LEADER 02948oam 2200481 450 001 996418198903316 005 20210415135633.0 010 $a981-15-8864-3 024 7 $a10.1007/978-981-15-8864-8 035 $a(CKB)4100000011513691 035 $a(DE-He213)978-981-15-8864-8 035 $a(MiAaPQ)EBC6380824 035 $a(PPN)262174650 035 $a(EXLCZ)994100000011513691 100 $a20210415d2020 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStochastic analysis /$fShigeo Kusuoka 205 $a1st ed. 2020. 210 1$aSingapore :$cSpringer,$d[2020] 210 4$d©2020 215 $a1 online resource (XII, 218 p. 1 illus.) 225 0 $aMonographs in Mathematical Economics,$x2364-8287 ;$vVolume 3 311 $a981-15-8863-5 327 $aChapter 1. Preparations from probability theory -- Chapter 2. Martingale with discrete parameter -- Chapter 3. Martingale with continuous parameter -- Chapter 4. Stochastic integral -- Chapter 5. Applications of stochastic integral -- Chapter 6. Stochastic differential equation -- Chapter 7. Application to finance -- Chapter 8. Appendices -- References. 330 $aThis book is intended for university seniors and graduate students majoring in probability theory or mathematical finance. In the first chapter, results in probability theory are reviewed. Then, it follows a discussion of discrete-time martingales, continuous time square integrable martingales (particularly, continuous martingales of continuous paths), stochastic integrations with respect to continuous local martingales, and stochastic differential equations driven by Brownian motions. In the final chapter, applications to mathematical finance are given. The preliminary knowledge needed by the reader is linear algebra and measure theory. Rigorous proofs are provided for theorems, propositions, and lemmas. In this book, the definition of conditional expectations is slightly different than what is usually found in other textbooks. For the Doob?Meyer decomposition theorem, only square integrable submartingales are considered, and only elementary facts of the square integrable functions are used in the proof. In stochastic differential equations, the Euler?Maruyama approximation is used mainly to prove the uniqueness of martingale problems and the smoothness of solutions of stochastic differential equations. . 410 0$aMonographs in Mathematical Economics,$x2364-8279 ;$v3 606 $aStochastic analysis 606 $aBusiness mathematics 615 0$aStochastic analysis. 615 0$aBusiness mathematics. 676 $a519.2 700 $aKusuoka$b Shigeo$060659 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bUtOrBLW 906 $aBOOK 912 $a996418198903316 996 $aStochastic analysis$92547559 997 $aUNISA