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Recently, considerable attention has been placed on the development and application of tools useful for the analysis of the high-dimensional and/or high-frequency datasets that now dominate the landscape. The purpose of this Special Issue is to collect both methodological and empirical papers that develop and utilize state-of-the-art econometric techniques for the analysis of such data.

Economics, finance, business & management --- level, slope, and curvature of the yield curve --- Nelson-Siegel factors --- supervised factor models --- combining forecasts --- principal components --- Minimum variance portfolio --- risk --- shrinkage --- S&amp --- P 500 --- high-frequency --- volatility --- forecasting --- realized measures --- bivariate GARCH --- Japanese candlestick --- ordered fuzzy number --- Kosiński’s number --- oriented fuzzy number --- dynamic analysis of securities --- integrated volatility --- high-frequency data --- jumps --- realized skewness --- cross-sectional stock returns --- signed jump variation --- long-range dependence --- log periodogram regression --- smoothed periodogram --- subsampling --- intraday returns --- portfolio selection --- maximum diversification --- regularization --- level, slope, and curvature of the yield curve --- Nelson-Siegel factors --- supervised factor models --- combining forecasts --- principal components --- Minimum variance portfolio --- risk --- shrinkage --- S&amp --- P 500 --- high-frequency --- volatility --- forecasting --- realized measures --- bivariate GARCH --- Japanese candlestick --- ordered fuzzy number --- Kosiński’s number --- oriented fuzzy number --- dynamic analysis of securities --- integrated volatility --- high-frequency data --- jumps --- realized skewness --- cross-sectional stock returns --- signed jump variation --- long-range dependence --- log periodogram regression --- smoothed periodogram --- subsampling --- intraday returns --- portfolio selection --- maximum diversification --- regularization