Results 11 to 20 of about 583,999 (299)
, 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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, 2017 In Sects. 2.3 and 4.2, the common volatility modelling oversights that exist in literature were highlighted. In this Chapter, we discuss the potential impact of these oversights on volatility forecasting and provide a methodology for testing the impact of these oversights on the forecasting accuracy of volatility models.
Mostafa, F, Dillon, T, Chang, E
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Data-driven adaptive hybrid models for exchange rate return forecasting [PDF]
Frontiers in Big DataBackgroundExchange-rate return forecasting is challenging because financial time series may exhibit linearity, nonlinearity, regime-switching behavior, and volatility.
Olumide Sunday Adesina +1 more
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Comparing various GARCH-type models in the estimation and forecasts of volatility of S&P 500 returns during Global Finance Crisis of 2008 and COVID-19 financial crisis [PDF]
SHS Web of Conferences, 2023 In this study, we utilize various GARCH-type models to estimate and forecast volatility on S&P 500 returns and compare the results between the two financial crises, the GFC of 2008 (Global Financial Crisis of 2008) and the COVID-19 financial crisis ...
Chen Xuanyu
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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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Global economic policy uncertainty and stock volatility: evidence from emerging economies
Journal of Applied Economics, 2021 We investigate the impact of the global economic policy uncertainty (GEPU) on stock volatility for nine emerging economies (Brazil, Russia, India, China, South Africa, Mexico, Indonesia, South Korea, and Turkey).
Xiaoling Yu, Yirong Huang, Kaitian Xiao
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Modeling and Forecasting the Volatility of NIFTY 50 Using GARCH and RNN Models
Economies, 2022 The stock market is constantly shifting and full of unknowns. In India in 2000, technological advancements led to significant growth in the Indian stock market, introducing online share trading via the internet and computers.
Vanshu Mahajan +2 more
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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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Forecasting Volatility in Asian Stock Markets: Contributions of Local, Regional, and Global Factors
Asian Development Review, 2011 This paper examines volatility forecasting for the broad market indices of 12 Asian stock markets. After considering the long memory in volatility and volatility jumps, the paper incorporates local, regional, and global factors into a heterogeneous ...
Jianxin Wang
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