Mathematical Finance with Applications
Wong, Wing-Keung
Mathematical Finance with Applications - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020 - 1 electronic resource (232 p.)
Open Access
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.
Creative Commons
English
books978-3-03943-574-6 9783039435739 9783039435746
10.3390/books978-3-03943-574-6 doi
Coins, banknotes, medals, seals (numismatics)
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 shock spillover
Mathematical Finance with Applications - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020 - 1 electronic resource (232 p.)
Open Access
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.
Creative Commons
English
books978-3-03943-574-6 9783039435739 9783039435746
10.3390/books978-3-03943-574-6 doi
Coins, banknotes, medals, seals (numismatics)
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 shock spillover