Semi-Markov Risk Models for Finance, Insurance and Reliability (Google eBook)
Springer Science & Business Media, May 15, 2007 - Business & Economics - 448 pages
This book presents applications of semi-Markov processes in finance, insurance and reliability, using real-life problems as examples. After a presentation of the main probabilistic tools necessary for understanding of the book, the authors show how to apply semi-Markov processes in finance, starting from the axiomatic definition and continuing eventually to the most advanced financial tools, particularly in insurance and in risk-and-ruin theories. Also considered are reliability problems that interact with credit risk theory in finance. The unique approach of this book is to solve finance and insurance problems with semi-Markov models in a complete way and furthermore present real-life applications of semi-Markov processes.
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AA A BBB AAA AA assumption asymptotic basic BB B CCC BBB BB Black and Scholes Brownian motion called Chapter compute conditional expectation conditional probability defined Definition discrete distribution function DTHSMP environment ergodic evolution equations example finite given gives ij ij independent interest rate introduce Janssen and Manca kernel Q Laplace transform Let us consider Markov chain Markov process Markov renewal martingale mean moreover non-homogeneous normal distribution notation obtain option parameters pension fund period possible present value probability space problem Proposition random variables random walk recurrent renewal process renewal theory represents the probability risk model ruin probability semi-Markov model semi-Markov process seniority sequence stochastic process suppose Table theorem total number transition matrix transition probabilities underlying asset vector λβ ω ω