Quantitative Methods for Economics and Finance
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 ga...
Format: | eBook |
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Language: | English |
Published: |
Basel, Switzerland
MDPI - Multidisciplinary Digital Publishing Institute
2021
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Subjects: | |
Online Access: | Open Access: DOAB: description of the publication Open Access: DOAB, download the publication |
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720 | 1 | |a Trinidad-Segovia, J.E. |4 edt | |
720 | 1 | |a Sánchez-Granero, Miguel Ángel |4 edt | |
720 | 1 | |a Sánchez-Granero, Miguel Ángel |4 oth | |
720 | 1 | |a Trinidad-Segovia, J.E. |4 oth | |
245 | 0 | 0 | |a Quantitative Methods for Economics and Finance |
260 | |a Basel, Switzerland |b MDPI - Multidisciplinary Digital Publishing Institute |c 2021 | ||
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506 | 0 | |a Open Access |f Unrestricted online access |2 star | |
520 | |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. | ||
540 | |a Creative Commons |f https://creativecommons.org/licenses/by/4.0/ |2 cc |u https://creativecommons.org/licenses/by/4.0/ | ||
546 | |a English | ||
650 | 7 | |a Coins, banknotes, medals, seals (numismatics) |2 bicssc | |
653 | |a academic cheating | ||
653 | |a asset pricing | ||
653 | |a autoregressive integrated moving average (ARIMA) | ||
653 | |a bilateral investment treaties | ||
653 | |a biotechnological firms | ||
653 | |a bitcoin | ||
653 | |a Bitcoin | ||
653 | |a cash flow management | ||
653 | |a centered model | ||
653 | |a Chinese listed companies | ||
653 | |a co-movement | ||
653 | |a cointegration | ||
653 | |a commodity prices | ||
653 | |a computational finance | ||
653 | |a copula | ||
653 | |a copulas | ||
653 | |a corporate prudential risk | ||
653 | |a correlation risk premium | ||
653 | |a cryptocurrency | ||
653 | |a DCC | ||
653 | |a DEA | ||
653 | |a decision-making process | ||
653 | |a decreasing impatience | ||
653 | |a deep learning | ||
653 | |a deep recurrent convolutional neural networks | ||
653 | |a delay | ||
653 | |a derivation | ||
653 | |a detection | ||
653 | |a discount | ||
653 | |a dispersion trading | ||
653 | |a dynamically simulated autoregressive distributed lag (DYS-ARDL) | ||
653 | |a econometrics | ||
653 | |a EGARCH | ||
653 | |a eigenvalues | ||
653 | |a elasticity | ||
653 | |a energy consumption | ||
653 | |a ensemble empirical mode decomposition (EEMD) | ||
653 | |a essential multicollinearity | ||
653 | |a Ethereum | ||
653 | |a EVT | ||
653 | |a FD4 approach | ||
653 | |a financial distress | ||
653 | |a financial distress prediction | ||
653 | |a financial markets | ||
653 | |a forecasting | ||
653 | |a foreign direct investment | ||
653 | |a futures prices | ||
653 | |a GARCH | ||
653 | |a generalized Pareto distribution | ||
653 | |a genetic algorithm (GA) | ||
653 | |a gold | ||
653 | |a historical simulation approach | ||
653 | |a hurst exponent | ||
653 | |a Hurst exponent | ||
653 | |a induced risk aversion | ||
653 | |a informality | ||
653 | |a intercept | ||
653 | |a intertemporal choice | ||
653 | |a liquidity constraints | ||
653 | |a liquidity risk | ||
653 | |a local optima vs. local minima | ||
653 | |a long memory | ||
653 | |a macroeconomic propagation | ||
653 | |a Markov Chain Monte Carlo simulation | ||
653 | |a mean square error | ||
653 | |a multicollinearity | ||
653 | |a multiperiod financial management | ||
653 | |a multiple periods | ||
653 | |a non-linear macroeconomic modelling | ||
653 | |a non-parametric efficiency | ||
653 | |a noncentered model | ||
653 | |a nonessential multicollinearity | ||
653 | |a number of factors | ||
653 | |a option arbitrage | ||
653 | |a P 500 | ||
653 | |a P500 | ||
653 | |a pairs trading | ||
653 | |a peaks-over-threshold | ||
653 | |a pharmaceutical industry | ||
653 | |a policy uncertainty | ||
653 | |a precautionary savings | ||
653 | |a probability | ||
653 | |a probability of volatility cluster | ||
653 | |a productivity | ||
653 | |a profitability | ||
653 | |a raise regression | ||
653 | |a regional trade agreements | ||
653 | |a Ripple | ||
653 | |a risk | ||
653 | |a S& | ||
653 | |a scale economies | ||
653 | |a SRA approach | ||
653 | |a stock prices | ||
653 | |a structural gravity model | ||
653 | |a student t-copula | ||
653 | |a support vector regression (SVR) | ||
653 | |a tax evasion | ||
653 | |a the financial accelerator | ||
653 | |a threshold regression | ||
653 | |a Tobin's q | ||
653 | |a unconstrained distributed lag model | ||
653 | |a United States | ||
653 | |a VaR | ||
653 | |a variance inflation factor | ||
653 | |a volatility cluster | ||
653 | |a volatility series | ||
653 | |a volatility trading | ||
793 | 0 | |a DOAB Library. | |
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856 | 4 | 0 | |u https://mdpi.com/books/pdfview/book/3389 |7 0 |z Open Access: DOAB, download the publication |