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Forecasting Foreign Exchange Volatility Using Deep Learning Autoencoder-LSTM Techniques
Since the breakdown of the Bretton Woods system in the early 1970s, the foreign exchange (FX) market has become an important focus of both academic and practical research.
Gunho Jung, Sun-Yong Choi
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Measurement and forecasting of volatility and income correlation are achieved by non-parametric methods using high-frequency price data. Due to accurate calculations of conditional volatility and correlation forecasting, it is possible to correctly ...
John Guyomey, Andrey Zaitsev
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Measuring and Forecasting Volatility in Chinese Stock Market Using HAR-CJ-M Model
Basing on the Heterogeneous Autoregressive with Continuous volatility and Jumps model (HAR-CJ), converting the realized Volatility (RV) into the adjusted realized volatility (ARV), and making use of the influence of momentum effect on the volatility, a ...
Chuangxia Huang +3 more
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Modeling and Forecasting the Volatility of Eastern European Emerging Markets
This study has attempted to seek a volatility forecasting model that can reflect sufficiently the long memory characteristic in the volatility of four Eastern European emerging stock markets, naThis study has attempted to seek a volatility forecasting ...
Sang Hoon Kang , Seong-Min Yoon
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There is increasing evidence that European Union allowance (EUA) futures return distributions exhibit features of time-varying higher moments (skewness and kurtosis), which plays an important role in modeling and forecasting EUA futures volatility ...
Xinyu Wu, Xueting Mei, Zhongming Ding
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Forecasting exchange rate volatility: GARCH models versus implied volatility forecasts [PDF]
This study investigates whether different specifications of univariate GARCH models can usefully forecast volatility in the foreign exchange market. The study compares in-sample forecasts from symmetric and asymmetric GARCH models with the implied volatility derived from currency options for four dollar parities.
Pilbeam, K., Langeland, K. N.
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Historical Perspectives in Volatility Forecasting Methods with Machine Learning
Volatility forecasting for financial institutions plays a pivotal role across a wide range of domains, such as risk management, option pricing, and market making.
Zhiang Qiu +3 more
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Hybrid Forecasting Models Based on the Neural Networks for the Volatility of Bitcoin
In this paper, we study the volatility forecasts in the Bitcoin market, which has become popular in the global market in recent years. Since the volatility forecasts help trading decisions of traders who want a profit, the volatility forecasting is an ...
Monghwan Seo, Geonwoo Kim
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Volatility Forecast Comparison Using Imperfect Volatility Proxies [PDF]
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Adding dummy variables: A simple approach for improved volatility forecasting in electricity market
This study used dummy variables to measure the influence of day-of-the-week effects and structural breaks on volatility. Considering day-of-the-week effects, structural breaks, or both, we propose three classes of HAR models to forecast electricity ...
Xu Gong, Boqiang Lin
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