Time series modeling for predictive purpose has been an active research area of machine
learning for many years. However, no sufficiently comprehensive and meanwhile …

The performance of portfolio model can be improved by introducing stock prediction based
on machine learning methods. However, the prediction error is inevitable, which may bring …

In an attempt to combat global warming, many countries have been introducing carbon
trading schemes, which, in turn, has led to increased research interest in carbon price …

We employ alpha-stable distribution to dynamically compute risk exposure measures for the
five most traded cryptocurrencies. Returns are jointly modeled with an ARMA-GARCH …

The objective of this research is to examine and compare the mean reversion phenomenon
in developed and emerging stock markets. An important aim is to measure and compare the …

Low-risk pricing anomalies, characterized by lower returns in higher-risk stocks, are
prevalent in equity markets and challenge traditional asset pricing theory. Previous studies …

Accurate predictions of stock markets are important for investors and other stakeholders of
the equity markets to formulate profitable investment strategies. The improved accuracy of a …

The time series data of financial markets are nonlinear, owing to rapid data accumulation.
Thus, research on stock price prediction has always been a challenge. This study proposes …

I propose a prediction method for weak-sense stationary time series with finite variance and
α-stable innovations using the Riemann sum approximation of the spectral representation …

We propose a novel approach for model-free time series forecasting. Unlike most existing
methods, the proposed method does not rely on parametric error distributions nor assume …