000			05497cam a22004218i 4500
001			on1264730485
003			OCoLC
005			20240726104841.0
008			210816s2021 nyu ob 001 0 eng
010			_a2021038486
040			_aDLC _beng _erda _cDLC _dOCLCO _dEBLCP _dOCLCF _dNT
020			_a9781536198492 _q((electronic)l(electronic)ctronic)
042			_apcc
050	0	0	_aQA274 _b.A675 2021
049			_aMAIN
245	1	0	_aApplications of Lévy processes /edited by Oleg Kudryavtsev, Southern Federal University, Rostov-on-Don, Russia; Rostov Branch of the Russian Customs Academy, Rostov-on-Don, Russia, Antonino Zanette, Department of Economics and Statistics, University of Udine, Udine, Italy.
300			_a1 online resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_adata file _2rda
490	0		_aMathematics research developments
504			_a2
520	0		_a"Lévy processes have found applications in various fields, including physics, chemistry, long-term climate change, telephone communication, and finance. The most famous Lévy process in finance is the Black-Scholes model. This book presents important financial applications of Lévy processes. The Editors consider jump-diffusion and pure non-Gaussian Lévy processes, the multi-dimensional Black-Scholes model, and regime-switching Lévy models. This book is comprised of seven chapters that focus on different approaches to solving applied problems under Lévy processes: Monte Carlo simulations, machine learning, the frame projection method, dynamic programming, the Fourier cosine series expansion, finite difference schemes, and the Wiener-Hopf factorization. Various numerical examples are carefully presented in tables and figures to illustrate the methods designed in the book"-- _cProvided by publisher.
505	0	0	_aIntro -- _tAPPLICATIONS OFLÉVY PROCESSES -- _tAPPLICATIONS OFLÉVY PROCESSES -- _tCONTENTS -- _tPREFACE -- _tChapter 1VARIANCE REDUCTION APPLIED TOMACHINE LEARNING FOR PRICINGBERMUDAN/AMERICAN OPTIONSIN HIGH DIMENSION -- _tAbstract -- _t1. INTRODUCTION -- _t2. AMERICAN OPTIONS IN THE MULTI-DIMENSIONAL BLACK-SCHOLES MODEL -- _t3. MACHINE LEARNING FOR AMERICAN OPTIONSIN THE MULTI-DIMENSIONAL BLACK-SCHOLESMODEL -- _t3.1. Gaussian Process Regression -- _t3.2. Machine Learning Exact Integration for European Options -- _t3.3. Machine Learning Control Variate Algorithm for AmericanOptions -- _t3.3.1. The GPR Monte CarloMethod
505	0	0	_a3.3.2. The GPR Monte Carlo Control Variate Method -- _t3.3.3. The Control Variate for GPR-Tree and GRP-EI -- _t4. NUMERICAL RESULTS -- _t4.1. Geometric and Arithmetic Basket Put Options -- _t4.2. Call on theMaximum Option -- _t4.3. Variance Reduction -- _tCONCLUSION -- _tREFERENCES -- _tChapter 2A MACHINE LEARNING APPROACH TOOPTION PRICING UNDER LÉ VY PROCESSES -- _tAbstract -- _t1. INTRODUCTION -- _t1.1. Machine Learning in Finance -- _tadvance.1.2. -- _t2. OPTION PRICING -- _t2.1. The Applications in Option Pricing -- _t2.2. Lévy Processes -- _t3. MACHINE LEARNING APPROACH -- _t4. CGMY MODEL CALIBRATION WITH GPR
505	0	0	_a5. ARTIFICIAL NEURAL NETWORKS -- _t5.1. Feedforward ANN -- _t5.2. Recurrent NN -- _t5.3. Long/Short Term -- _t5.4. Gated Recurrent Units -- _t5.5. Bidirectional Recurrent Neural Networks -- _t5.6. BoltzmannMachines -- _t5.7. Restricted BoltzmannMachines -- _t5.8. Convolutional Networks -- _t6. ACTIVATION FUNCTIONS -- _t6.1. Step Function -- _t6.2. Linear Activation Function -- _t6.3. Sigmoid Activation Function -- _t6.4. Hyperbolic Tangent Activation Function -- _t6.5. Softsign Activation Function -- _t6.6. Basic Rectified Linear Unit (ReLU)The -- _t6.7. Leaky ( -- _t6.8. Modified Rectifiers (MELU)Numerous attempts have
505	0	0	_a6.9. Softplus Activation Function -- _t7. APPLYING A FF ANN TO SOLVE THE MODELCALIBRATION PROBLEM -- _t7.1. Historical Data Preparation -- _t7.2. Synthetic Data -- _t7.3. Training the Network -- _t7.4. Market States ClassificationFinancial markets -- _t8. PRICING OPTIONS IN THE CGMY MODEL VIA AFF ANN -- _tCONCLUSION -- _tACKNOWLEDGMENT -- _tREFERENCES -- _tChapter 3ON SWING OPTION PRICINGUNDER LÉ VY PROCESS DYNAMICS -- _tAbstract -- _t1. INTRODUCTION -- _t2. SWING OPTIONS -- _t2.1. Policy Constraints -- _t2.1.1. Volume Penalties -- _t2.1.2. Ramping Constraints -- _t2.2. Cash Flows -- _t2.2.1. The Locally Constrained Case
505	0	0	_a2.3. Swing Rights and Recovery -- _t3. MODELS FOR THE UNDERLYING -- _t3.1. Exponential Lévy Dynamics -- _t3.2. Mean-Reverting -- _t4. PRICING METHODS -- _t4.1. A Discrete Time Formulation -- _t4.1.1. Value Functions -- _t4.1.2. Optimal Swing Policies -- _t4.2. Trees and Grids -- _t4.3. Monte Carlo -- _t4.4. PROJ Method -- _t4.4.1. Value Functions -- _t4.4.2. Pure Fixed Rights -- _t4.4.3. Numerical Examples: Fixed Rights -- _t4.5. A Continuous Time Formulation -- _t4.5.1. Variational Inequalities -- _t4.6. COSMethod -- _t4.7. PROJ: American Contracts -- _t4.7.1. Algorithm Structure -- _t4.7.2. Numerical Example: Constant Recovery
530			_a2 _ub
650		0	_aLévy processes.
655		1	_aElectronic Books.
700	1		_aKudryavtsev, Oleg, _e5
856	4	0	_zClick to access digital title | log in using your CIU ID number and my.ciu.edu password. _uhttpss://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2987252&site=eds-live&custid=s3260518
942			_cOB _D _eEB _hQA. _m2021 _QOL _R _x _8NFIC _2LOC
994			_a92 _bNT
999			_c80429 _d80429
902			_a1 _bCynthia Snell _c1 _dCynthia Snell