SOME ANALYTIC ITERATIVE METHODS FOR SOLVING VARIOUS CLASSES OF STOCHASTIC HEREDITARY INTEGRODIFFERENTIAL EQUATIONS UDC :531.36:

Similar documents
SELECTION OF MOST APPROPRIATE PROJECT ALTERNATIVES FOR REALIZATION OF INVESTMENT STUDY UDC

Estimating the Current Value of Time-Varying Beta

Geometric Brownian Motion (Stochastic Population Growth)

Parameter estimation of diffusion models from discrete observations

Practical example of an Economic Scenario Generator

Stochastic Approximation Algorithms and Applications

Overnight Index Rate: Model, calibration and simulation

Implementing Models in Quantitative Finance: Methods and Cases

Properties of IRR Equation with Regard to Ambiguity of Calculating of Rate of Return and a Maximum Number of Solutions

Revista Economică 68:1 (2016) BROWNIAN MOVEMENT OF STOCK QUOTES OF THE COMPANIES LISTED ON THE BUCHAREST STOCK EXCHANGE AND PROBABILITY RANGES

Markov Decision Processes

Some derivative free quadratic and cubic convergence iterative formulas for solving nonlinear equations

Curriculum Map for Mathematics and Statistics MS (Predoctoral Studies in Mathematics) Pure* Math Applied** Math

Equivalence between Semimartingales and Itô Processes

Interest Rate Bermudan Swaption Valuation and Risk

X ln( +1 ) +1 [0 ] Γ( )

SOME COMPARATIVE CONSIDERATIONS OF REVENUE, ECONOMY, PROFIT, AND PROFITABILITY FUNCTIONS * UDC Miroljub Đ. Milojević, Vesna M.

Heinz W. Engl. Industrial Mathematics Institute Johannes Kepler Universität Linz, Austria

Option Pricing Formula for Fuzzy Financial Market

Using of stochastic Ito and Stratonovich integrals derived security pricing

OPTIMAL PORTFOLIO CONTROL WITH TRADING STRATEGIES OF FINITE

Fundamentals of Stochastic Filtering

Option Pricing under Delay Geometric Brownian Motion with Regime Switching

Options Pricing Using Combinatoric Methods Postnikov Final Paper

Graduate School of Information Sciences, Tohoku University Aoba-ku, Sendai , Japan

M.S. in Quantitative Finance & Risk Analytics (QFRA) Fall 2017 & Spring 2018

Curriculum Map for Mathematics and Statistics BS (Traditional Track)

1 Consumption and saving under uncertainty

17 MAKING COMPLEX DECISIONS

Interest Rate Cancelable Swap Valuation and Risk

The Nonlinear Real Interest Rate Growth Model: USA

Artificially Intelligent Forecasting of Stock Market Indexes

The Optimization Process: An example of portfolio optimization

On the White Noise of the Price of Stocks related to the Option Prices from the Black-Scholes Equation

Advanced Numerical Techniques for Financial Engineering

I Preliminary Material 1

PRINCIPLES REGARDING PROVISIONS FOR LIFE RISKS SOCIETY OF ACTUARIES COMMITTEE ON ACTUARIAL PRINCIPLES*

A Comparative Study of Black-Scholes Equation

2.1 Random variable, density function, enumerative density function and distribution function

INTERNATIONAL FINANCIAL REPORTING STANDARD ON SMEs: OPPORTUNITY TO CHANGE NATIONAL ACCOUNTING LEGISLATURE? UDC 006.3:

FX Smile Modelling. 9 September September 9, 2008

Elif Özge Özdamar T Reinforcement Learning - Theory and Applications February 14, 2006

THE OPTIMAL HEDGE RATIO FOR UNCERTAIN MULTI-FOREIGN CURRENCY CASH FLOW

A NEW NOTION OF TRANSITIVE RELATIVE RETURN RATE AND ITS APPLICATIONS USING STOCHASTIC DIFFERENTIAL EQUATIONS. Burhaneddin İZGİ

Greek parameters of nonlinear Black-Scholes equation

Lecture 17: More on Markov Decision Processes. Reinforcement learning

CONSTRUCTION OF CODES BY LATTICE VALUED FUZZY SETS. 1. Introduction. Novi Sad J. Math. Vol. 35, No. 2, 2005,

Intro to Reinforcement Learning. Part 3: Core Theory

REAL OPTION DECISION RULES FOR OIL FIELD DEVELOPMENT UNDER MARKET UNCERTAINTY USING GENETIC ALGORITHMS AND MONTE CARLO SIMULATION

Contents Critique 26. portfolio optimization 32

A Numerical Approach to the Estimation of Search Effort in a Search for a Moving Object

Option Pricing Using Bayesian Neural Networks

FINN 422 Quantitative Finance Fall Semester 2016

SCIENCE, TECHNOLOGY AND INNOVATION

International Journal of Computer Engineering and Applications, Volume XII, Issue II, Feb. 18, ISSN

Financial Risk Modeling on Low-power Accelerators: Experimental Performance Evaluation of TK1 with FPGA

ELEMENTS OF MONTE CARLO SIMULATION

EFFICIENT MONTE CARLO ALGORITHM FOR PRICING BARRIER OPTIONS

THE OPTIMAL ASSET ALLOCATION PROBLEMFOR AN INVESTOR THROUGH UTILITY MAXIMIZATION

PART II IT Methods in Finance

Financial and Actuarial Mathematics

TDT4171 Artificial Intelligence Methods

OPTIMIZATION PROBLEM OF FOREIGN RESERVES

Does my beta look big in this?

A distributed Laplace transform algorithm for European options

NUMERICAL METHODS OF PARTIAL INTEGRO-DIFFERENTIAL EQUATIONS FOR OPTION PRICE

The Mathematics of Currency Hedging

4. Black-Scholes Models and PDEs. Math6911 S08, HM Zhu

Applied Mathematical Sciences, Vol. 8, 2014, no. 1, 1-12 HIKARI Ltd,

LOSS SEVERITY DISTRIBUTION ESTIMATION OF OPERATIONAL RISK USING GAUSSIAN MIXTURE MODEL FOR LOSS DISTRIBUTION APPROACH

Exam in TFY4275/FY8907 CLASSICAL TRANSPORT THEORY Feb 14, 2014

Yao s Minimax Principle

Math 623 (IOE 623), Winter 2008: Final exam

Math Computational Finance Option pricing using Brownian bridge and Stratified samlping

sample-bookchapter 2015/7/7 9:44 page 1 #1 THE BINOMIAL MODEL

Fast Computation of the Economic Capital, the Value at Risk and the Greeks of a Loan Portfolio in the Gaussian Factor Model

THE USE OF NUMERAIRES IN MULTI-DIMENSIONAL BLACK- SCHOLES PARTIAL DIFFERENTIAL EQUATIONS. Hyong-chol O *, Yong-hwa Ro **, Ning Wan*** 1.

MODELLING 1-MONTH EURIBOR INTEREST RATE BY USING DIFFERENTIAL EQUATIONS WITH UNCERTAINTY

Empirical Study on Short-Term Prediction of Shanghai Composite Index Based on ARMA Model

COS402- Artificial Intelligence Fall Lecture 17: MDP: Value Iteration and Policy Iteration

Markov Decision Processes

Advanced. of Time. of Measure. Aarhus University, Denmark. Albert Shiryaev. Stek/ov Mathematical Institute and Moscow State University, Russia

Math Option pricing using Quasi Monte Carlo simulation

Risk-Neutral Valuation

Assembly systems with non-exponential machines: Throughput and bottlenecks

Lecture 2: Making Good Sequences of Decisions Given a Model of World. CS234: RL Emma Brunskill Winter 2018

Investigation of Dependency between Short Rate and Transition Rate on Pension Buy-outs. Arık, A. 1 Yolcu-Okur, Y. 2 Uğur Ö. 2

November 2006 LSE-CDAM

MFE Course Details. Financial Mathematics & Statistics

MBF1923 Econometrics Prepared by Dr Khairul Anuar

Single Machine Inserted Idle Time Scheduling with Release Times and Due Dates

Exact shape-reconstruction by one-step linearization in EIT

F A S C I C U L I M A T H E M A T I C I

MFE Course Details. Financial Mathematics & Statistics

Outline. 1 Introduction. 2 Algorithms. 3 Examples. Algorithm 1 General coordinate minimization framework. 1: Choose x 0 R n and set k 0.

Macroeconomic Analysis and Parametric Control of Economies of the Customs Union Countries Based on the Single Global Multi- Country Model

Recovery of time-dependent parameters of a Black- Scholes-type equation: an inverse Stieltjes moment approach

Econ 582 Nonlinear Regression

Math 416/516: Stochastic Simulation

Continuous Time Finance. Tomas Björk

Transcription:

FACTA UNIVERSITATIS Series: Mechanics, Automatic Control and Robotics Vol.4, N o 16, 2004, pp. 11-31 Invited Paper SOME ANALYTIC ITERATIVE METHODS FOR SOLVING VARIOUS CLASSES OF STOCHASTIC HEREDITARY INTEGRODIFFERENTIAL EQUATIONS UDC 519.218.7:531.36:629.7.058.6 Svetlana Janković, Miljana Jovanović * Faculty of Science, Department of Mathematics, University of Niš, Višegradska 33, 18000 Niš, Serbia and Montenegro E-mail: svjank@pmf.ac.ni.yu, mima@pmf.ac.ni.yu Abstract. The notion of hereditary phenomena is particularly convenient for studying such phenomena in continuum mechanics of materials with memories, as a version of the well-known theory of fading memory spaces. Mathematical models represent deterministic hereditary differential equations researched in many papers and monographs. Later, this notion was appropriately used in an investigation of the effect of the Gaussian white noise, which mathematical interpretation is represented by stochastic hereditary differential equations of the Ito type. In the present paper we consider a general analytic iterative method for solving stochastic hereditary integrodifferential equation of the Ito type. We give sufficient conditions under which a sequence of iterations converges with probability one to the solution of the original equation. The generality of this method is in the sense that many well-known iterative methods are its special cases, the Picard-Lindelof method of successive approximations, for example. Some other iterative methods, including linearizations of the coefficients of the original equation, are suggested. Especially, using a concept of a random bounded integral contractor, basically introduced by Altman and Kuo, we show that the iterative procedure utilized to prove the existence and uniqueness of the solution of the stochastic hereditary integrodifferential equation, is also a special algorithm included in the considered general iterative procedure. Key words: Stochastic differential equation, stochastic hereditary integrodifferential equation, random integral contractor, Z-algorithm, determining sequence. Received August 01, 2003 AMS Mathematics Subject Classification (2000): 60H10, 60H20 * Supported by Grant No 1834 of MNTRS through Math. Institute SANU.

12 S. JANKOVIĆ, M. JOVANOVIĆ

Some Analytic Iterative Methods for Solving Various Classes of Stochastic Hereditary Integrodifferential Equations 13

14 S. JANKOVIĆ, M. JOVANOVIĆ

Some Analytic Iterative Methods for Solving Various Classes of Stochastic Hereditary Integrodifferential Equations 15

16 S. JANKOVIĆ, M. JOVANOVIĆ

Some Analytic Iterative Methods for Solving Various Classes of Stochastic Hereditary Integrodifferential Equations 17

18 S. JANKOVIĆ, M. JOVANOVIĆ

Some Analytic Iterative Methods for Solving Various Classes of Stochastic Hereditary Integrodifferential Equations 19

20 S. JANKOVIĆ, M. JOVANOVIĆ

Some Analytic Iterative Methods for Solving Various Classes of Stochastic Hereditary Integrodifferential Equations 21

22 S. JANKOVIĆ, M. JOVANOVIĆ

Some Analytic Iterative Methods for Solving Various Classes of Stochastic Hereditary Integrodifferential Equations 23

24 S. JANKOVIĆ, M. JOVANOVIĆ

Some Analytic Iterative Methods for Solving Various Classes of Stochastic Hereditary Integrodifferential Equations 25

26 S. JANKOVIĆ, M. JOVANOVIĆ

Some Analytic Iterative Methods for Solving Various Classes of Stochastic Hereditary Integrodifferential Equations 27

28 S. JANKOVIĆ, M. JOVANOVIĆ

Some Analytic Iterative Methods for Solving Various Classes of Stochastic Hereditary Integrodifferential Equations 29

30 S. JANKOVIĆ, M. JOVANOVIĆ

Some Analytic Iterative Methods for Solving Various Classes of Stochastic Hereditary Integrodifferential Equations 31 NEKE ANALITIČKE ITERATIVNE METODE ZA REŠAVANJE RAZLIČITIH KLASA STOHASTIČKIH NASLEDNIH INTEGRODIFERENCIJALNIH JEDNAČINA Svetlana Janković, Miljana Jovanović Nasledni fenomeni su posebno pogodni za proučavanje fenomena u mehanici kontinuuma materijala sa memorijom. Matematički modeli takvih pojava se opisuju determinističkim naslednim diferencijalnim jednačinama, proučavanim u mnogim radovima i monografijama. Kasnije, ovi pojmovi su adekvatno prošireni na istraživanja pod uticajem Gaussovog belog šuma, sa matematičkom interpretacijom stohastičkim naslednim diferencijalnim jednačinama tipa Itoa. U ovom radu se razmatra opšta analitička iterativna metoda za rešavanje stohastičkih naslednih integrodiferencijalnih jednačina tipa Itoa. Daju se dovoljni uslovi pri kojima niz iteracija konvergira u verovatnoći ka rešenju originalne jednačine. Ova metoda je opšta, u smislu da su mnoge poznate iterativne metode njeni specijalni slučajevi, na primer metoda sukcesivnih aproksimacija Picard-Lindelofa. Prikazane su i neke druge iterativne metode sa linearizacijom koeficijenata originalne jednačine. Specijalno, koristeći koncept ograničenog slučajnog integralnog kontraktora u smislu Altmana i Kuoa, pokazuje se da je iterativna metoda primenjena u dokazu teoreme egzistencije i jedinstvenosti rešenja stohastičke nasledne integrodiferencijalne jednačine takodje specijalan slučaj prethodno opisane opšte iterativne metode.