J Ruf, W Wang - arXiv preprint arXiv:1911.05620, 2019 - arxiv.org
Neural networks have been used as a nonparametric method for option pricing and hedging since the early 1990s. Far over a hundred papers have been published on this topic. This …
HY Kim, CH Won - Expert Systems with Applications, 2018 - Elsevier
Volatility plays crucial roles in financial markets, such as in derivative pricing, portfolio risk management, and hedging strategies. Therefore, accurate prediction of volatility is critical …
M Qiu, Y Song - PloS one, 2016 - journals.plos.org
In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index; hence, there is a great deal of research being conducted regarding the …
This paper provides an evaluation of the predictive performance of the volatility of three cryptocurrencies and three currencies with recognized stores of value using daily and hourly …
F Rundo, F Trenta, AL Di Stallo, S Battiato - Applied Sciences, 2019 - mdpi.com
Featured Application The described approaches can be used in various applications in the field of quantitative finance from HFT trading systems to financial portfolio allocation and …
A Bahrammirzaee - Neural Computing and Applications, 2010 - Springer
Nowadays, many current real financial applications have nonlinear and uncertain behaviors which change across the time. Therefore, the need to solve highly nonlinear, time variant …
JZ Wang, JJ Wang, ZG Zhang, SP Guo - Expert Systems with Applications, 2011 - Elsevier
Stock prices as time series are non-stationary and highly-noisy due to the fact that stock markets are affected by a variety of factors. Predicting stock price or index with the noisy data …
Forecasting the stock market price index is a challenging task. The exponential smoothing model (ESM), autoregressive integrated moving average model (ARIMA), and the back …
D Pradeepkumar, V Ravi - Applied Soft Computing, 2017 - Elsevier
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 …