Purpose The increasing globalization and technological advancements have increased the
information spillover on stock markets from various variables. However, there is a dearth of a …

Purpose This study aims to examine the theoretical foundations for multivariate portfolio
optimization algorithms under illiquid market conditions. In this study, special emphasis is …

We review the past 25 years of research into time series forecasting. In this silver jubilee
issue, we naturally highlight results published in journals managed by the International …

Accurate forecasting of volatility from financial time series is paramount in financial decision
making. This paper presents a novel, Particle Swarm Optimization (PSO)-trained Quantile …

Financial market volatility forecasting is one of today's most important areas of expertise for
professionals and academics in investment, option pricing, and financial market regulation …

Practical Issues in the Analysis of Univariate GARCH Models Page 1 Practical Issues in the
Analysis of Univariate GARCH Models Eric Zivot Abstract This chapter gives a tour through …

Our objective in this paper is to examine whether one can use option-implied information to
improve the selection of mean-variance portfolios with a large number of stocks, and to …

This study examines whether Bitcoin, a digital decentralized currency, can become a viable
alternative to fiat currencies. Bitcoin currently does not fulfill the criteria of being a currency …

The growth of financial texts in the wake of big data has challenged most organizations and
brought escalating demands for analysis tools. In general, text streams are more challenging …

We introduce a new regression framework, Gaussian process regression networks (GPRN),
which combines the structural properties of Bayesian neural networks with the non …