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Financial modeling is the task of building an abstract representation (a model) of a real world financial situation. [1] This is a mathematical model designed to represent (a simplified version of) the performance of a financial asset or portfolio of a business, project , or any other investment.
Mathematical finance, also known as quantitative finance and financial mathematics, is a field of applied mathematics, concerned with mathematical modeling in the financial field. In general, there exist two separate branches of finance that require advanced quantitative techniques: derivatives pricing on the one hand, and risk and portfolio ...
In 1977, Phelim Boyle pioneered the use of simulation in derivative valuation in his seminal Journal of Financial Economics paper. [4] This article discusses typical financial problems in which Monte Carlo methods are used. It also touches on the use of so-called "quasi-random" methods such as the use of Sobol sequences.
The Brownian motion models for financial markets are based on the work of Robert C. Merton and Paul A. Samuelson, as extensions to the one-period market models of Harold Markowitz and William F. Sharpe, and are concerned with defining the concepts of financial assets and markets, portfolios, gains and wealth in terms of continuous-time stochastic processes.
Fama–French three-factor model; Fama–MacBeth regression; Financial Modelers' Manifesto; Financial modeling; Financial models with long-tailed distributions and volatility clustering; Fuzzy pay-off method for real option valuation
Financial risk modeling is the use of formal mathematical and econometric techniques to measure, monitor and control the market risk, credit risk, and operational risk on a firm's balance sheet, on a bank's accounting ledger of tradeable financial assets, or of a fund manager's portfolio value; see Financial risk management.
When the volatility and drift of the instantaneous forward rate are assumed to be deterministic, this is known as the Gaussian Heath–Jarrow–Morton (HJM) model of forward rates. [ 1 ] : 394 For direct modeling of simple forward rates the Brace–Gatarek–Musiela model represents an example.
The model structure basically helps in understanding how the flows are connected from a behavioral perspective or in simple words how the behavior of a sector affects the flow of funds in the system, e.g., the factors that affect the consumption (C) of the household is not clear from the flow of funds but can be explained by the model.