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210512 ||| eng |
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|a books978-3-0365-0197-0
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|a 9783036501970
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|a 9783036501963
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| 100 |
1 |
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|a Trinidad-Segovia, J.E.
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| 245 |
0 |
0 |
|a Quantitative Methods for Economics and Finance
|h Elektronische Ressource
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| 260 |
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|a Basel, Switzerland
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2021
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| 300 |
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|a 1 electronic resource (418 p.)
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| 653 |
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|a essential multicollinearity
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| 653 |
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|a GARCH
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| 653 |
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|a Tobin’s q
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| 653 |
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|a EGARCH
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| 653 |
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|a local optima vs. local minima
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| 653 |
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|a deep learning
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| 653 |
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|a probability
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| 653 |
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|a intercept
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| 653 |
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|a derivation
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| 653 |
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|a mean square error
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| 653 |
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|a Ripple
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| 653 |
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|a long memory
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| 653 |
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|a macroeconomic propagation
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| 653 |
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|a induced risk aversion
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| 653 |
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|a eigenvalues
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| 653 |
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|a Markov Chain Monte Carlo simulation
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| 653 |
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|a computational finance
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| 653 |
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|a Bitcoin
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| 653 |
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|a asset pricing
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| 653 |
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|a bitcoin
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| 653 |
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|a biotechnological firms
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| 653 |
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|a corporate prudential risk
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| 653 |
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|a generalized Pareto distribution
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| 653 |
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|a profitability
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| 653 |
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|a FD4 approach
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| 653 |
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|a volatility cluster
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| 653 |
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|a risk
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| 653 |
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|a financial distress prediction
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| 653 |
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|a student t-copula
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| 653 |
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|a threshold regression
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| 653 |
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|a P500
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| 653 |
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|a volatility series
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| 653 |
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|a regional trade agreements
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| 653 |
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|a decision-making process
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| 653 |
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|a centered model
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| 653 |
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|a pharmaceutical industry
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| 653 |
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|a Coins, banknotes, medals, seals (numismatics) / bicssc
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| 653 |
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|a multicollinearity
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| 653 |
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|a copula
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| 653 |
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|a bilateral investment treaties
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| 653 |
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|a cointegration
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| 653 |
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|a raise regression
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| 653 |
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|a United States
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| 653 |
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|a volatility trading
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| 653 |
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|a Chinese listed companies
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| 653 |
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|a gold
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| 653 |
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|a liquidity constraints
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| 653 |
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|a ensemble empirical mode decomposition (EEMD)
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| 653 |
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|a deep recurrent convolutional neural networks
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| 653 |
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|a commodity prices
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| 653 |
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|a liquidity risk
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| 653 |
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|a unconstrained distributed lag model
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| 653 |
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|a the financial accelerator
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| 653 |
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|a multiperiod financial management
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| 653 |
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|a policy uncertainty
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| 653 |
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|a nonessential multicollinearity
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| 653 |
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|a DCC
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| 653 |
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|a noncentered model
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| 653 |
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|a VaR
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| 653 |
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|a foreign direct investment
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| 653 |
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|a forecasting
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| 653 |
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|a non-parametric efficiency
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| 653 |
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|a hurst exponent
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| 653 |
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|a copulas
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| 653 |
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|a genetic algorithm (GA)
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| 653 |
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|a number of factors
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| 653 |
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|a energy consumption
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| 653 |
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|a productivity
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| 653 |
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|a structural gravity model
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| 653 |
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|a discount
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| 653 |
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|a option arbitrage
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| 653 |
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|a DEA
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| 653 |
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|a econometrics
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| 653 |
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|a historical simulation approach
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| 653 |
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|a pairs trading
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| 653 |
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|a multiple periods
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| 653 |
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|a precautionary savings
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| 653 |
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|a elasticity
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| 653 |
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|a co-movement
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| 653 |
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|a futures prices
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| 653 |
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|a cryptocurrency
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| 653 |
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|a EVT
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| 653 |
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|a delay
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| 653 |
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|a informality
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| 653 |
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|a dispersion trading
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| 653 |
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|a autoregressive integrated moving average (ARIMA)
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| 653 |
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|a financial distress
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| 653 |
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|a support vector regression (SVR)
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| 653 |
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|a peaks-over-threshold
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| 653 |
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|a probability of volatility cluster
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| 653 |
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|a correlation risk premium
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| 653 |
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|a variance inflation factor
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| 653 |
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|a financial markets
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| 653 |
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|a Hurst exponent
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| 653 |
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|a cash flow management
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| 653 |
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|a intertemporal choice
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| 653 |
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|a decreasing impatience
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| 653 |
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|a detection
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| 653 |
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|a non-linear macroeconomic modelling
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| 653 |
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|a P 500
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| 653 |
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|a academic cheating
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| 653 |
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|a tax evasion
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| 653 |
|
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|a S&
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| 653 |
|
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|a dynamically simulated autoregressive distributed lag (DYS-ARDL)
|
| 653 |
|
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|a SRA approach
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| 653 |
|
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|a Ethereum
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| 653 |
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|a scale economies
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| 653 |
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|a stock prices
|
| 700 |
1 |
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|a Sánchez-Granero, Miguel Ángel
|
| 700 |
1 |
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|a Trinidad-Segovia, J.E.
|
| 700 |
1 |
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|a Sánchez-Granero, Miguel Ángel
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
| 989 |
|
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|b DOAB
|a Directory of Open Access Books
|
| 500 |
|
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
|
| 024 |
8 |
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|a 10.3390/books978-3-0365-0197-0
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| 856 |
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|u https://directory.doabooks.org/handle/20.500.12854/68376
|3 Volltext
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|a 000
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|a 330
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|a 900
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|a 333
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|a 320
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|a 380
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|a This book is a collection of papers for the Special Issue “Quantitative Methods for Economics and Finance” of the journal Mathematics. This Special Issue reflects on the latest developments in different fields of economics and finance where mathematics plays a significant role. The book gathers 19 papers on topics such as volatility clusters and volatility dynamic, forecasting, stocks, indexes, cryptocurrencies and commodities, trade agreements, the relationship between volume and price, trading strategies, efficiency, regression, utility models, fraud prediction, or intertemporal choice.
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