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008 150903s2008 xxk| o |||| 0|eng d
020 _a9781848000032
_99781848000032
024 7 _a10.1007/9781848000032
_2doi
035 _avtls000344117
039 9 _a201509030406
_bVLOAD
_c201405050302
_dVLOAD
_y201402061248
_zstaff
040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
050 4 _aQA273.A1-274.9
100 1 _aSchmidli, Hanspeter.
_eautor
_9323180
245 1 0 _aStochastic Control in Insurance /
_cby Hanspeter Schmidli.
264 1 _aLondon :
_bSpringer London,
_c2008.
300 _brecurso en línea.
336 _atexto
_btxt
_2rdacontent
337 _acomputadora
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
_2rda
490 0 _aProbability and Its Applications,
_x1431-7028
500 _aSpringer eBooks
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.
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9781848000025
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-1-84800-003-2
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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