Results 11 to 20 of about 507,052 (301)
, 2005 Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. This chapter provides a selective survey of the most important theoretical developments and empirical insights to emerge from this burgeoning literature, with a distinct focus on forecasting applications ...
Andersen, Torben G. +3 more
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Probing Trading Activities in Commodity Futures Market via Volatility Modeling [PDF]
E3S Web of Conferences, 2021 In the context of the great fluctuation of the global financial market, it is particularly important to forecast the changing futures market. Inspired by the utilization of the Heterogeneous Autoregressive model of the Realized Volatility (HAR-RV) model ...
Yan Yunxi, Hu Shiyou
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Forecasting Coherent Volatility Breakouts [PDF]
SSRN Electronic Journal, 2014 The paper develops an algorithm for making long-term (up to three months ahead) predictions of volatility reversals based on long memory properties of financial time series. The approach for computing fractal dimension using sequence of the minimal covers with decreasing scale (proposed in [1]) is used to decompose volatility into two0dynamic ...
Didenko, Alexander +2 more
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Forecasting the Volatility of the Cryptocurrency Market by GARCH and Stochastic Volatility
Mathematics, 2021 This study examines the volatility of nine leading cryptocurrencies by market capitalization—Bitcoin, XRP, Ethereum, Bitcoin Cash, Stellar, Litecoin, TRON, Cardano, and IOTA-by using a Bayesian Stochastic Volatility (SV) model and several GARCH models ...
Jong-Min Kim, Chulhee Jun, Junyoup Lee
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The effect of Business Strategies on the Company's Information Environment [PDF]
مطالعات تجربی حسابداری مالی, 2018 In this study, the effect of business strategy on the Company's Information Environment (Information Asymmetry, stock returns Volatility, Earning Forecast Errors), is studied.
Robab Shakeri, Mohammad Marfou
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Journal of Forecasting, 2006 AbstractIn this paper, we investigate the time series properties of S&P 100 volatility and the forecasting performance of different volatility models. We consider several nonparametric and parametric volatility measures, such as implied, realized and model‐based volatility, and show that these volatility processes exhibit an extremely slow mean ...
Nikolay Gospodinov +2 more
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The Importance of the Volatility Risk Premium for Volatility Forecasting [PDF]
SSRN Electronic Journal, 2013 In this paper, we study the role of the volatility risk premium for the forecasting performance of implied volatility. We introduce a non-parametric and parsimonious approach to adjust the model-free implied volatility for the volatility risk premium and implement this methodology using more than 20 years of options and futures data on three major ...
Prokopczuk, Marcel, Wese Simen, Chardin
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The Improved Value-at-Risk for Heteroscedastic Processes and Their Coverage Probability
Journal of Probability and Statistics, 2020 A risk measure commonly used in financial risk management, namely, Value-at-Risk (VaR), is studied. In particular, we find a VaR forecast for heteroscedastic processes such that its (conditional) coverage probability is close to the nominal. To do so, we
Khreshna Syuhada
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Forecasting of onion (Allium cepa) price and volatility movements using ARIMAX-GARCH and DCC models
The Indian Journal of Agricultural Sciences, 2020 In the present investigation an attempt has been made to forecast and understand the volatility transmission in onion prices for three vital markets in Maharashtra, viz. Lasalgaon, Pune and Nagpur. The ARIMAX-GARCH model was employed to estimate mean and
Sourav Ghosh +4 more
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Mathematics, 2023 Forecasting stock markets is an important challenge due to leptokurtic distributions with heavy tails due to uncertainties in markets, economies, and political fluctuations.
Özgür Ömer Ersin, Melike Bildirici
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