Mathematical Finance with Applications

doi:10.3390/books978-3-03943-574-6

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Hardback

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Keywords

cluster analysis; equity index networks; machine learning; copulas; dependence structures; quotient of random variables; density functions; distribution functions; multi-factor model; risk factors; OLS and ridge regression model; python; chi-square test; quantile; VaR; quadrangle; CVaR; conditional value-at-risk; expected shortfall; ES; superquantile; deviation; risk; error; regret; minimization; CVaR estimation; regression; linear regression; linear programming; portfolio safeguard; PSG; equity option pricing; factor models; stochastic volatility; jumps; mathematics; probability; statistics; finance; applications; investment home bias (IHB); bivariate first-degree stochastic dominance (BFSD); keeping up with the Joneses (KUJ); correlation loving (CL); return spillover; volatility spillover; optimal weights; hedge ratios; US financial crisis; Chinese stock market crash; stock price prediction; auto-regressive integrated moving average; artificial neural network; stochastic process-geometric Brownian motion; financial models; firm performance; causality tests; leverage; long-term debt; capital structure; return spillover; volatility spillover; shock spillover; US financial crisis; Chinese stock market crash

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Mathematical Finance with Applications

Edited by

December 2020

232 pages

ISBN978-3-03943-573-9 (Hardback)
ISBN978-3-03943-574-6 (PDF)

https://doi.org/10.3390/books978-3-03943-574-6

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This is a Reprint of the Special Issue Mathematical Finance with Applications that was published in

Business & Economics

Computer Science & Mathematics

Summary

Mathematical finance plays a vital role in many fields within finance and provides the theories and tools that have been widely used in all areas of finance. Knowledge of mathematics, probability, and statistics is essential to develop finance theories and test their validity through the analysis of empirical, real-world data. For example, mathematics, probability, and statistics could help to develop pricing models for financial assets such as equities, bonds, currencies, and derivative securities.