000 03698nam a22005775i 4500
001 978-1-84800-003-2
003 DE-He213
005 20180115171544.0
007 cr nn 008mamaa
008 100301s2008 xxk| s |||| 0|eng d
020 _a9781848000032
_9978-1-84800-003-2
024 7 _a10.1007/978-1-84800-003-2
_2doi
050 4 _aHG8779-8793
072 7 _aKFFN
_2bicssc
072 7 _aBUS033000
_2bisacsh
082 0 4 _a368.01
_223
100 1 _aSchmidli, Hanspeter.
_eauthor.
245 1 0 _aStochastic Control in Insurance
_h[electronic resource] /
_cby Hanspeter Schmidli.
264 1 _aLondon :
_bSpringer London,
_c2008.
300 _aXVI, 258 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aProbability and Its Applications,
_x1431-7028
505 0 _aStochastic Control in Discrete Time -- Stochastic Control in Continuous Time -- Problems in Life Insurance -- Asymptotics of Controlled Risk Processes -- Appendices -- Stochastic Processes and Martingales -- Markov Processes and Generators -- Change of Measure Techniques -- Risk Theory -- The Black-Scholes Model -- Life Insurance -- References -- Index -- List of Principal Notation.
520 _aStochastic control is one of the methods being used to find optimal decision-making strategies in fields such as operations research and mathematical finance. In recent years, stochastic control techniques have been applied to non-life insurance problems, and in life insurance the theory has been further developed. This book provides a systematic treatment of optimal control methods applied to problems from insurance and investment, complete with detailed proofs. The theory is discussed and illustrated by way of examples, using concrete simple optimisation problems that occur in the actuarial sciences. The problems come from non-life insurance as well as life and pension insurance and also cover the famous Merton problem from mathematical finance. Wherever possible, the proofs are probabilistic but in some cases well-established analytical methods are used. The book is directed towards graduate students and researchers in actuarial science and mathematical finance who want to learn stochastic control within an insurance setting, but it will also appeal to applied probabilists interested in the insurance applications and to practitioners who want to learn more about how the method works. Readers should be familiar with basic probability theory and have a working knowledge of Brownian motion, Markov processes, martingales and stochastic calculus. Some knowledge of measure theory will also be useful for following the proofs.
650 0 _aMathematics.
650 0 _aFinance.
650 0 _aActuarial science.
650 0 _aMathematical optimization.
650 0 _aCalculus of variations.
650 0 _aProbabilities.
650 0 _aControl engineering.
650 0 _aRobotics.
650 0 _aMechatronics.
650 1 4 _aMathematics.
650 2 4 _aActuarial Sciences.
650 2 4 _aProbability Theory and Stochastic Processes.
650 2 4 _aCalculus of Variations and Optimal Control; Optimization.
650 2 4 _aOptimization.
650 2 4 _aFinance, general.
650 2 4 _aControl, Robotics, Mechatronics.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781848000025
830 0 _aProbability and Its Applications,
_x1431-7028
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-84800-003-2
912 _aZDB-2-SMA
999 _c370981
_d370981