LEADER 05666nam 2200769 a 450 001 9910814678603321 005 20200520144314.0 010 $a1-283-37237-1 010 $a9786613372376 010 $a1-118-46737-X 010 $a0-470-72213-4 035 $a(CKB)2550000000073863 035 $a(EBL)698224 035 $a(OCoLC)768731745 035 $a(SSID)ssj0000566782 035 $a(PQKBManifestationID)11349962 035 $a(PQKBTitleCode)TC0000566782 035 $a(PQKBWorkID)10552498 035 $a(PQKB)11681057 035 $a(MiAaPQ)EBC698224 035 $a(Au-PeEL)EBL698224 035 $a(CaPaEBR)ebr10518735 035 $a(CaONFJC)MIL337237 035 $a(PPN)188938516 035 $a(EXLCZ)992550000000073863 100 $a20080904d2008 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aStochastic simulation and applications in finance with MATLAB programs$b[electronic resource] /$fHuu Tue Huynh, Van Son Lai and Issouf Soumare? 210 $aChichester, England ;$aHoboken, N.J. $cJohn Wiley & Sons$dc2008 215 $a1 online resource (356 p.) 225 1 $aWiley finance 300 $aDescription based upon print version of record. 311 $a0-470-72538-9 320 $aIncludes bibliographical references and index. 327 $aStochastic Simulation and Applications in Finance with MATLABŪ Programs; Contents; Preface; 1 Introduction to Probability; 1.1 Intuitive Explanation; 1.1.1 Frequencies; 1.1.2 Number of Favorable Cases Over The Total Number of Cases; 1.2 Axiomatic Definition; 1.2.1 Random Experiment; 1.2.2 Event; 1.2.3 Algebra of Events; 1.2.4 Probability Axioms; 1.2.5 Conditional Probabilities; 1.2.6 Independent Events; 2 Introduction to Random Variables; 2.1 Random Variables; 2.1.1 Cumulative Distribution Function; 2.1.2 Probability Density Function 327 $a2.1.3 Mean, Variance and Higher Moments of a Random Variable2.1.4 Characteristic Function of a Random Variable; 2.2 Random vectors; 2.2.1 Cumulative Distribution Function of a Random Vector; 2.2.2 Probability Density Function of a Random Vector; 2.2.3 Marginal Distribution of a Random Vector; 2.2.4 Conditional Distribution of a Random Vector; 2.2.5 Mean, Variance and Higher Moments of a Random Vector; 2.2.6 Characteristic Function of a Random Vector; 2.3 Transformation of Random Variables; 2.4 Transformation of Random Vectors 327 $a2.5 Approximation of the Standard Normal Cumulative Distribution Function3 Random Sequences; 3.1 Sum of Independent Random Variables; 3.2 Law of Large Numbers; 3.3 Central Limit Theorem; 3.4 Convergence of Sequences of Random Variables; 3.4.1 Sure Convergence; 3.4.2 Almost Sure Convergence; 3.4.3 Convergence in Probability; 3.4.4 Convergence in Quadratic Mean; 4 Introduction to Computer Simulation of Random Variables; 4.1 Uniform Random Variable Generator; 4.2 Generating Discrete Random Variables; 4.2.1 Finite Discrete Random Variables 327 $a4.2.2 Infinite Discrete Random Variables: Poisson Distribution4.3 Simulation of Continuous Random Variables; 4.3.1 Cauchy Distribution; 4.3.2 Exponential Law; 4.3.3 Rayleigh Random Variable; 4.3.4 Gaussian Distribution; 4.4 Simulation of Random Vectors; 4.4.1 Case of a Two-Dimensional Random Vector; 4.4.2 Cholesky Decomposition of the Variance-Covariance Matrix; 4.4.3 Eigenvalue Decomposition of the Variance-Covariance Matrix; 4.4.4 Simulation of a Gaussian Random Vector with MATLAB; 4.5 Acceptance-Rejection Method; 4.6 Markov Chain Monte Carlo Method (MCMC) 327 $a4.6.1 Definition of a Markov Process4.6.2 Description of the MCMC Technique; 5 Foundations of Monte Carlo Simulations; 5.1 Basic Idea; 5.2 Introduction to the Concept of Precision; 5.3 Quality of Monte Carlo Simulations Results; 5.4 Improvement of the Quality of Monte Carlo Simulations or Variance Reduction Techniques; 5.4.1 Quadratic Resampling; 5.4.2 Reduction of the Number of Simulations Using Antithetic Variables; 5.4.3 Reduction of the Number of Simulations Using Control Variates; 5.4.4 Importance Sampling; 5.5 Application Cases of Random Variables Simulations 327 $a5.5.1 Application Case: Generation of Random Variables as a Function of the Number of Simulations 330 $aStochastic Simulation and Applications in Finance with MATLAB Programs explains the fundamentals of Monte Carlo simulation techniques, their use in the numerical resolution of stochastic differential equations and their current applications in finance. Building on an integrated approach, it provides a pedagogical treatment of the need-to-know materials in risk management and financial engineering. The book takes readers through the basic concepts, covering the most recent research and problems in the area, including: the quadratic re-sampling technique, the Least Squared Method, the d 410 0$aWiley finance series. 606 $aFinance$xMathematical models 606 $aStochastic models 615 0$aFinance$xMathematical models. 615 0$aStochastic models. 676 $a332.01/51923 676 $a332.0151923 686 $aDAT 306f$2stub 686 $aMAT 605f$2stub 686 $aQP 890$2rvk 686 $aST 601 M35$2rvk 686 $aWIR 160f$2stub 700 $aHuynh$b Huu Tue$0611231 701 $aLai$b Van Son$0611232 701 $aSoumare?$b Issouf$0611233 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910814678603321 996 $aStochastic simulation and applications in finance with MATLAB programs$91137149 997 $aUNINA