A Data‐Driven Inverse Design Methodology for Magnetic Soft Millirobots Navigating in Confined Spaces
Advanced Science, EarlyView.A data‐efficient inverse design framework automates the optimization of magnetic soft millirobots for confined‐space navigation. Integrating a physics‐based Cosserat rod model with Bayesian optimization efficiently identifies high‐performance geometries.
Ziyu Ren +5 more
wiley +1 more source
Periodic Asymmetric LogGARCH Stochastic Volatility Models: Structure and Application
AxiomsThis paper introduces a new class of periodic volatility models, namely, the Stochastic Volatility Periodic Logarithmic Asymmetric GARCH (PlogAG-SV) model.
Omar Alzeley, Ahmed Ghezal
doaj +1 more source
Asymptotic Analysis for One-Name Credit Derivatives
Abstract and Applied Analysis, 2013 We propose approximate solutions to price defaultable zero-coupon bonds as well as the corresponding credit default swaps and bond options. We consider the intensity-based approach of a two-correlated-factor Hull-White model with stochastic volatility of
Yong-Ki Ma, Beom Jin Kim
doaj +1 more source
Bayesian Markov Chain Monte Carlo for reparameterized Stochastic volatility models using Asian FX rates during Covid-19. [PDF]
J Appl Stat, 2023 Poonvoralak W.
europepmc +1 more source
Femtosecond‐Laser‐Induced Physical Unclonable Random Maze Structure for Storage‐Free Encryption
Advanced Science, EarlyView.Femtosecond‐laser‐induced gold random maze structures serve as multimodal physical unclonable functions for storage‐free encryption. Their stochastic optical, electrical, and Raman responses are generated by plasmon‐assisted Marangoni formation and converted into AES‐compatible keys without permanent secret‐key storage, offering a portable route toward
Shiru Jiang +6 more
wiley +1 more source
A Locally Both Leptokurtic and Fat-Tailed Distribution with Application in a Bayesian Stochastic Volatility Model. [PDF]
Entropy (Basel), 2021 Lenart Ł, Pajor A, Kwiatkowski Ł.
europepmc +1 more source
"Leverage, heavy-tails and correlated jumps in stochastic volatility models" [PDF]
This paper proposes the efficient and fast Markov chain Monte Carlo estimation methods for the stochastic volatility model with leverage effects, heavy-tailed errors and jump components, and for the stochastic volatility model with correlated jumps.
Jouchi Nakajima, Yasuhiro Omori
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BVARs and stochastic volatility
Bayesian vector autoregressions (BVARs) are the workhorse in macroeconomic forecasting. Research in the last decade has established the importance of allowing time-varying volatility to capture both secular and cyclical variations in macroeconomic uncertainty.
openaire +2 more sources
RRAM Variability Harvesting for CIM‐Integrated TRNG
Advanced Electronic Materials, EarlyView.This work demonstrates a compute‐in‐memory‐compatible true random number generator that harvests intrinsic cycle‐to‐cycle variability from a 1T1R RRAM array. Parallel entropy extraction enables high‐throughput bit generation without dedicated circuits. This approach achieves NIST‐compliant randomness and low per‐bit energy, offering a scalable hardware
Ankit Bende +4 more
wiley +1 more source
A two factor long memory stochastic volatility model [PDF]
, 2006 In this paper we fit the main features of financial returns by means of a two factor long memory stochastic volatility model (2FLMSV). Volatility, which is not observable, is explained by both a short-run and a long-run factor. The first factor follows a
Veiga, Helena
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