Results 51 to 60 of about 13,792,612 (176)
International Journal of Mathematics and Mathematical Sciences, Volume 2025, Issue 1, 2025.Stock price forecasting is complex due to the nonlinear and nonstationary nature of financial time series. This study proposes a hybrid variational mode decomposition (VMD)–generalized autoregressive conditional heteroskedasticity (GARCH)–long short‐term memory (LSTM) model to predict Airtel’s stock prices, integrating VMD, GARCH, and LSTM networks ...
John Kamwele Mutinda +3 more
wiley +1 more source
Forecasting Volatility of Dhaka Stock Exchange: Linear Vs Non-linear models
International Journal of Science and Engineering, 2012 Prior information about a financial market is very essential for investor to invest money on parches share from the stock market which can strengthen the economy.
Masudul Islam +2 more
doaj +1 more source
Investor Sentiment, Unexpected Inflation, and Bitcoin Basis Risk
Journal of Futures Markets, Volume 44, Issue 11, Page 1807-1831, November 2024.ABSTRACT The introduction of regulated CME futures contracts on Bitcoin in 2017 raised an expectation that cryptocurrencies would become part of mainstream financial markets. This also heightened links between traditional markets and Bitcoin, implying that the cryptocurrency would be subject to systematic spillovers. This paper uses high‐frequency data
Thomas Conlon, Shaen Corbet, Les Oxley
wiley +1 more source
Journal of Time Series Analysis, Volume 45, Issue 6, Page 910-930, November 2024.Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model and its variations have been widely adopted in the study of financial volatilities, while the extension of GARCH‐type models to high‐dimensional data is always difficult because of over‐parameterization and computational complexity. In this article, we propose a multi‐variate GARCH‐
Yue Pan, Jiazhu Pan
wiley +1 more source
Pemodelan Return Ihsg Periode 15 September 1998 – 13 September 2013 Menggunakan Threshold Generalized Autoregressive Conditional Heteroskedasticity (Tgarch(1,1)) Dengan Dua [PDF]
, 2013 Pemodelan Return Ihsg Periode 15 September 1998 – 13 September 2013 Menggunakan Threshold Generalized Autoregressive Conditional Heteroskedasticity (Tgarch(1,1)) Dengan Dua Threshold Abstrak Data Time Series Merupakan Data Pengamatan Yang ...
Sholihah, SumaSuci
core
A Hybrid GARCH and Deep Learning Method for Volatility Prediction
Journal of Applied Mathematics, Volume 2024, Issue 1, 2024.Volatility prediction plays a vital role in financial data. The time series movements of stock prices are commonly characterized as highly nonlinear and volatile. This study is aimed at enhancing the accuracy of return volatility forecasts for stock prices by investigating the prediction of their price volatility through the integration of diverse ...
Hailabe T. Araya +3 more
wiley +1 more source
, 2021 Bimonthly magazine discussing topics related to aviation and model airplane engines including collecting, restoring, maintaining, and identifying engines, along with ...
The Model Museum, Daniels, Timothy J.
core +1 more source
, 2016 ABSTRAK Ekonomi merupakan aspek penting suatu negara, beragamnya bentuk kegiatan ekonomi menggambarkan pentingnya ekonomi bagi masyarakat. Salah satu kegiatan ekonomi adalah investasi, investasi saat ini sangat beragam salah satunya investasi emas. Emas
Puspita, Entit +2 more
core
, 2013 Penelitian ini bertujuan untuk menetapkan pemodelan nilai tukar Rupiah terhadap Dollar Amerika dengan Autoregressive Conditional Heteroscedasticity (ARCH), Generalized Autoregressive Conditional Heteroscedasticity (GARCH) atau Threshold Generalized ...
Herdiansyah, Vindri +2 more
core +2 more sources