Book contents
- Frontmatter
- Contents
- 1 Introduction
- Part One Consistencies
- 2 Strong Markov Consistency of Multivariate Markov Families and Processes
- 3 Consistency of Finite Multivariate Markov Chains
- 4 Consistency of Finite Multivariate Conditional Markov Chains
- 5 Consistency of Multivariate Special Semimartingales
- Part Two Structures
- Part Three Further Developments
- Part Four Applications of Stochastic Structures
- Appendices
- References
- Notation Index
- Subject Index
5 - Consistency of Multivariate Special Semimartingales
from Part One - Consistencies
Published online by Cambridge University Press: 18 September 2020
- Frontmatter
- Contents
- 1 Introduction
- Part One Consistencies
- 2 Strong Markov Consistency of Multivariate Markov Families and Processes
- 3 Consistency of Finite Multivariate Markov Chains
- 4 Consistency of Finite Multivariate Conditional Markov Chains
- 5 Consistency of Multivariate Special Semimartingales
- Part Two Structures
- Part Three Further Developments
- Part Four Applications of Stochastic Structures
- Appendices
- References
- Notation Index
- Subject Index
Summary
In this chapter we introduce and discuss various concepts of consistency for multivariate special semimartingales. The results here are mainly based on Theorem 5.1, which generalizes to the case of semimartingales that are not special. Thus, these results themselves generalize in a straightforward manner to the case of semimartingales that are not special. We chose to work with special semimartingales in order to ease somewhat the presentation. Throughout this chapter the semimartingale truncation functions will be considered to be standard truncation functions of appropriate dimensions. In what follows, the semimartingale characteristics will be always computed with respect to the relevant standard truncation functions. Thus, the semimartingale characteristics for all the semimartingales showing in the rest of this chapter are considered to be unique (as functions of the trajectories on the canonical space) once the filtration is chosen with respect to which the characteristics are computed. The theory is illustrated by various examples.
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- Structured Dependence between Stochastic Processes , pp. 67 - 76Publisher: Cambridge University PressPrint publication year: 2020