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Mathematical Finance with Applications
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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.
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Keywords
- Antiques & collectables
- applications
- artificial neural network
- auto-regressive integrated moving average
- bivariate first-degree stochastic dominance (BFSD)
- capital structure
- causality tests
- chi-square test
- Chinese stock market crash
- Cluster analysis
- conditional value-at-risk
- Copulas
- correlation loving (CL)
- CVaR
- CVaR estimation
- density functions
- dependence structures
- deviation
- distribution functions
- equity index networks
- equity option pricing
- error
- ES
- expected shortfall
- factor models
- Finance
- financial models
- Firm Performance
- hedge ratios
- investment home bias (IHB)
- jumps
- keeping up with the Joneses (KUJ)
- leverage
- Lifestyle, sport & leisure
- Linear Programming
- linear regression
- long-term debt
- Machine learning
- Mathematics
- minimization
- multi-factor model
- OLS and ridge regression model
- optimal weights
- portfolio safeguard
- Probability
- PSG
- Python
- quadrangle
- quantile
- quotient of random variables
- régression
- Regret
- return spillover
- Risk
- Risk factors
- shock spillover
- Statistics
- stochastic process-geometric Brownian motion
- stochastic volatility
- stock price prediction
- superquantile
- US financial crisis
- Var
- volatility spillover
Links
DOI: 10.3390/books978-3-03943-574-6Editions