Quantum mechanics studies nature and its behavior at the scale of atoms and subatomic
particles. By applying quantum mechanics, a lot of problems can be solved in a more …

In this systematic literature review, we examine the existing studies predicting realized
volatility and implied volatility indices using artificial intelligence and machine learning. We …

The term Machine Learning (ML) was introduced by Arthur Samuel while working for IBM in
1959, mainly to describe the pattern recognition tasks that delivered the “learning” …

In this work, I studied whether news media sentiments have an impact on Bitcoin volatility. In
doing so, I applied three different range-based volatility estimates along with two different …

We extend existing studies by considering the higher-order moments relationships among
crude oil, gold, and Bitcoin markets. Using high-frequency data from December 2, 2014 to …

This study examines the connectedness in high-order moments between cryptocurrency,
major stock (US, UK, Eurozone, and Japan), and commodity (gold and oil) markets. Using …

Academic research relies heavily on exogenous drivers to improve the forecasting accuracy
of Bitcoin volatility. The present study provides additional insight into the role of both …

Purpose Cryptocurrencies such as Bitcoin (BTC) attracted a lot of attention in recent months
due to their unprecedented price fluctuations. This paper aims to propose a new method for …

Bitcoin's growing use as a financial asset and transaction instrument has economic and
monetary effects. In this paper, we examine the short-and long-term interactions between …

Objectives: Accurate prediction of stock market returns is a very challenging task due to the
volatile and non-linear nature of the financial stock markets. In this work, we consider …