Bitcoin price prediction poses a considerable challenge due to its intricate, ever-changing nature, nonlinear trends and susceptibility various influencing factors, rendering simplistic models inadequate for accurate forecasts. One of the commonly used data mining methods in field machine learning is support vector machine. The purpose this study assess limitations existing bitcoin forecasting approaches conventional machines. Specifically, machine’s features comprise other seven...

This article deals with an European option pricing via proportional transaction costs in the incomplete environment and without arbitrage opportunities under two long memory versions of Heston model. Observing introducing a traded proxy for volatility modern market, we use conditional expectation delta hedging strategies present generalized fractional Ito formula to obtain price partial differential equations (PDEs). To solve these PDEs, apply finite difference method employ K-antithetic...

Artificial neural networks are popular data-driven models extensively used for predicting the prices of precious metals. This study suggests an optimized artificial network model specifically designed monthly price metals forecasting. For this purpose, Lévy flight optimization algorithm is presented to adjust weights and biases involved in proposed network. In a groundbreaking approach within time series forecasting literature, we enhance precision short-term forecasts by organizing features...

The aim of this paper is to present a new hybrid algorithm for pricing financial derivatives in the arithmetic Asian options. In paper, two variance reduction techniques are combined, multiple control variates (MCV) and antithetic (AV). We propose an efficient options based on AV MCV procedures. A detailed numerical study illustrates efficiency proposed algorithm.

Investors always pay attention to the two factors of return and risk in portfolio optimization. There are different metrics for calculation factor, among which most important one is Conditional Value at Risk (CVaR). On other hand, Data Envelopment Analysis (DEA) can be used form optimal evaluate its efficiency. In these models, created by stocks or companies with high Since search space vast actual markets there limitations such as number assets their weight, optimization problem becomes...

Purpose The purpose of this paper is to evaluate a European option using the fractional version Black-Scholes model. Design/methodology/approach In paper, authors employ block-pulse operational matrix algorithm approximate solution equation with initial condition for pricing problem. Findings derivative will be described in Caputo sense paper. show accuracy and computational efficiency proposed through some numerical examples. Originality/value This first that considers an alternative