Results 81 to 90 of about 552,350 (298)

Engineering Local Polar Frustration in Lead‐Free Dielectric Ceramics for Ultrahigh Normalized Energy Storage Performance

Advanced Functional Materials, EarlyView.
Local polar frustration is engineered to stabilize an ergodic relaxer through nanoscale multiphase coexistence, suppressing long‐range ferroelectric order while preserving high polarization. This strategy delivers ultrahigh low‐field normalized energy density (Wa >0.020 mC cm−2), exceptional thermal stability, and a transferable design principle for ...
Hareem Zubairi, Muhammad Wasim, Songhao Fu, Xiaojiao Liu, Annette K Kleppe, Zhilun Lu, Antonio Feteira, Diming Xu, Joseph C.A. Prentice, Ge Wang   +9 more
wiley   +1 more source

Volatility forecasts: a continuous time model versus discrete time models [PDF]

, 2006
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
core   +1 more source

Modelling the implied volatility – A case of EUR/PLN currency options

International Journal of Management and Economics
Implied volatility, quoted by market makers for Over-the-Counter foreign exchange options, constructs a volatility surface that facilitates the pricing of all vanilla contracts.
Mielus Piotr
doaj   +1 more source

Forecasting Stock Market Volatility Using CNN-BiLSTM-Attention Model with Mixed-Frequency Data

Mathematics
Existing stock volatility forecasting models predominantly rely on same-frequency market data while neglecting mixed-frequency integration and face particular challenges in incorporating low-frequency macroeconomic variables that exhibit temporal ...
Yufeng Zhang, Tonghui Zhang, Jingyi Hu
doaj   +1 more source

Volatility forecasting and value-at-risk estimation in emerging markets: the case of the stock market index portfolio in South Africa

South African Journal of Economic and Management Sciences, 2011
Accurate modelling of volatility is important as it relates to the forecasting of Value-at-Risk (VaR). The RiskMetrics model to forecast volatility is the benchmark in the financial sector.
Lumengo Bonga-Bonga, George Mutema
doaj   +1 more source

Forecasting S&P 500 Daily Volatility using a Proxy for Downward Price Pressure [PDF]

This paper decomposes volatility proxies according to upward and downward price movements in high-frequency financial data, and uses this decomposition for forecasting volatility.
Visser, Marcel P.
core  

Unified Phase‐Field Framework for Antiferroelectric, Ferroelectric and Dielectric Phases: Application to HZO Thin Films

Advanced Functional Materials, EarlyView.
HfxZr1−xO2${\rm Hf}_x{\rm Zr}_{1-x}{\rm O}_2$ offers CMOS‐compatible nanoscale ferroelectricity yet suffers from a high Ec${\rm E}_c$ demanding large operating voltages. A unified phase‐field framework spanning AFE/FE/DE phases shows how FE grains soften neighboring AFE grains over λ$\lambda$ ≈$\approx$ 22–37 nm.
P. Pankaj, Sandeep Sugathan, Jiyoung Kim, Si Joon Kim, Pil‐Ryung Cha   +4 more
wiley   +1 more source

The Impact of Jumps and Leverage in Forecasting the Co-Volatility of Oil and Gold Futures

Energies, 2019
This paper investigates the impact of jumps in forecasting co-volatility in the presence of leverage effects for daily crude oil and gold futures. We use a modified version of the jump-robust covariance estimator of Koike (2016), such that the estimated ...
Manabu Asai, Rangan Gupta, Michael McAleer   +2 more
doaj   +1 more source

A model-free approach to do long-term volatility forecasting and its variants. [PDF]

Financ Innov, 2023
Wu K, Karmakar S.
europepmc   +1 more source

Forecasting Realized Volatility with Linear and Nonlinear Univariate Models [PDF]

In this paper we consider a nonlinear model based on neural networks as well as linear models to forecast the daily volatility of the S&P 500 and FTSE 100 futures.
Michael McAleer, Marcelo C. Medeiros
core