2005
DOI: 10.1142/p386
|View full text |Cite
|
Sign up to set email alerts
|

Introduction to Stochastic Calculus with Applications

Abstract: Preface to the Second EditionThe second edition is revised, expanded and enhanced. This is now a more complete text in Stochastic Calculus, from both a theoretical and an applications point of view. Changes came about, as a result of using this book for teaching courses in Stochastic Calculus and Financial Mathematics over a number of years. Many topics are expanded with more worked out examples and exercises. Solutions to selected exercises are included. A new chapter on bonds and interest rates contains deri… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
192
0
9

Year Published

2010
2010
2016
2016

Publication Types

Select...
5
4

Relationship

0
9

Authors

Journals

citations
Cited by 422 publications
(201 citation statements)
references
References 0 publications
0
192
0
9
Order By: Relevance
“…A system of Stochastic differential equations which arise when a random noise is introduced into ordering differential equations, [7]:…”
Section: Stochastic Differentiation and Integrationmentioning
confidence: 99%
“…A system of Stochastic differential equations which arise when a random noise is introduced into ordering differential equations, [7]:…”
Section: Stochastic Differentiation and Integrationmentioning
confidence: 99%
“…85,86 Roughly speaking, a martingale is a stochastic process M (t) whose expectation value is constant in time: ∂ t E[M (t)] = 0. For this reason we call consequences of Eq.…”
Section: Martingale Conditions On Partition Functionsmentioning
confidence: 99%
“…Now we set t = 0 in (78). It is then straightforward to use the stochastic equations (61) and (70) and Ito's formula 85,86 to transform this equation into a Fokker-Planck equation:…”
Section: Martingale Conditions On Partition Functionsmentioning
confidence: 99%
“…where the drift [12]). A well-known method for solving the boundary crossing problem for diffusion processes is to express them as functions of a Brownian motion, and the boundary crossing probability for diffusion processes is equivalent to a boundary crossing probability for the Brownian motion with transformed time interval and boundaries.…”
Section: Dx(t) = B(t X(t)) Dt + σ (T X(t)) Dw (T)mentioning
confidence: 99%