Results 31 to 40 of about 552,350 (298)
Historical Perspectives in Volatility Forecasting Methods with Machine Learning
RisksVolatility 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
Applied Sciences, 2020 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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Modelling and Forecasting Noisy Realized Volatility [PDF]
Several methods have recently been proposed in the ultra high frequency financial literature to remove the effects of microstructure noise and to obtain consistent estimates of the integrated volatility (IV) as a measure of ex-post daily volatility. Even
Michael McAleer +2 more
core +6 more sources
International Journal of Management, Finance and AccountingThis study examines whether realised range volatility improves multivariate volatility forecasting performance in an emerging equity market setting. A comparison between vector heterogeneous autoregressive models constructed with realised volatility ...
Mariam Mohamed Abdelwahab Mohamed Badawi +3 more
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Volatility Forecasting: Downside Risk, Jumps and Leverage Effect
Econometrics, 2016 We provide empirical evidence of volatility forecasting in relation to asymmetries present in the dynamics of both return and volatility processes. Using recently-developed methodologies to detect jumps from high frequency price data, we estimate the ...
Francesco Audrino, Yujia Hu
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Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To characterize the demographic, clinical, and laboratory features of the Chinese patients of genetic Creutzfeldt‐Jakob disease with T188K variant (T188K‐gCJD), the most common subtype of genetic prion diseases (gPrDs) in China. Methods In this nationwide retrospective study, data from 98 genetically confirmed T188K‐gCJD patients ...
Chun‐Jie Li +11 more
wiley +1 more source
A general equilibrium approach to pricing volatility risk.
PLoS ONE, 2019 This paper provides a general equilibrium approach to pricing volatility. Existing models (e.g., ARCH/GARCH, stochastic volatility) take a statistical approach to estimating volatility, volatility indices (e.g., CBOE VIX) use a weighted combination of ...
Jianlei Han +4 more
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Volatility Forecast in Crises and Expansions [PDF]
Journal of Risk and Financial Management, 2015 We build a discrete-time non-linear model for volatility forecasting purposes. This model belongs to the class of threshold-autoregressive models, where changes in regimes are governed by past returns. The ability to capture changes in volatility regimes and using more accurate volatility measures allow outperforming other benchmark models, such as ...
openaire +3 more sources
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background Hereditary Spastic Paraplegia (HSP) comprises a group of rare genetic diseases characterized by length‐dependent axonal degeneration of the corticospinal tracts and dorsal columns, whose main clinical feature is spastic gait. Pathogenic variants in the SPG4 gene cause Spastic Paraplegia Type 4 (SPG4‐HSP), the most common form of HSP.
Gaia Fattorini +12 more
wiley +1 more source
Volatility Forecasting Models and Market Co-Integration: A Study on South-East Asian Markets
Indonesian Capital Market Review, 2014 Volatility forecasting is an imperative research field in financial markets and crucial component in most financial decisions. Nevertheless, which model should be used to assess volatility remains a complex issue as different volatility models result in ...
Erie Febrian, Aldrin Herwany
doaj +1 more source