
  • Ilmumisaeg: 16-Oct-2007
  • Kirjastus: World Scientific Publishing Co Pte Ltd
  • ISBN-13: 9789812770851
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  • World Scientific e-raamat
  • Hind: 81,31 €*
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Modelling Financial Time Series 2nd Revised editionSuurem pilt
  • Formaat: 296 pages
  • Ilmumisaeg: 16-Oct-2007
  • Kirjastus: World Scientific Publishing Co Pte Ltd
  • ISBN-13: 9789812770851
Teised raamatud teemal:
World Scientific e-raamatudRohkem infot World Scientific e-raamatute kohta
Raamatu kodulehekülg: http://www.worldscientific.com/worldscibooks/10.1142/6578#t=toc
Building on a classical text in financial econometrics, this edition includes new material on measuring, modeling and forecasting volatility and detecting and exploiting price trends and contains several innovative models for the prices of financial assets. Taylor (Lancaster U.) begins by preparing readers to use stochastic processes, including linear stochastic process, and continues by describing features of financial returns, including their construction of financial time series and standard deviations. He describes the process of modeling price volatility (including material on results for ARCH processes), forecasting standard deviations, discerning the accuracy of autocorrelation estimates, testing the "random walk" hypothesis, forecasting trends in prices, understanding evidence against the efficiency of futures markets, and valuing options. He includes a computer program for modeling financial time series. Annotation ©2008 Book News, Inc., Portland, OR (booknews.com)
Page Preface to the 2nd edition xv
Preface to the 1st edition xxv
1 INTRODUCTION
1
1.1 Financial time series
1
1.2 About this study
2
1.3 The world's major financial markets
3
1.4 Examples of daily price series
4
1.5 A selective review of previous research
8
Important questions
8
The random walk hypothesis
8
The efficient market hypothesis
10
1.6 Daily returns
12
1.7 Models
13
1.8 Models in this book
15
1.9 Stochastic processes
16
General remarks
16
Stationary processes
16
Autocorrelation
17
Spectral density
18
White noise
19
ARMA processes
20
Gaussian processes
23
1.10 Linear stochastic processes
23
Their definition
23
Autocorrelation tests
24
2 FEATURES OF FINANCIAL RETURNS 26
2.1 Constructing financial time series
26
Sources
26
Time scales
27
Additional information
27
Using futures contracts
28
2.2 Prices studied
28
Spot prices
28
Futures prices
30
Commodity futures
30
Financial futures
31
Extended series
32
2.3 Average returns and risk premia
32
Annual expected returns
33
Common stocks and ordinary shares
35
Spot commodities
36
Spot currencies
36
Commodity futures
36
2.4 Standard deviations
38
Risks compared
39
Futures and contract age
40
2.5 Calendar effects
41
Day-of-the-week
41
Stocks
41
Currencies
41
Agricultural futures
42
Standard deviations
42
Month-of-the-year effects for stocks
43
2.6 Skewness
44
2.7 Kurtosis
44
2.8 Plausible distributions
45
2.9 Autocorrelation
48
First-lag
49
Lags 1 to 30
50
Tests
50
2.10 Non-linear structure
52
Not strict white noise
52
A characteristic of returns
52
Not linear
56
Consequences of non-linear structure
57
2.11 Summary
58
Appendix 2(A) Autocorrelation caused by day-of-the-week effects
58
Appendix 2(B) Autocorrelations of a squared linear process
60
3 MODELLING PRICE VOLATILITY
62
3.1 Introduction
62
3.2 Elementary variance models
63
Step change, discrete distributions
63
Markov variances, discrete distributions
64
Step variances, continuous distributions
65
Markov variances, continuous distributions
66
3.3 A general variance model
67
Notation
69
3.4 Modelling variance jumps
69
3.5 Modelling frequent variance changes not caused by prices
70
General models
70
Stationary models
72
The lognormal, autoregressive model
73
3.6 Modelling frequent variance changes caused by past prices
75
General concepts
75
Caused by past squared returns
76
Caused by past absolute returns
78
ARMACH models
78
3.7 Modelling autocorrelation and variance changes
79
Variances not caused by returns
81
Variances caused by returns
82
3.8 Parameter estimation for variance models
83
3.9 Parameter estimates for product processes
84
Lognormal AR(1)
86
Results
88
3.10 Parameter estimates for ARMACH processes
90
Results
92
3.11 Summary
93
Appendix 3(A) Results for ARCH processes
95
4 FORECASTING STANDARD DEVIATIONS
97
4.1 Introduction
97
4.2 Key theoretical results
98
Uncorrelated returns
98
Correlated returns
100
Relative mean square errors
100
Stationary processes
100
4.3 Forecasts: methodology and methods
101
Benchmark forecast
101
Parametric forecasts
101
Product process forecasts
102
ARMACH forecasts
103
EWMA forecasts
103
Futures forecasts
104
Empirical RMSE
105
4.4 Forecasting results
106
Absolute returns
106
Conditional standard deviations
107
Two leading forecasts
108
More distant forecasts
108
Conclusions about stationarity
110
Another approach
110
4.5 Recommended forecasts for the next day
110
Examples
113
4.6 Summary
114
5 THE ACCURACY OF AUTOCORRELATION ESTIMATES
116
5.1 Introduction
116
5.2 Extreme examples
117
5.3 A special null hypothesis
118
5.4 Estimates of the variances of sample autocorrelations
119
5.5 Some asymptotic results
120
Linear processes
121
Non-linear processes
122
5.6 Interpreting the estimates
123
5.7 The estimates for returns
124
5.8 Accurate autocorrelation estimates
126
Rescaled returns
127
Variance estimates for recommended coefficients
128
Exceptional series
130
5.9 Simulation results
130
5.10 Autocorrelations of restated processes
131
5.11 Summary
132
6 TESTING THE RANDOM WALK HYPOTHESIS
133
6.1 Introduction
133
6.2 Test methodology
134
6.3 Distributions of sample autocorrelations
135
Asymptotic limits
136
Finite samples
136
6.4 A selection of test statistics
137
Autocorrelation tests
137
Spectral tests
138
The runs test
140
6.5 The price-trend hypothesis
141
Price-trend autocorrelations
141
An example
142
Price-trend spectral density
143
6.6 Tests for random walks versus price-trends
143
6.7 Consequences of data errors
145
6.8 Results of random walk tests
146
Stocks
150
Commodities and currencies
152
About the rest of this chapter
156
6.9 Some test results for returns
157
6.10 Power comparisons
159
6.11 Testing equilibrium models
161
Stocks
161
Simulation results
163
Tests
165
Other equilibrium models
166
Conclusion
166
6.12 Institutional effects
167
Limit rules
167
Bid–ask spreads
169
6.13 Results for subdivided series
169
6.14 Conclusions
170
6.15 Summary
172
Appendix 6(A) Correlation between test values for two related series
172
7 FORECASTING TRENDS IN PRICES
174
7.1 Introduction
174
7.2 Price-trend models
174
A non-linear trend model
176
A linear trend model
176
7.3 Estimating the trend parameters
178
Methods
178
Futures
179
Spots
181
Accuracy
183
7.4 Some results from simulations
183
Estimates
183
A puzzle solved
185
7.5 Forecasting returns: theoretical results
185
The next return
186
More distant returns
187
Sums of future returns
187
7.6 Empirical forecasting results
188
Benchmark forecasts
188
Price-trend forecasts
189
Summary statistics
189
Futures
190
Spots
192
7.7 Further forecasting theory
193
Expected changes in prices
193
Forecasting the direction of the trend
194
Forecasting prices
194
7.8 Summary
194
8 EVIDENCE AGAINST THE EFFICIENCY OF FUTURES MARKETS
196
8.1 Introduction
196
8.2 The efficient market hypothesis
197
8.3 Problems raised by previous studies
199
Filter rules
199
Benchmarks
200
Significance
201
Optimization
201
8.4 Problems measuring risk and return
201
Returns
201
Risk
202
Necessary assumptions
203
8.5 Trading conditions
203
8.6 Theoretical analysis
204
Trading strategies
204
Assumptions
205
Conditions for trading profits
206
Inefficient regions
207
Some implications
209
8.7 Realistic strategies and assumptions
210
Strategies
211
Assumptions
212
Notes on objectives
213
8.8 Trading simulated contracts
213
Commodities
214
Currencies
215
8.9 Trading results for futures
216
Calibration contracts
216
Test contracts
217
Portfolio results
222
8.10 Towards conclusions
223
8.11 Summary
224
9 VALUING OPTIONS
225
9.1 Introduction
225
9.2 Black—Scholes option pricing formulae
226
9.3 Evaluating standard formulae
227
9.4 Call values when conditional variances change
228
Formulae for a stationary process
228
Examples
230
Non-stationary processes
233
Conclusions
233
9.5 Price trends and call values
234
A formula for trend models
234
Examples
235
9.6 Summary
237
10 CONCLUDING REMARKS 238
10.1 Price behaviour
238
10.2 Advice to traders
239
10.3 Further research
240
10.4 Stationary models
241
Random walks
241
Price trends
242
APPENDIX: A COMPUTER PROGRAM FOR MODELLING FINANCIAL TIME SERIES 243
Output produced
243
Computer time required
244
User-defined parameters
244
Optional parameters
245
Input requirements
245
About the subroutines
247
FORTRAN program
248
References 256
Author index 262
Subject index 264
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