Results 61 to 70 of about 583,999 (299)

Modelling and Forecasting Noisy Realized Volatility [PDF]

SSRN Electronic Journal, 2009
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.
Manabu Asai, Michael McAleer, Marcelo C. Medeiros   +2 more
openaire   +9 more sources

alessiounisa/Deep-Learning-for-Volatility-Forecasting: Datasets

, 2019
Data for the Deep Learning for Volatility Forecasting research ...
alessiounisa
core   +1 more source

Electrospun Wood‐Derived Biopolymers as Electrodes in Electrochemical Energy Storage Technologies

Advanced Functional Materials, EarlyView.
Electrospinning transforms wood‐derived cellulose and lignin into architecturally defined, binder‐free carbon electrodes with tuneable porosity and functionality. This review shows how fibre design enables decoupled charge and mass transport, enhancing performance across battery systems, while identifying key challenges in spinnability, scalability ...
Michael W. Thielke, Hattie R. Chisnall, Jesus Vazquez Chavez, Ana B. Jorge Sobrido   +3 more
wiley   +1 more source

Coin impact on cross-crypto realized volatility and dynamic cryptocurrency volatility connectedness

Financial Innovation
This study evaluates the predictive accuracy of traditional time series (TS) models versus machine learning (ML) methods in forecasting realized volatility across major cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), and Ripple (XRP ...
Burak Korkusuz, Mehmet Sahiner
doaj   +1 more source

Forecasting the Covolatility of Coffee Arabica and Crude Oil Prices: A Multivariate GARCH Approach with High-Frequency Data

Journal of Probability and Statistics, 2020
Forecasting the covolatility of asset return series is becoming the subject of extensive research among academics, practitioners, and portfolio managers.
Dawit Yeshiwas, Yebelay Berelie
doaj   +1 more source

Implantable Ionic Memristors Based on Natural Polymer Heterojunctions

Advanced Functional Materials, EarlyView.
We report an implantable natural polymer‐based ionic memristor composed of hyaluronic acid, chitosan, and PDMS. The device achieved 98.94% accuracy in MNIST classification while reducing training time by 36.8% compared with a conventional artificial neural network (ANN).
Dong‐yup Lee, Byung Ik Park, Gihyeon Yim, Hyunjae Yoo, Sangjun Ma, Jundong Park, Jeong‐Yun Sun   +6 more
wiley   +1 more source

Multimode Oxide‐Based Optoelectronic Memtransistor for In‐Sensor Vision Processing

Advanced Functional Materials, EarlyView.
A multimode optoelectronic memtransistor (OEMT) is demonstrated for vision explainable artificial intelligence (VXAI) hardware. By integrating optical sensing, electrical masking, and non‐volatile memory, the device enables key operations required for generating saliency information.
Min Gu Lee, Geunyoung Kim, Hwa Young Kim, Do Hoon Kim, Hanchan Song, Huisu Noh, Jungwan Noh, Seungwoo Lee, Sang‐Hee Ko Park, Himchan Cho, Kyung Min Kim   +10 more
wiley   +1 more source

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
Tim Bollerslev, Torben G. Andersen, Francis X. Diebold, Paul Labys   +3 more
core   +2 more sources

Memristive‐Gated RC‐Delay Synaptic Transistors for Time‐Encoded Analog in‐Memory Computing

Advanced Functional Materials, EarlyView.
A Memristive‐Gated Transistor for Time‐Encoded Analog In‐Memory Computing — By exploiting the RC delay of a self‐rectifying interface‐type memristor, nonlinear I–V distortion is structurally bypassed, enabling 3‐bit nonvolatile memory, spike‐timing‐based analog encoding, and hardware‐calibrated reservoir‐computing validation within a unified device ...
Yun‐Seo Shin, Eunjoo Yoo, Ki‐Hoon Son, Hyun‐Sik Kim, Dae‐Hee Han, Jeong Hyeon Son, Seung Hwan Lee, Hong‐Sub Lee   +7 more
wiley   +1 more source

Cryptocurrency price returns volatility modeling and forecasting with GARCH models [PDF]

RAUSP Management Journal
PurposeThe paper aims to identify suitable conditional variance models for the estimation and forecasting of cryptocurrency returns volatility.Design/methodology/approachThe methodology comprises the use of GARCH-family models estimated by maximum ...
Lukas Silva, Leandro Maciel
doaj   +1 more source