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001448723 001__ 1448723
001448723 003__ OCoLC
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001448723 019__ $$a1340032544
001448723 020__ $$a9783031038617$$q(electronic bk.)
001448723 020__ $$a3031038614$$q(electronic bk.)
001448723 020__ $$z3031038606
001448723 020__ $$z9783031038600
001448723 0247_ $$a10.1007/978-3-031-03861-7$$2doi
001448723 035__ $$aSP(OCoLC)1340959588
001448723 040__ $$aEBLCP$$beng$$epn$$cEBLCP$$dGW5XE$$dOCLCQ$$dYDX$$dEBLCP$$dOCLCF$$dSFB$$dOCLCQ
001448723 049__ $$aISEA
001448723 050_4 $$aQA276.8
001448723 08204 $$a519.5/44$$223/eng/20220817
001448723 1001_ $$aBishwal, Jaya P. N.
001448723 24510 $$aParameter estimation in stochastic volatility models /$$cJaya P.N. Bishwal.
001448723 260__ $$aCham :$$bSpringer,$$c2022.
001448723 300__ $$a1 online resource (634 pages)
001448723 336__ $$atext$$btxt$$2rdacontent
001448723 337__ $$acomputer$$bc$$2rdamedia
001448723 338__ $$aonline resource$$bcr$$2rdacarrier
001448723 5050_ $$aStochastic Volatility Models: Methods of Pricing, Hedging and Estimation -- Sequential Monte Carlo Methods -- Parameter Estimation in the Heston Model -- Fractional Ornstein-Uhlenbeck Processes, Levy-Ornstein-Uhlenbeck Processes and Fractional Levy-Ornstein-Uhlenbeck Processes -- Inference for General Semimartingales and Selfsimilar Processes -- Estimation in Gamma-Ornstein-Uhlenbeck Stochastic Volatility Model -- Berry-Esseen Inequalities for the Functional Ornstein-Uhlenbeck-Inverse-Gaussian Process -- Maximum Quasi-likelihood Estimation in Fractional Levy Stochastic Volatility Model -- Estimation in Barndorff-Neilsen-Shephard Ornstein-Uhlenbeck Stochastic Volatility Model -- Parameter Estimation in Student Ornstein-Uhlenbeck Model -- Berry-Esseen Asymptotics for Pearson Diffusions -- Bayesian Maximum Likelihood Estimation in Fractional Stochastic Volatility Models -- Berry-Esseen-Stein-Malliavin Theory for Fractional Ornstein-Uhlenbeck Process -- Approximate Maximum Likelihood Estimation for Sub-fractional Hybrid Stochastic Volatility Model -- Appendix.
001448723 506__ $$aAccess limited to authorized users.
001448723 520__ $$aThis book develops alternative methods to estimate the unknown parameters in stochastic volatility models, offering a new approach to test model accuracy. While there is ample research to document stochastic differential equation models driven by Brownian motion based on discrete observations of the underlying diffusion process, these traditional methods often fail to estimate the unknown parameters in the unobserved volatility processes. This text studies the second order rate of weak convergence to normality to obtain refined inference results like confidence interval, as well as nontraditional continuous time stochastic volatility models driven by fractional Levy processes. By incorporating jumps and long memory into the volatility process, these new methods will help better predict option pricing and stock market crash risk. Some simulation algorithms for numerical experiments are provided.
001448723 588__ $$aDescription based on print version record.
001448723 650_0 $$aParameter estimation.
001448723 650_0 $$aStochastic differential equations.
001448723 655_7 $$aLlibres electrònics.$$2thub
001448723 655_0 $$aElectronic books.
001448723 77608 $$iPrint version:$$aBishwal, Jaya P.N.$$tParameter Estimation in Stochastic Volatility Models.$$dCham : Springer International Publishing AG, ©2022$$z9783031038600
001448723 852__ $$bebk
001448723 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-03861-7$$zOnline Access$$91397441.1
001448723 909CO $$ooai:library.usi.edu:1448723$$pGLOBAL_SET
001448723 980__ $$aBIB
001448723 980__ $$aEBOOK
001448723 982__ $$aEbook
001448723 983__ $$aOnline
001448723 994__ $$a92$$bISE