Mathematical Finance with Applications

Mathematical Finance with Applications

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...

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Bibliographic Details
Other Authors: Wong, Wing-Keung (Editor), Guo, Xu (Editor), Lozza, Sergio Ortobelli (Editor)
Format: Electronic Book Chapter
Language:English
Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020
Subjects:
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
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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.
Physical Description:1 electronic resource (232 p.)
ISBN:books978-3-03943-574-6
9783039435739
9783039435746
Access:Open Access

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