Results 101 to 110 of about 2,349,483 (333)
Long Seasonal Cycle Modeling: the Case of Realized Volatility [PDF]
Statistika: Statistics and Economy Journal, 2018 Time series with long seasonal periods are very common. Several methods have been proposed for modeling of long seasonal cycles, the most commonly used ones being those based on basis expansion. In this paper, we present and discuss these methods.
Jiří Procházka +3 more
doaj
Journal of Business Economics and Management, 2014 This study examines the information content of implied volatility, using the options of the underlying S&P CNX Nifty index. In this study, implied, historical and realized volatilities are calculated using non-overlapping monthly at-the-money samples ...
Puja Padhi, Imlak Shaikh
doaj +1 more source
The Implied-Realized Volatility Relation with Jumps in Underlying Asset Prices [PDF]
Recent developments allow a nonparametric separation of the continuous sample path component and the jump component of realized volatility. The jump component has very different time series properties than the continuous component, and accounting for ...
Bent Jesper Christensen +1 more
core
Photoswitching Conduction in Framework Materials
Advanced Functional Materials, EarlyView.This mini‐review summarizes recent advances in state‐of‐the‐art proton and electron conduction in framework materials that can be remotely and reversibly switched on and off by light. It discusses the various photoswitching conduction mechanisms and the strategies employed to enhance photoswitched conductivity.
Helmy Pacheco Hernandez +4 more
wiley +1 more source
The Volatility of Realized Volatility [PDF]
Using unobservable conditional variance as measure, latent–variable approaches, such as GARCH and stochastic–volatility models, have traditionally been dominating the empirical finance literature.
Christian Pigorsch +3 more
core
Modeling and Forecasting Realized Volatility [PDF]
This paper provides a general framework for integration of high-frequency intraday data into the measurement forecasting of daily and lower frequency volatility and return distributions. Most procedures for modeling and forecasting financial asset return
Francis X. Diebold +3 more
core +3 more sources
International Journal of Forecasting, 2019 We construct a set of HAR models with three types of infinite Hidden Markov regime-switching structures. In particular, jumps, leverage effects, and speculation effects are all taken into account in the realized volatility modeling.
Jiawen Luo +3 more
semanticscholar +1 more source
Unleashing the Power of Machine Learning in Nanomedicine Formulation Development
Advanced Functional Materials, EarlyView.A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore +7 more
wiley +1 more source
Advanced Functional Materials, EarlyView.The combination of formamidinium thiocyanate and 1,3‐propane diammonium iodide for bulk and top‐surface passivation, and a ternary fullerene blend to improve energy band alignment, suppresses energy losses in wide‐bandgap FAPbBr3 perovskite solar cells.
Laura Bellini +9 more
wiley +1 more source
A Framework for Exploring the Macroeconomic Determinants of Systematic Risk [PDF]
We selectively survey, unify and extend the literature on realized volatility of financial asset returns. Rather than focusing exclusively on characterizing the properties of realized volatility, we progress by examining economically interesting ...
Francis X. Diebold +3 more
core +3 more sources