Results 171 to 180 of about 37,989 (306)

Designable van der Waals Crystal for Artificial Neuronal Cell Mimicking

Advanced Materials, EarlyView.
Designable van der Waals crystal has been demonstrated for device‐scale neuronal cell mimicking. The structural similarity between ion‐channel in biological membranes and layered vdW lattices is realized with nano‐crystallization via Ar + H2S plasma sulfurization.
Jinhyoung Lee, Gunhyoung Kim, Dongho Lee, Seowoo Son, Hyunho Seok, Sihoon Son, Hyunbin Choi, Geonwook Kim, Geumji Back, Hyunkyu Kim, Chaerin Park, Junmin Ahn, Seongyun Je, Chaeyoung Im, Junil Cho, Magdalena Grzeszczyk, Seongho Kim, Eunseo Go, Hyunwoo Shim, Donghwan Choi, Muyoung Kim, Hyoeng‐U. Kim, Won‐Jun Jang, Taesung Kim   +23 more
wiley   +1 more source

Stock Volatility and the Crash of '87 [PDF]

This paper analyzes the behavior of stock return volatility using daily data from 1885 through 1987. The October 1987 stock market crash was unusual in many ways relative to prior history.
G. William Schwert
core  

Ultra‐High‐Throughput Discovery of Multifunctional Polyphenolic Coatings on Droplet Microarrays

Advanced Materials, EarlyView.
An ultra‐high‐throughput (UHT) combinatorial strategy enables the miniaturized synthesis and screening of ≈30 000 polyamine‐polyphenolic (PaPp) coatings using droplet microarrays (DMA). This approach reveals hundreds of previously unknown fluorescent, redox‐active, and antibacterial materials, including multifunctional, cell‐compatible surfaces ...
Vania Tanda Widyaya, Joaquín E. Urrutia Gómez, Paul Reuß, Alexander Welle, Rolf A. Gattung, Jana Mayer, Peter Krolla, Markus Reischl, Pascal Friederich, Anna A. Popova, Thomas Schwartz, Pavel A. Levkin   +11 more
wiley   +1 more source

Why do we smile? On the determinants of the implied volatility function. [PDF]

We report simple regressions and Granger causality tests in order to understand the pattern of implied volatilities across exercise prices. We employ all calls and puts transacted between 16:00 and 16:45 on the Spanish IBEX-35 index from January 1994 to ...
Rubio, Gonzalo, Peña Sánchez de Rivera, Juan Ignacio, Serna, Gregorio   +2 more
core  

COVID-19, Lockdowns and Herding Towards a Cryptocurrency Market-Specific Implied Volatility Index

, 2020
Rubbaniy G, Polyzos S, Rizvi SKA.
europepmc   +1 more source

Australian Implied Volatility Index

, 2018
Volatility implied from option prices is widely regarded as the market's estimate of future expected volatility of the underlying asset. We construct an implied volatility index for the S&P/ASX 200 Index, the AVX, which we find contains important information for predicting volatility of the ASX and significantly outperforms other predictors commonly ...
Frijns, Bart, Tallau, Christian, Tourani-Rad, Alireza   +2 more
openaire   +1 more source

Deep Learning Inverse Design of Phase‐Change Reconfigurable Terahertz Metadevices for Multidimensional Secure Communication

Advanced Materials, EarlyView.
A deep learning inverse‐design framework is established to create versatile reconfigurable terahertz metadevices. By synergizing deep learning with phase‐change materials, this approach enables on‐demand customization of multidimensional electromagnetic responses.
Yisheng Dong, Xieyu Chen, Aarthy Nagarajan, Yi Yang, Zhihao Wang, Rui Yu, Chuang Zheng, Chunmei Ouyang, Xueqian Zhang, Tun Cao, Ranjan Singh, Zhen Tian   +11 more
wiley   +1 more source

SHORT-TERM OPTIONS WITH STOCHASTIC VOLATILITY: ESTIMATION AND EMPIRICAL PERFORMANCE [PDF]

This paper examines the stochastic volatility model suggested by Heston (1993). We employ a time-series approach to estimate the model and we discuss the potential effects of time-varying skewness and kurtosis on the performance of the model.
Gabriele Fiorentini, Ángel León, Gonzalo Rubio   +2 more
core  

Dataset on composite efficiency indices for ASEAN foreign exchange markets. [PDF]

Data Brief
Huynh TT, An NTK.
europepmc   +1 more source

Ferroelectric Dynamic‐Field‐Driven Nucleation and Growth Model for Predictive Materials‐To‐Circuit Co‐Design

Advanced Materials, EarlyView.
This study presents a compact dynamic‐field‐driven nucleation and growth (DFNG) model that captures ferroelectric switching behavior under arbitrary voltage waveforms. It enables extraction of time‐dependent domain wall velocity and growth dimensionality, which can then be extended to device‐level modeling.
Yi Liang, Soohyeon Kim, Tony Chiang, Megan K. Lenox, Ian Mercer, John J. Plombon, Jon‐Paul Maria, Jon F. Ihlefeld, Wenhao Sun, Wei Lu, John T. Heron   +10 more
wiley   +1 more source