The cryptocurrency market is one of the most dynamic and volatile markets in the world's
financial ecosystem, and investment landscapes in the US financial market have changed …

Time-series forecasting is essential for system analysis. Many traditional studies have paid
attention to individual stock-oriented solutions and disregarded general approaches on …

This study introduces a finetuned hybrid forecasting model combining both Discrete Wavelet
Transform (DWT) and Long Short-Term Memory network (LSTM) to predict dirty and clean …

This paper studies the Bitcoin volatility forecasting performance between popular traditional
econometric models and machine learning techniques. We compare the 1-day to 2-month …

This study proposes a novel parameter tuning strategy, daily dynamic tuning, and applies it
to forecast volatility in the cryptocurrency market. Comparative analysis with HAR-RV and …

We investigate the predictive relationship between climate change indexes and international
corporate debt market volumes, focusing on forecasting domestic and foreign net purchases …

The notion that investors shift to gold during economic market crises remains unverified for
many cryptocurrency markets. This paper investigates the connectedness between the 10 …

Utilizing a generative regime switching framework, we perform Monte-Carlo simulations of
asset returns for Value at Risk threshold estimation. Using equity markets and long term …

We investigate the benefits of using intraday realized volatility (RV) commonality, and
propose a novel non-parametric framework for forecasting one-day ahead intraday RV (1D …

This study examines the interdependence structure between cryptocurrencies and Chinese
financial assets. The results show a substantial multifractal cross-correlation between …