Modelling financial time series / Stephen J. Taylor
Taylor, Stephen J.
Material type: Book; Format: print Publisher: New Jersey : World Scientific, 2008Edition: 2nd ed.Description: XXVI, 268 p. : il. ; 24 cm.ISBN: 978-981-277-084-4.Subject(s): Mercado financiero | Análisis de series temporales | InversionesItem type | Home library | Call number | Status | Loan | Date due | Barcode | Item holds | Course reserves |
---|---|---|---|---|---|---|---|---|
Manuales (7 días) | 07. BIBLIOTECA CIENCIAS SOCIALES | 336.76/TAY/mod (Browse shelf) | Shelving location | Bibliomaps^{®} | BIBLIOG. RECOM. | 3744330269 | |||
Fuera de préstamo | 07. BIBLIOTECA CIENCIAS SOCIALES | 336.76/TAY/mod *Prof. Coronado (Browse shelf) | Not for loan | NO SE PRESTA | 3742928143 |
Enhanced descriptions from Syndetics:
This book contains several innovative models for the prices of financial assets. First published in 1986, it is a classic text in the area of financial econometrics. It presents ARCH and stochastic volatility models that are often used and cited in academic research and are applied by quantitative analysts in many banks. Another often-cited contribution of the first edition is the documentation of statistical characteristics of financial returns, which are referred to as stylized facts.This second edition takes into account the remarkable progress made by empirical researchers during the past two decades from 1986 to 2006. In the new Preface, the author summarizes this progress in two key areas: firstly, measuring, modelling and forecasting volatility; and secondly, detecting and exploiting price trends.
Índice
Bibliografía: p. 256-261
This book contains several innovative models for the prices of financial assets. First published in 1986, it is a classic text in the area of financial econometrics. It presents ARCH and stochastic volatility models that are often used and cited in academic research and are applied by quantitative analysts in many banks. Another often-cited contribution of the first edition is the documentation of statistical characteristics of financial returns, which are referred to as stylized facts. This second edition takes into account the remarkable progress made by empirical researchers during the past two decades from 1986 to 2006. In the new Preface, the author summarizes this progress in two key areas: firstly, measuring, modelling and forecasting volatility; and secondly, detecting and exploiting price trends.
Table of contents provided by Syndetics
- Preface to the 2nd edition (p. xv)
- Preface to the 1st edition (p. xxv)
- 1 Introduction (p. 1)
- 1.1 Financial time series (p. 1)
- 1.2 About this study (p. 2)
- 1.3 The world's major financial markets (p. 3)
- 1.4 Examples of daily price series (p. 4)
- 1.5 A selective review of previous research (p. 8)
- Important questions (p. 8)
- The random walk hypothesis (p. 8)
- The efficient market hypothesis (p. 10)
- 1.6 Daily returns (p. 12)
- 1.7 Models (p. 13)
- 1.8 Models in this book (p. 15)
- 1.9 Stochastic processes (p. 16)
- General remarks (p. 16)
- Stationary processes (p. 16)
- Autocorrelation (p. 17)
- Spectral density (p. 18)
- White noise (p. 19)
- ARMA processes (p. 20)
- Gaussian processes (p. 23)
- 1.10 Linear stochastic processes (p. 23)
- Their definition (p. 23)
- Autocorrelation tests (p. 24)
- 2 Features of Financial Returns (p. 26)
- 2.1 Constructing financial time series (p. 26)
- Sources (p. 26)
- Time scales (p. 27)
- Additional information (p. 27)
- Using futures contracts (p. 28)
- 2.2 Prices studied (p. 28)
- Spot prices (p. 28)
- Futures prices (p. 30)
- Commodity futures (p. 30)
- Financial futures (p. 31)
- Extended series (p. 32)
- 2.3 Average returns and risk premia (p. 32)
- Annual expected returns (p. 33)
- Common stocks and ordinary shares (p. 35)
- Spot commodities (p. 36)
- Spot currencies (p. 36)
- Commodity futures (p. 36)
- 2.4 Standard deviations (p. 38)
- Risks compared (p. 39)
- Futures and contract age (p. 40)
- 2.5 Calendar effects (p. 41)
- Day-of-the-week (p. 41)
- Stocks (p. 41)
- Currencies (p. 41)
- Agricultural futures (p. 42)
- Standard deviations (p. 42)
- Month-of-the-year effects for stocks (p. 43)
- 2.6 Skewness (p. 44)
- 2.7 Kurtosis (p. 44)
- 2.8 Plausible distributions (p. 45)
- 2.9 Autocorrelation (p. 48)
- First-lag (p. 49)
- Lags 1 to 30 (p. 50)
- Tests (p. 50)
- 2.10 Non-linear structure (p. 52)
- Not strict white noise (p. 52)
- A characteristic of returns (p. 52)
- Not linear (p. 56)
- Consequences of non-linear structure (p. 57)
- 2.11 Summary (p. 58)
- Appendix 2(A) Autocorrelation caused by day-of-the-week effects (p. 58)
- Appendix 2(B) Autocorrelations of a squared linear process (p. 60)
- 3 Modelling Price Volatility (p. 62)
- 3.1 Introduction (p. 62)
- 3.2 Elementary variance models (p. 63)
- Step change, discrete distributions (p. 63)
- Markov variances, discrete distributions (p. 64)
- Step variances, continuous distributions (p. 65)
- Markov variances, continuous distributions (p. 66)
- 3.3 A general variance model (p. 67)
- Notation (p. 69)
- 3.4 Modelling variance jumps (p. 69)
- 3.5 Modelling frequent variance changes not caused by prices (p. 70)
- General models (p. 70)
- Stationary models (p. 72)
- The lognormal, autoregressive model (p. 73)
- 3.6 Modelling frequent variance changes caused by past prices (p. 75)
- General concepts (p. 75)
- Caused by past squared returns (p. 76)
- Caused by past absolute returns (p. 78)
- ARMACH models (p. 78)
- 3.7 Modelling autocorrelation and variance changes (p. 79)
- Variances not caused by returns (p. 81)
- Variances caused by returns (p. 82)
- 3.8 Parameter estimation for variance models (p. 83)
- 3.9 Parameter estimates for product processes (p. 84)
- Lognormal AR(1) (p. 86)
- Results (p. 88)
- 3.10 Parameter estimates for ARMACH processes (p. 90)
- Results (p. 92)
- 3.11 Summary (p. 93)
- Appendix 3(A) Results for ARCH processes (p. 95)
- 4 Forecasting Standard Deviations (p. 97)
- 4.1 Introduction (p. 97)
- 4.2 Key theoretical results (p. 98)
- Uncorrelated returns (p. 98)
- Correlated returns (p. 100)
- Relative mean square errors (p. 100)
- Stationary processes (p. 100)
- 4.3 Forecasts: methodology and methods (p. 101)
- Benchmark forecast (p. 101)
- Parametric forecasts (p. 101)
- Product process forecasts (p. 102)
- ARMACH forecasts (p. 103)
- EWMA forecasts (p. 103)
- Futures forecasts (p. 104)
- Empirical RMSE (p. 105)
- 4.4 Forecasting results (p. 106)
- Absolute returns (p. 106)
- Conditional standard deviations (p. 107)
- Two leading forecasts (p. 108)
- More distant forecasts (p. 108)
- Conclusions about stationarity (p. 110)
- Another approach (p. 110)
- 4.5 Recommended forecasts for the next day (p. 110)
- Examples (p. 113)
- 4.6 Summary (p. 114)
- 5 The Accuracy of Autocorrelation Estimates (p. 116)
- 5.1 Introduction (p. 116)
- 5.2 Extreme examples (p. 117)
- 5.3 A special null hypothesis (p. 118)
- 5.4 Estimates of the variances of sample autocorrelations (p. 119)
- 5.5 Some asymptotic results (p. 120)
- Linear processes (p. 121)
- Non-linear processes (p. 122)
- 5.6 Interpreting the estimates (p. 123)
- 5.7 The estimates for returns (p. 124)
- 5.8 Accurate autocorrelation estimates (p. 126)
- Rescaled returns (p. 127)
- Variance estimates for recommended coefficients (p. 128)
- Exceptional series (p. 130)
- 5.9 Simulation results (p. 130)
- 5.10 Autocorrelations of rescaled processes (p. 131)
- 5.11 Summary (p. 132)
- 6 Testing the Random Walk Hypothesis (p. 133)
- 6.1 Introduction (p. 133)
- 6.2 Test methodology (p. 134)
- 6.3 Distributions of sample autocorrelations (p. 135)
- Asymptotic limits (p. 136)
- Finite samples (p. 136)
- 6.4 A selection of test statistics (p. 137)
- Autocorrelation tests (p. 137)
- Spectral tests (p. 138)
- The runs test (p. 140)
- 6.5 The price-trend hypothesis (p. 141)
- Price-trend autocorrelations (p. 141)
- An example (p. 142)
- Price-trend spectral density (p. 143)
- 6.6 Tests for random walks versus price-trends (p. 143)
- 6.7 Consequences of data errors (p. 145)
- 6.8 Results of random walk tests (p. 146)
- Stocks (p. 150)
- Commodities and currencies (p. 152)
- About the rest of this chapter (p. 156)
- 6.9 Some test results for returns (p. 157)
- 6.10 Power comparisons (p. 159)
- 6.11 Testing equilibrium models (p. 161)
- Stocks (p. 161)
- Simulation results (p. 163)
- Tests (p. 165)
- Other equilibrium models (p. 166)
- Conclusion (p. 166)
- 6.12 Institutional effects (p. 167)
- Limit rules (p. 167)
- Bid-ask spreads (p. 169)
- 6.13 Results for subdivided series (p. 169)
- 6.14 Conclusions (p. 170)
- 6.15 Summary (p. 172)
- Appendix 6(A) Correlation between test values for two related series (p. 172)
- 7 Forecasting Trends in Prices (p. 174)
- 7.1 Introduction (p. 174)
- 7.2 Price-trend models (p. 174)
- A non-linear trend model (p. 176)
- A linear trend model (p. 176)
- 7.3 Estimating the trend parameters (p. 178)
- Methods (p. 178)
- Futures (p. 179)
- Spots (p. 181)
- Accuracy (p. 183)
- 7.4 Some results from simulations (p. 183)
- Estimates (p. 183)
- A puzzle solved (p. 185)
- 7.5 Forecasting returns: theoretical results (p. 185)
- The next return (p. 186)
- More distant returns (p. 187)
- Sums of future returns (p. 187)
- 7.6 Empirical forecasting results (p. 188)
- Benchmark forecasts (p. 188)
- Price-trend forecasts (p. 189)
- Summary statistics (p. 189)
- Futures (p. 190)
- Spots (p. 192)
- 7.7 Further forecasting theory (p. 193)
- Expected changes in prices (p. 193)
- Forecasting the direction of the trend (p. 194)
- Forecasting prices (p. 194)
- 7.8 Summary (p. 194)
- 8 Evidence Against the Efficiency of Futures Markets (p. 196)
- 8.1 Introduction (p. 196)
- 8.2 The efficient market hypothesis (p. 197)
- 8.3 Problems raised by previous studies (p. 199)
- Filter rules (p. 199)
- Benchmarks (p. 200)
- Significance (p. 201)
- Optimization (p. 201)
- 8.4 Problems measuring risk and return (p. 201)
- Returns (p. 201)
- Risk (p. 202)
- Necessary assumptions (p. 203)
- 8.5 Trading conditions (p. 203)
- 8.6 Theoretical analysis (p. 204)
- Trading strategies (p. 204)
- Assumptions (p. 205)
- Conditions for trading profits (p. 206)
- Inefficient regions (p. 207)
- Some implications (p. 209)
- 8.7 Realistic strategies and assumptions (p. 210)
- Strategies (p. 211)
- Assumptions (p. 212)
- Notes on objectives (p. 213)
- 8.8 Trading simulated contracts (p. 213)
- Commodities (p. 214)
- Currencies (p. 215)
- 8.9 Trading results for futures (p. 216)
- Calibration contracts (p. 216)
- Test contracts (p. 217)
- Portfolio results (p. 222)
- 8.10 Towards conclusions (p. 223)
- 8.11 Summary (p. 224)
- 9 Valuing Options (p. 225)
- 9.1 Introduction (p. 225)
- 9.2 Black-Scholes option pricing formulae (p. 226)
- 9.3 Evaluating standard formulae (p. 227)
- 9.4 Call values when conditional variances change (p. 228)
- Formulae for a stationary process (p. 228)
- Examples (p. 230)
- Non-stationary processes (p. 233)
- Conclusions (p. 233)
- 9.5 Price trends and call values (p. 234)
- A formula for trend models (p. 234)
- Examples (p. 235)
- 9.6 Summary (p. 237)
- 10 Concluding Remarks (p. 238)
- 10.1 Price behaviour (p. 238)
- 10.2 Advice to traders (p. 239)
- 10.3 Further research (p. 240)
- 10.4 Stationary models (p. 241)
- Random walks (p. 241)
- Price trends (p. 242)
- Appendix A computer Program for Modelling Financial Time Series (p. 243)
- Output produced (p. 243)
- Computer time required (p. 244)
- User-defined parameters (p. 244)
- Optional parameters (p. 245)
- Input requirements (p. 245)
- About the subroutines (p. 247)
- FORTRAN program (p. 248)
- References (p. 256)
- Author index (p. 262)
- Subject index (p. 264)
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