Given the high potential benefits and impacts of accurate stock market predictions,
considerable research attention has been devoted to time series forecasting for stock …

Cryptocurrencies are currently traded worldwide, with hundreds of different currencies in
existence and even more on the way. This study implements some statistical and machine …

Accurate modeling for forecasting of stock market volatility is a widely interesting research
area both in academia as well as financial markets. This paper proposes an innovative …

In this paper a new hybrid model integrating an interval type2 fuzzy logic system (IT2FLS)
with a computationally efficient functional link artificial neural network (CEFLANN) and an …

In this paper we consider the European option valuation problem. We assume that the
underlying asset follows a geometric Levy process. The log-price is a sum of a Brownian …

Volatility modeling and forecasting play a key role in asset allocation, risk management,
derivatives pricing and policy making. The purpose of this paper is to develop an evolving …

This paper proposes an innovative semiparametric nonlinear fuzzy-EGARCH-ANN model to
solve the problem of accurate modeling for forecasting stock market volatility. This model …

Generalized autoregressive conditional heteroskedasticity (GARCH) is one of the most
popular models for time-series forecasting. The GARCH model uses a maximum likelihood …

Важную роль при принятии инвестиционных решений играет корректное
моделирование и успешное прогнозирование волатильности доходности финансовых …

In finance, accurately forecasting volatility of any financial asset is very important due to its
usefulness in areas such as option pricing, decision making, and risk management …