Mathematics in Finance: Risk Management and Predictive Analytics
DOI:
https://doi.org/10.36676/mdmp.v1.i3.34Keywords:
Mathematics in Finance, Risk Management, Predictive Analytics, Probability TheoryAbstract
The application of mathematical principles in finance has revolutionized risk management and predictive analytics, enabling more precise modeling, assessment, and mitigation of financial risks. This paper explores the critical role of mathematics in developing robust financial models that enhance decision-making processes and improve the accuracy of financial forecasts. Key mathematical techniques, including probability theory, statistics, stochastic processes, and optimization, are examined in the context of their application to risk management and predictive analytics. the use of probability theory and statistics in modeling financial risks, such as market risk, credit risk, and operational risk. These mathematical tools provide the foundation for quantifying and managing uncertainty in financial markets. Stochastic processes, including Brownian motion and geometric Brownian motion, are explored for their role in modeling asset prices and interest rates, forming the basis of various financial derivatives and options pricing models.
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