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Forecasting stock market realized variance with Echo State Neural Networks
Fičura, Milan - In: European financial and accounting journal : EFAJ 12 (2017) 3, pp. 145-156
results show that asymmetric models generally outperform symmetric ones, indicating that a correlation between volatility and …
Persistent link: https://www.econbiz.de/10011818288
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Risk and policy uncertainty on stock-bond return correlations : evidence from the US markets
Chiang, Thomas C. - In: Risks : open access journal 8 (2020) 2/58, pp. 1-16
positively correlated with economic policy uncertainty, however, are negatively correlated with the monetary policy and fiscal … policy uncertainties. Correlation coefficients between stock and bond returns are positively related to total policy … uncertainty for returns of the Dow-Jones Industrial Average (DJIA) and the S&P 500 Value stock index (VALUE), but negatively …
Persistent link: https://www.econbiz.de/10012292914
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PREDICTING STOCK RETURN AND VOLATILITY WITH MACHINE LEARNING AND ECONOMETRIC MODELS : A COMPARATIVE CASE STUDY OF THE BALTIC STOCK MARKET
Nõu, Anders; Lapitskaya, Darya; Eratalay, Mustafa Hakan; … - 2022
For stock market predictions, the essence of the problem is usually predicting the magnitude and direction of the stock price movement as accurately as possible. There are different approaches (e.g., econometrics and machine learning) for predicting stock returns. However, it is non-trivial to...
Persistent link: https://www.econbiz.de/10013305881
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Realized semicovariances : looking for signs of direction inside the covariance matrix
Bollerslev, Tim; Patton, Andrew J.; Quaedvlieg, Rogier - 2017 - This version: September 5, 2017
components and the mixed-sign component load differently on economic information concerning stochastic correlation and jumps. The …
Persistent link: https://www.econbiz.de/10012116691
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Dynamic stock-bond return correlations and financial market uncertainty
Chiang, Thomas C.; Li, Jiandong; Yang, Sheng-Yung - In: Review of quantitative finance and accounting 45 (2015) 1, pp. 59-88
Persistent link: https://www.econbiz.de/10011333137
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Effects of macroeconomic uncertainty on the stock and bond markets
Asgharian, Hossein; Christiansen, Charlotte; Hou, Ai Jun - In: Finance research letters 13 (2015), pp. 10-16
Persistent link: https://www.econbiz.de/10011552317
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Regime shifts in a long-run risks model of stock and treasury bond markets
Li, Kai; Xu, Chenjie - In: China finance review international 12 (2022) 4, pp. 541-570
Persistent link: https://www.econbiz.de/10013453608
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Macro-Finance Determinants of the Long-Run Stock-Bond Correlation : The DCC-MIDAS Specification
Asgharian, Hossein - 2015
We investigate long-run stock-bond correlation using a model that combines the dynamic conditional correlation model … with the mixed-data sampling approach and allows long-run correlation to be affected by macro-finance factors (historical …, and market uncertainty. Macro-finance factors, particularly their forecasts, are good at forecasting long-run stock …
Persistent link: https://www.econbiz.de/10013033824
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Realised Volatility Forecasting : Machine Learning via Financial Word Embedding
Rahimikia, Eghbal; Zohren, Stefan; Poon, Ser-Huang - 2021
We develop FinText, a novel, state-of-the-art, financial word embedding from Dow Jones Newswires Text News Feed Database. Incorporating this word embedding in a machine learning model produces a substantial increase in volatility forecasting performance on days with volatility jumps for 23...
Persistent link: https://www.econbiz.de/10013217713
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Machine Learning for Realised Volatility Forecasting
Rahimikia, Eghbal; Poon, Ser-Huang - 2021
This paper examines, for the first time, the performance of machine learning models in realised volatility forecasting using big data sets such as LOBSTER limit order books and news stories from Dow Jones News Wires for 28 NASDAQ stocks over a sample period of July 27, 2007, to November 18,...
Persistent link: https://www.econbiz.de/10013222880