LEADER 05825nam 2200769Ia 450 001 9910133450703321 005 20220629163455.0 010 $a1-283-40519-9 010 $a9786613405197 010 $a1-119-99008-4 010 $a1-119-99007-6 035 $a(CKB)3400000000000354 035 $a(EBL)675281 035 $a(SSID)ssj0000477787 035 $a(PQKBManifestationID)11300014 035 $a(PQKBTitleCode)TC0000477787 035 $a(PQKBWorkID)10513679 035 $a(PQKB)10017070 035 $a(WaSeSS)IndRDA00117721 035 $a(Au-PeEL)EBL675281 035 $a(CaPaEBR)ebr10510385 035 $a(CaONFJC)MIL340519 035 $a(CaSebORM)9781119990208 035 $a(MiAaPQ)EBC675281 035 $a(OCoLC)705354523 035 $a(EXLCZ)993400000000000354 100 $a20101028d2011 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aOption pricing and estimation of financial models with R$b[electronic resource] /$fStefano M. Iacus 205 $a1st edition 210 $aChichester, West Sussex, U.K. $cWiley$d2011 215 $a1 online resource (474 p.) 300 $aDescription based upon print version of record. 311 $a1-119-99020-3 311 $a0-470-74584-3 320 $aIncludes bibliographical references and index. 327 $aOption Pricing and Estimation of Financial Models with R; Contents; Preface; 1 A synthetic view; 1.1 The world of derivatives; 1.1.1 Different kinds of contracts; 1.1.2 Vanilla options; 1.1.3 Why options?; 1.1.4 A variety of options; 1.1.5 How to model asset prices; 1.1.6 One step beyond; 1.2 Bibliographical notes; References; 2 Probability, random variables and statistics; 2.1 Probability; 2.1.1 Conditional probability; 2.2 Bayes' rule; 2.3 Random variables; 2.3.1 Characteristic function; 2.3.2 Moment generating function; 2.3.3 Examples of random variables; 2.3.4 Sum of random variables 327 $a2.3.5 Infinitely divisible distributions2.3.6 Stable laws; 2.3.7 Fast Fourier Transform; 2.3.8 Inequalities; 2.4 Asymptotics; 2.4.1 Types of convergences; 2.4.2 Law of large numbers; 2.4.3 Central limit theorem; 2.5 Conditional expectation; 2.6 Statistics; 2.6.1 Properties of estimators; 2.6.2 The likelihood function; 2.6.3 Efficiency of estimators; 2.6.4 Maximum likelihood estimation; 2.6.5 Moment type estimators; 2.6.6 Least squares method; 2.6.7 Estimating functions; 2.6.8 Confidence intervals; 2.6.9 Numerical maximization of the likelihood; 2.6.10 The ?-method; 2.7 Solution to exercises 327 $a2.8 Bibliographical notesReferences; 3 Stochastic processes; 3.1 Definition and first properties; 3.1.1 Measurability and filtrations; 3.1.2 Simple and quadratic variation of a process; 3.1.3 Moments, covariance, and increments of stochastic processes; 3.2 Martingales; 3.2.1 Examples of martingales; 3.2.2 Inequalities for martingales; 3.3 Stopping times; 3.4 Markov property; 3.4.1 Discrete time Markov chains; 3.4.2 Continuous time Markov processes; 3.4.3 Continuous time Markov chains; 3.5 Mixing property; 3.6 Stable convergence; 3.7 Brownian motion; 3.7.1 Brownian motion and random walks 327 $a3.7.2 Brownian motion is a martingale3.7.3 Brownian motion and partial differential equations; 3.8 Counting and marked processes; 3.9 Poisson process; 3.10 Compound Poisson process; 3.11 Compensated Poisson processes; 3.12 Telegraph process; 3.12.1 Telegraph process and partial differential equations; 3.12.2 Moments of the telegraph process; 3.12.3 Telegraph process and Brownian motion; 3.13 Stochastic integrals; 3.13.1 Properties of the stochastic integral; 3.13.2 Ito? formula; 3.14 More properties and inequalities for the Ito? integral; 3.15 Stochastic differential equations 327 $a3.15.1 Existence and uniqueness of solutions3.16 Girsanov's theorem for diffusion processes; 3.17 Local martingales and semimartingales; 3.18 Le?vy processes; 3.18.1 Le?vy-Khintchine formula; 3.18.2 Le?vy jumps and random measures; 3.18.3 Ito?-Le?vy decomposition of a Le?vy process; 3.18.4 More on the Le?vy measure; 3.18.5 The Ito? formula for Le?vy processes; 3.18.6 Le?vy processes and martingales; 3.18.7 Stochastic differential equations with jumps; 3.18.8 Ito? formula for Le?vy driven stochastic differential equations; 3.19 Stochastic differential equations in Rn; 3.20 Markov switching diffusions 327 $a3.21 Solution to exercises 330 $aPresents inference and simulation of stochastic process in the field of model calibration for financial times series modelled by continuous time processes and numerical option pricing. Introduces the bases of probability theory and goes on to explain how to model financial times series with continuous models, how to calibrate them from discrete data and further covers option pricing with one or more underlying assets based on these models. Analysis and implementation of models goes beyond the standard Black and Scholes framework and includes Markov switching models, Le?vy models and other mod 606 $aOptions (Finance)$xPrices 606 $aProbabilities 606 $aStochastic processes 606 $aTime-series analysis 606 $aR (Computer program language) 615 0$aOptions (Finance)$xPrices. 615 0$aProbabilities. 615 0$aStochastic processes. 615 0$aTime-series analysis. 615 0$aR (Computer program language). 676 $a332.64/53 686 $aMAT029000$2bisacsh 700 $aIacus$b Stefano M$g(Stefano Maria)$0874215 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910133450703321 996 $aOption pricing and estimation of financial models with R$91977231 997 $aUNINA