Results 41 to 50 of about 114,012 (307)
The cryptocurrency market is highly volatile compared to traditional financial markets. Hence, forecasting its volatility is crucial for risk management. In this paper, we investigate CryptoQuant data (e.g.
Dorien Herremans, Kah Wee Low
doaj +1 more source
Evaluating Volatility and Correlation Forecasts [PDF]
This chapter considers the problems of evaluation and comparison of volatility forecasts, both univariate (variance) and multivariate (covariance matrix and/or correlation). We pay explicit attention to the fact that the object of interest in these applications is unobservable, even ex post, and so the evaluation and comparison of volatility forecasts ...
Andrew J. Patton, Kevin Sheppard
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The thermal diffusivity of MgO‐C refractories is highly sensitive to sample preparation and processing procedures. In this article, the effects of coking sequence, machining conditions, structural inhomogeneity, and graphite coating application on measurements using laser flash apparatus are systematically investigated.
Luyao Pan +4 more
wiley +1 more source
Innovative Study on Volatility Prediction Model for New Energy Stock Indices
Stock market volatility is a pivotal research area in finance, and accurately forecasting stock market volatility has long been a challenge for both academia and practice.
Yanguo Li, Chao Long
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Volatility Forecasting Model-Free Implied Volatility [PDF]
Volatility in the financial market is an important variable, which in asset pricing, investment, risk management and policy-making process plays an important role. Methods for predicting volatility are mainly divided into two categories, one is the historical information method, based on the historical information to predict the future volatility; the ...
Guibin Lu, Jingfei Cheng
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Stabilization of L‐PBF Ni50.7Ti49.3 under low‐cycle loading was investigated. Recoverable strain after cycling was dependent on the amount of applied load. Recovery ratio was 53.4% and 35.1% at intermediate and high load, respectively. The maximum total strain reached 10.3% at a high load of 1200 MPa.
Ondřej Červinek +5 more
wiley +1 more source
Volatility forecasts: a continuous time model versus discrete time models [PDF]
This paper compares empirically the forecasting performance of a continuous time stochastic volatility model with two volatility factors (SV2F) to a set of alternative models (GARCH, FIGARCH, HYGARCH, FIEGARCH and Component GARCH).
Veiga, Helena
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Modeling and forecasting exchange rate volatility in time-frequency domain [PDF]
This paper proposes an enhanced approach to modeling and forecasting volatility using high frequency data. Using a forecasting model based on Realized GARCH with multiple time-frequency decomposed realized volatility measures, we study the influence of ...
Barunik, Jozef +2 more
core +1 more source
Electrical Conductivities of Conductors, Semiconductors, and Their Mixtures at Elevated Temperatures
This article presents a comprehensive review of temperature‐dependent electrical conductivity data for multiple material classes at elevated temperatures, highlighting a persistent conductivity gap between metals and semiconductors in the range of 102$\left(10\right)^{2}$– 107$\left(10\right)^{7}$ S/m. Metal–ceramic irregular metamaterials are proposed
Valentina Torres Nieto, Marcia A. Cooper
wiley +1 more source
Geometry‐driven thermal behavior in wire‐arc additive manufacturing (WAAM) influences microstructural evolution during nonequilibrium solidification of a chemically complex Fe–Cr–Nb–W–Mo–C nanocomposite system. By comparing different deposits configurations, distinct entropy–cooling rate correlations, segregation, and carbide evolution are revealed ...
Blanca Palacios +5 more
wiley +1 more source

