Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Blockchain and cryptocurrencies have recently captured the interest of academics and those in industry. Cryptocurrencies are essentially digital currencies that use blockchain technology and cryptography to facilitate secure and anonymous transactions. The cryptocurrency market is currently worth over $500 billion. Many institutions and countries are starting to understand and implement the idea of cryptocurrencies in their business models. This Special Issue will provide a collection of papers from leading experts in the area of blockchain and cryptocurrencies. The topics covered in this Special Issue will include but are not limited to the following: academic research on blockchain and cryptocurrencies; industrial applications of blockchain and cryptocurrencies; applications of fintech in academia and industry; the economics of blockchain technology, and the financial analysis and risk management with cryptocurrencies.

Technology: general issues --- cryptocurrencies --- connectedness --- spill overs --- spectral analysis --- time-frequency-dynamic --- Bitcoin --- cryptocurrency --- spillover risks --- Copulas --- Student’s-t --- survey --- bitcoin --- efficient market hypothesis --- ARIMA --- artificial neural network --- static forecast --- contagion effect --- detrended cross-correlation analysis --- liquidity --- Ethereum --- market liquidity --- Hurst exponent --- high frequency --- fraud --- algorithms --- correlations --- impact --- risks --- regulation --- blockchain --- autoregression --- time-series analysis --- simulation --- predictive modes --- endogenous --- exogenous variables --- Blockchain --- Cryptocurrencies --- Digital Currencies --- Risk management --- Financial analysis --- cryptocurrencies --- connectedness --- spill overs --- spectral analysis --- time-frequency-dynamic --- Bitcoin --- cryptocurrency --- spillover risks --- Copulas --- Student’s-t --- survey --- bitcoin --- efficient market hypothesis --- ARIMA --- artificial neural network --- static forecast --- contagion effect --- detrended cross-correlation analysis --- liquidity --- Ethereum --- market liquidity --- Hurst exponent --- high frequency --- fraud --- algorithms --- correlations --- impact --- risks --- regulation --- blockchain --- autoregression --- time-series analysis --- simulation --- predictive modes --- endogenous --- exogenous variables --- Blockchain --- Cryptocurrencies --- Digital Currencies --- Risk management --- Financial analysis

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Blockchain and cryptocurrencies have recently captured the interest of academics and those in industry. Cryptocurrencies are essentially digital currencies that use blockchain technology and cryptography to facilitate secure and anonymous transactions. The cryptocurrency market is currently worth over $500 billion. Many institutions and countries are starting to understand and implement the idea of cryptocurrencies in their business models. This Special Issue will provide a collection of papers from leading experts in the area of blockchain and cryptocurrencies. The topics covered in this Special Issue will include but are not limited to the following: academic research on blockchain and cryptocurrencies; industrial applications of blockchain and cryptocurrencies; applications of fintech in academia and industry; the economics of blockchain technology, and the financial analysis and risk management with cryptocurrencies.

Technology: general issues --- cryptocurrencies --- connectedness --- spill overs --- spectral analysis --- time-frequency-dynamic --- Bitcoin --- cryptocurrency --- spillover risks --- Copulas --- Student’s-t --- survey --- bitcoin --- efficient market hypothesis --- ARIMA --- artificial neural network --- static forecast --- contagion effect --- detrended cross-correlation analysis --- liquidity --- Ethereum --- market liquidity --- Hurst exponent --- high frequency --- fraud --- algorithms --- correlations --- impact --- risks --- regulation --- blockchain --- autoregression --- time-series analysis --- simulation --- predictive modes --- endogenous --- exogenous variables --- Blockchain --- Cryptocurrencies --- Digital Currencies --- Risk management --- Financial analysis

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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.

Coins, banknotes, medals, seals (numismatics) --- 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&amp --- 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 --- cryptocurrency --- gold --- P 500 --- DCC --- copula --- copulas --- Markov Chain Monte Carlo simulation --- local optima vs. local minima --- 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

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Hydrological extremes have become a major concern because of their devastating consequences and their increased risk as a result of climate change and the growing concentration of people and infrastructure in high-risk zones. The analysis of hydrological extremes is challenging due to their rarity and small sample size, and the interconnections between different types of extremes and becomes further complicated by the untrustworthy representation of meso-scale processes involved in extreme events by coarse spatial and temporal scale models as well as biased or missing observations due to technical difficulties during extreme conditions. The complexity of analyzing hydrological extremes calls for robust statistical methods for the treatment of such events. This Special Issue is motivated by the need to apply and develop innovative stochastic and statistical approaches to analyze hydrological extremes under current and future climate conditions. The papers of this Special Issue focus on six topics associated with hydrological extremes: Historical changes in hydrological extremes; Projected changes in hydrological extremes; Downscaling of hydrological extremes; Early warning and forecasting systems for drought and flood; Interconnections of hydrological extremes; Applicability of satellite data for hydrological studies.

artificial neural network --- downscaling --- innovative methods --- reservoir inflow forecasting --- simulation --- extreme events --- climate variability --- sparse monitoring network --- weighted mean analogue --- sampling errors --- precipitation --- drought indices --- discrete wavelet --- SWSI --- hyetograph --- trends --- climate change --- SIAP --- Kabul river basin --- Hurst exponent --- extreme rainfall --- evolutionary strategy --- the Cauca River --- hydrological drought --- global warming --- least square support vector regression --- polynomial normal transform --- TRMM --- satellite data --- Fiji --- heavy storm --- flood regime --- compound events --- random forest --- uncertainty --- seasonal climate forecast --- INDC pledge --- Pakistan --- wavelet artificial neural network --- HBV model --- temperature --- APCC Multi-Model Ensemble --- meteorological drought --- flow regime --- high resolution --- rainfall --- clausius-clapeyron scaling --- statistical downscaling --- ENSO --- forecasting --- variation analogue --- machine learning --- extreme rainfall analysis --- hydrological extremes --- multivariate modeling --- monsoon --- non-stationary --- support vector machine --- ANN model --- stretched Gaussian distribution --- drought prediction --- non-normality --- statistical analysis --- extreme precipitation exposure --- drought analysis --- extreme value theory --- streamflow --- flood management

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

In geodesy and geoinformation science, as well as in many other technical disciplines, it is often not possible to directly determine the desired target quantities. Therefore, the unknown parameters must be linked with the measured values by a mathematical model which consists of the functional and the stochastic models. The functional model describes the geometrical–physical relationship between the measurements and the unknown parameters. This relationship is sufficiently well known for most applications. With regard to the stochastic model, two problem domains of fundamental importance arise: 1. How can stochastic models be set up as realistically as possible for the various geodetic observation methods and sensor systems? 2. How can the stochastic information be adequately considered in appropriate least squares adjustment models? Further questions include the interpretation of the stochastic properties of the computed target values with regard to precision and reliability and the use of the results for the detection of outliers in the input data (measurements). In this Special Issue, current research results on these general questions are presented in ten peer-reviewed articles. The basic findings can be applied to all technical scientific fields where measurements are used for the determination of parameters to describe geometric or physical phenomena.

History of engineering & technology --- EM-algorithm --- multi-GNSS --- PPP --- process noise --- observation covariance matrix --- extended Kalman filter --- machine learning --- GNSS phase bias --- sequential quasi-Monte Carlo --- variance reduction --- autoregressive processes --- ARMA-process --- colored noise --- continuous process --- covariance function --- stochastic modeling --- time series --- elementary error model --- terrestrial laser scanning --- variance-covariance matrix --- terrestrial laser scanner --- stochastic model --- B-spline approximation --- Hurst exponent --- fractional Gaussian noise --- generalized Hurst estimator --- very long baseline interferometry --- sensitivity --- internal reliability --- robustness --- CONT14 --- Errors-In-Variables Model --- Total Least-Squares --- prior information --- collocation vs. adjustment --- mean shift model --- variance inflation model --- outlierdetection --- likelihood ratio test --- Monte Carlo integration --- data snooping --- GUM analysis --- geodetic network adjustment --- stochastic properties --- random number generator --- Monte Carlo simulation --- 3D straight line fitting --- total least squares (TLS) --- weighted total least squares (WTLS) --- nonlinear least squares adjustment --- direct solution --- singular dispersion matrix --- laser scanning data