Discrete Time Series, Processes, and Applications in Finance
Most financial and investment decisions are based on considerations of possible future changes and require forecasts on the evolution of the financial world. Time series and processes are the natural tools for describing the dynamic behavior of financial data, leading to the required forecasts. This...
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| Language: | English |
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Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2013.
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| Edition: | 1st ed. 2013. |
| Series: | Springer Finance,
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| Online Access: | https://doi.org/10.1007/978-3-642-31742-2 |
Table of Contents:
- Preface
- List of Figures.-List of Tables
- 1. Introduction
- 2.Notation, naming and general definitions
- 3.Stylized facts
- 4.Empirical mug shots
- 5.Process Overview
- 6.Logarithmic versus relative random walks
- 7.ARCH processes
- 8.Stochastic volatility processes
- 9.Regime switching process
- 10.Price and volatility using high-frequency data
- 11.Time reversal asymmetry
- 12.Characterizing heteroskedasticity
- 13.The innovation distributions
- 14.Leverage effect
- 15.Processes and market risk evaluation
- 16.Option pricing
- 17.Properties of large covariance matrices
- 18.Multivariate ARCH processes
- 19.The processes compatible with the stylized facts
- 20.Further thoughts.-Bibliography
- Index.