The application of Mathematics in finance: Key lessons for studentson risk management and forecasting

Abstract

This paper explores the applications of mathematics in financial risk management and forecasting, emphasizing the importance of basic mathematical techniques such as probability, statistics, algebra, and calculus in assessing, predicting, and mitigating financial risks. Key applications discussed include asset pricing models, credit risk models, portfolio optimization, Monte Carlo simulations, and time series forecasting. The paper also addresses mathematical methods commonly used in financial forecasting, such as time series analysis, econometric models, and machine learning algorithms. These methods use regression techniques, probability, and statistical analysis to predict market trends and make more accurate investment decisions. Furthermore, the paper presents real-world examples from Vietnam, including the use of Value-at-Risk (VaR) models by banks during the 2008 financial crisis, logistic regression for credit risk assessment, and ARIMA models for forecasting the Vietnamese stock market .