Results 41 to 50 of about 254,826 (323)
Can we use volatility to diagnose financial bubbles? lessons from 40 historical bubbles
Quantitative Finance and Economics, 2018 We inspect the price volatility before, during, and after financial asset bubbles in orderto uncover possible commonalities and check empirically whether volatility might be used as anindicator or an early warning signal of an unsustainable price ...
Didier Sornette +2 more
doaj +1 more source
Multiple time scales and the exponential Ornstein-Uhlenbeck stochastic volatility model [PDF]
Quantitative Finance 6, 423-433 (2006), 2005 We study the exponential Ornstein-Uhlenbeck stochastic volatility model and observe that the model shows a multiscale behavior in the volatility autocorrelation. It also exhibits a leverage correlation and a probability profile for the stationary volatility which are consistent with market observations. All these features make the model quite appealing
arxiv +1 more source
Advanced Engineering Materials, EarlyView.This perspective article explores an innovative powder metallurgical approach to producing high‐nitrogen steels by utilizing a mixture of stainless steel and Si3N4. This mixture undergoes hot isostatic pressing followed by direct quenching. The article also examines adapting this method to laser powder bed fusion (PBF‐LB/M) to overcome nitrogen ...
Louis Becker +5 more
wiley +1 more source
Chaos Theory and Applications, 2023 A time series data contains a large amount of information in itself. Chaos data and volatility data which calculated by any time series are also derivative information included in the same time series. According to these assumptions, it is very important
Hüseyin Serdar Yalçınkaya +1 more
doaj +1 more source
Asymptotic expansion for some local volatility models arising in finance [PDF]
Decisions in Economics and Finance, 2019 In this paper we study the small noise asymptotic expansions for certain classes of local volatility models arising in finance. We provide explicit expressions for the involved coefficients as well as accurate estimates on the remainders. Moreover, we perform a detailed numerical analysis, with accuracy comparisons, of the obtained results by mean of ...
Albeverio S. +3 more
openaire +5 more sources
Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials
Advanced Engineering Materials, EarlyView.This article explores how machine learning (ML) revolutionizes the study and design of disordered materials by uncovering hidden patterns, predicting properties, and optimizing multiscale structures. It highlights key advancements, including generative models, graph neural networks, and hybrid ML‐physics methods, addressing challenges like data ...
Hamidreza Yazdani Sarvestani +4 more
wiley +1 more source
Forward implied volatility expansion in time-dependent local volatility models******
ESAIM: Proceedings and Surveys, 2014 We introduce an analytical approximation to efficiently price forward start options on equity in time-dependent local volatility models as the forward start date, the maturity or the volatility coefficient are small.
Bompis Romain, Hok Julien
doaj +1 more source
Combined Mutiplicative-Heston Model for Stochastic Volatility [PDF]
Physica A 561, 1 January 2021, 125263, 2018 We consider a model of stochastic volatility which combines features of the multiplicative model for large volatilities and of the Heston model for small volatilities. The steady-state distribution in this model is a Beta Prime and is characterized by the power-law behavior at both large and small volatilities.
arxiv +1 more source
Advanced Engineering Materials, EarlyView.This study explores aerosol jet‐printed (AJP) surface roughness, its effects on the performance of microwave electronics, and its process contributors. First, an electromagnetic model is vetted for AJP's unique roughness signature. Simulations are built which show process‐induced roughness is as significant as conductor resistivity in driving microwave
Christopher Areias, Alkim Akyurtlu
wiley +1 more source
Volatility in the Italian Stock Market: an Empirical Study
, 1999 We study the volatility of the MIB30-stock-index high-frequency data from November 28, 1994 through September 15, 1995. Our aim is to empirically characterize the volatility random walk in the framework of continuous-time finance. To this end, we compute
Ball +11 more
core +3 more sources