Probability Theory II
Description
This book offers a modern approach to the theory of continuous-time stochastic processes and stochastic calculus. The content is treated rigorously, comprehensively, and independently. In the first part, the theory of Markov processes and martingales is introduced, with a focus on Brownian motion and the Poisson process. Subsequently, the theory of stochastic integration for continuous semimartingales was developed. A substantial portion is dedicated to stochastic differential equations, the main results of solvability and uniqueness in weak and strong sense, linear stochastic equations, and their relation to deterministic partial differential equations. Each chapter is accompanied by numerous examples. This text stems from over twenty years of teaching experience in stochastic processes and calculus within master's degrees in mathematics, quantitative finance, and postgraduate courses in mathematics for applications and mathematical finance at the University of Bologna. The book provides material for at least two semester-long courses in scientific studies (Mathematics, Physics, Engineering, Statistics, Economics, etc.) and aims to provide a solid background for those interested in the development of stochastic calculus theory and its applications. This text completes the journey started with the first volume of Probability Theory I - Random Variables and Distributions, through a selection of advanced classic topics in stochastic analysis.
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Andrea Pascucci is a professor of Probability and Mathematical Statistics at the Alma Mater Studiorum - University of Bologna. His research activity encompasses various aspects of the theory of stochastic differential equations for diffusions and jump processes, degenerate partial differential equations, and their applications to mathematical finance. He has authored 6 books and over 80 scientific articles on the following topics: linear and nonlinear Kolmogorov-Fokker-Planck equations; regularity and asymptotic estimates of transition densities for multidimensional diffusions and jump processes; free boundary problems, optimal stopping, and applications to American-style financial derivatives; Asian options and volatility models. He has been invited as a speaker at more than 40 international conferences. He serves as an editor for the Journal of Computational Finance and is the director of a postgraduate program in Mathematical Finance at the University of Bologna.
Content
1 Stochastic processes.- 2 Markov processes.- 3 Continuous processes.- 4 Brownian motion.- 5 Poisson process.- 6 Stopping times.- 7 Strong Markov property.- 8 Continuous martingales.- 9 Theory of variation.- 10 Stochastic integral.- 11 Itô's formula.- 12 Multidimensional stochastic calculus.- 13 Change of measure and martingale representation.- 14 Stochastic differential equations.- 15 Feynman-Kac formulas.- 16 Linear stochastic equations.- 17 Strong solutions.- 18 Weak solutions.- 19 Complements.-20 A primer on parabolic PDEs.