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Non-negativity preserving numerical algorithms for stochastic differential equations  [PDF]
Esteban Moro,Henri Schurz
Mathematics , 2005,
Abstract: Construction of splitting-step methods and properties of related non-negativity and boundary preserving numerical algorithms for solving stochastic differential equations (SDEs) of Ito-type are discussed. We present convergence proofs for a newly designed splitting-step algorithm and simulation studies for numerous numerical examples ranging from stochastic dynamics occurring in asset pricing theory in mathematical finance (SDEs of CIR and CEV models) to measure-valued diffusion and superBrownian motion (SPDEs) as met in biology and physics.
Polynomial Preserving Diffusions and Applications in Finance  [PDF]
Damir Filipovic,Martin Larsson
Mathematics , 2014,
Abstract: This paper provides the mathematical foundation for polynomial preserving diffusions. They play an important role in a growing range of applications in finance, including financial market models for interest rates, credit risk, stochastic volatility, commodities and electricity. Uniqueness of polynomial preserving diffusions is established via moment determinacy in combination with pathwise uniqueness. Existence boils down to a stochastic invariance problem that we solve for semialgebraic state spaces. Examples include the unit ball, the product of the unit cube and nonnegative orthant, and the unit simplex.
Mathematical Models in Finance
Halim Kazan,Ahmet Ergülen
Sel?uk Journal of Applied Mathematics , 2005,
Abstract: Finance is the corner stone of the free enterprise system. Good financial management is therefore vitally important to the economic health of business firms, and thus the nation and the world. The field is relatively complex, and it is undergoing constant change in response to shifts in economic conditions say Brigham and Gapenski in the introduction of their Financial Management book (Brigham, Eugene F., Gapenski, Louis C 1994). As they said the field is relatively complex since most of the financial decisions are involved with uncertainty and risk. This is where quantitative methods and finance meets. In financial decision making process, like most of the decision making process, final decision made by managers, not by some mathematical tools. However, those mathematical tools, used in financial decision making process, contribute to managers' decision a lot. Finance is consist of three interrelated areas which are Money and Capital Markets, dealing with securities markets and financial institutions, Investments, focusing on the decisions of individuals, financial and other institutions while they choose securities for their investment portfolios; and Financial Management, involving the actual management of non financial firms (Brigham, E. F., Gapenski, L. C;1994) In this study I tried to summarize mathematical methods that have been used in finance historically.
Duality and optimality conditions in stochastic optimization and mathematical finance  [PDF]
Sara Biagini,Teemu Pennanen,Ari-Pekka Perkki?
Mathematics , 2015,
Abstract: This article studies convex duality in stochastic optimization over finite discrete-time. The first part of the paper gives general conditions that yield explicit expressions for the dual objective in many applications in operations research and mathematical finance. The second part derives optimality conditions by combining general saddle-point conditions from convex duality with the dual representations obtained in the first part of the paper. Several applications to stochastic optimization and mathematical finance are given.
Comparison Tests of Variable-Stepsize Algorithms for Stochastic Ordinary Differential Equations of Finance  [PDF]
Yin Mei Wong,Joshua Wilkie
Physics , 2006,
Abstract: Since the introduction of the Black-Scholes model stochastic processes have played an increasingly important role in mathematical finance. In many cases prices, volatility and other quantities can be modeled using stochastic ordinary differential equations. Available methods for solving such equations have until recently been markedly inferior to analogous methods for deterministic ordinary differential equations. Recently, a number of methods which employ variable stepsizes to control local error have been developed which appear to offer greatly improved speed and accuracy. Here we conduct a comparative study of the performance of these algorithms for problems taken from the mathematical finance literature.
Convex duality in stochastic programming and mathematical finance  [PDF]
Teemu Pennanen
Mathematics , 2010,
Abstract: This paper proposes a general duality framework for the problem of minimizing a convex integral functional over a space of stochastic processes adapted to a given filtration. The framework unifies many well-known duality frameworks from operations research and mathematical finance. The unification allows the extension of some useful techniques from these two fields to a much wider class of problems. In particular, combining certain finite-dimensional techniques from convex analysis with measure theoretic techniques from mathematical finance, we are able to close the duality gap in some situations where traditional topological arguments fail.
On three filtering problems arising in mathematical finance  [PDF]
Damiano Brigo,Bernard Hanzon
Quantitative Finance , 2008,
Abstract: Three situations in which filtering theory is used in mathematical finance are illustrated at different levels of detail. The three problems originate from the following different works: 1) On estimating the stochastic volatility model from observed bilateral exchange rate news, by R. Mahieu, and P. Schotman; 2) A state space approach to estimate multi-factors CIR models of the term structure of interest rates, by A.L.J. Geyer, and S. Pichler; 3) Risk-minimizing hedging strategies under partial observation in pricing financial derivatives, by P. Fischer, E. Platen, and W. J. Runggaldier; In the first problem we propose to use a recent nonlinear filtering technique based on geometry to estimate the volatility time series from observed bilateral exchange rates. The model used here is the stochastic volatility model. The filters that we propose are known as projection filters, and a brief derivation of such filters is given. The second problem is introduced in detail, and a possible use of different filtering techniques is hinted at. In fact the filters used for this problem in 2) and part of the literature can be interpreted as projection filters and we will make some remarks on how more general and possibly more suitable projection filters can be constructed. The third problem is only presented shortly.
Fast Multiple Order-Preserving Matching Algorithms  [PDF]
Myoungji Han,Munseong Kang,Sukhyeun Cho,Geonmo Gu,Jeong Seop Sim,Kunsoo Park
Computer Science , 2015,
Abstract: Given a text $T$ and a pattern $P$, the order-preserving matching problem is to find all substrings in $T$ which have the same relative orders as $P$. Order-preserving matching has been an active research area since it was introduced by Kubica et al. \cite{kubica2013linear} and Kim et al. \cite{kim2014order}. In this paper we present two algorithms for the multiple order-preserving matching problem, one of which runs in sublinear time on average and the other in linear time on average. Both algorithms run much faster than the previous algorithms.
Structure-Preserving Algorithms for the Lorenz System
GANG Tie-Qiang,MEI Feng-Xiang,CHEN Li-Jie,

中国物理快报 , 2008,
Abstract: Based on a splitting method and a composition method, we construct some structure-preserving algorithms with first-order, second-order and fourth-order accuracy for a Lorenz system. By using the Liouville's formula, it is proven that the structure-preserving algorithms exactly preserve the volume of infinitesimal cube for the Lorenz system. Numerical experimental results illustrate that for the conservative Lorenz system, the qualitative behaviour of the trajectories described by the classical explicit fourth-order Runge--Kutta (RK4) method and the fifth-order Runge--Kutta--Fehlberg (RKF45) method is wrong, while the qualitative behaviour derived from the structure-preserving algorithms with different orders of accuracy is correct. Moreover, for the smalldissipative Lorenz system, the norm errors of the structure-preserving algorithms in phase space are less than those of the Runge--Kutta methods.
Existence, uniqueness, and global regularity for degenerate elliptic obstacle problems in mathematical finance  [PDF]
Panagiota Daskalopoulos,Paul M. N. Feehan
Quantitative Finance , 2011,
Abstract: The Heston stochastic volatility process, which is widely used as an asset price model in mathematical finance, is a paradigm for a degenerate diffusion process where the degeneracy in the diffusion coefficient is proportional to the square root of the distance to the boundary of the half-plane. The generator of this process with killing, called the elliptic Heston operator, is a second-order degenerate elliptic partial differential operator whose coefficients have linear growth in the spatial variables and where the degeneracy in the operator symbol is proportional to the distance to the boundary of the half-plane. With the aid of weighted Sobolev spaces, we prove existence, uniqueness, and global regularity of solutions to stationary variational inequalities and obstacle problems for the elliptic Heston operator on unbounded subdomains of the half-plane. In mathematical finance, solutions to obstacle problems for the elliptic Heston operator correspond to value functions for perpetual American-style options on the underlying asset.
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