Quantitative Methods for Economics and Finance

Edited by
February 2021
418 pages
  • ISBN978-3-0365-0196-3 (Hardback)
  • ISBN978-3-0365-0197-0 (PDF)

This book is a reprint of the Special Issue Quantitative Methods for Economics and Finance that was published in

Computer Science & Mathematics
Physical Sciences
Public Health & Healthcare
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.
  • Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
academic cheating; tax evasion; informality; pairs trading; hurst exponent; financial markets; long memory; co-movement; cointegration; risk; delay; decision-making process; probability; discount; detection; mean square error; multicollinearity; raise regression; variance inflation factor; derivation; intertemporal choice; decreasing impatience; elasticity; GARCH; EGARCH; VaR; historical simulation approach; peaks-over-threshold; EVT; student t-copula; generalized Pareto distribution; centered model; noncentered model; intercept; essential multicollinearity; nonessential multicollinearity; commodity prices; futures prices; number of factors; eigenvalues; volatility cluster; Hurst exponent; FD4 approach; volatility series; probability of volatility cluster; S& P500; Bitcoin; Ethereum; Ripple; bitcoin; deep learning; deep recurrent convolutional neural networks; forecasting; asset pricing; financial distress prediction; unconstrained distributed lag model; multiple periods; Chinese listed companies; cash flow management; corporate prudential risk; the financial accelerator; financial distress; induced risk aversion; liquidity constraints; liquidity risk; macroeconomic propagation; multiperiod financial management; non-linear macroeconomic modelling; Tobin’s q; precautionary savings; pharmaceutical industry; scale economies; profitability; biotechnological firms; non-parametric efficiency; productivity; DEA; dispersion trading; option arbitrage; volatility trading; correlation risk premium; econometrics; computational finance; ensemble empirical mode decomposition (EEMD); autoregressive integrated moving average (ARIMA); support vector regression (SVR); genetic algorithm (GA); energy consumption; forecasting; cryptocurrency; gold; S& P 500; GARCH; DCC; copula; copulas; Markov Chain Monte Carlo simulation; local optima vs. local minima; financial markets; SRA approach; foreign direct investment; bilateral investment treaties; regional trade agreements; structural gravity model; policy uncertainty; stock prices; dynamically simulated autoregressive distributed lag (DYS-ARDL); threshold regression; United States