Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Types of Skewness01:09

Types of Skewness

11.6K
If the frequency distribution of a data set is more inclined towards smaller or larger values, the distribution is said to be skewed. If data values are skewed to the right, then the distribution is called positively skewed. Conversely, if the plot is skewed to the left, the distribution is called negatively skewed.
For instance, in the middle of a pandemic, the geographical distribution of vaccine coverage may be positively skewed towards populations in the global north countries. However,...
11.6K
Skewness01:06

Skewness

11.0K
The measures of central tendency calculated from a data set may not reveal much about its intrinsic distribution. If a plot is made of the data set’s values, the mean and the median may not only differ, but also the plot may have more values on one side of the central tendencies. Such a data set is said to be skewed towards that side.
The longer the tail of the plot on one side, the more skewed it is. The skewness of a data set’s values suggests that the measures of central tendency...
11.0K
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

73
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
73
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

430
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
430
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.1K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.1K
Microsoft Excel: Finding Central Tendency, Skew, and Kurtosis01:24

Microsoft Excel: Finding Central Tendency, Skew, and Kurtosis

170
Central tendency refers to the central point or typical value of a dataset. It summarizes the data set with a single value that represents the center of its distribution. The three main measures of central tendency are:
Mean: The arithmetic average of all data points. It is calculated by adding all the values together and dividing by the number of values. The mean is sensitive to extreme values (outliers).
Median: The middle value when the data points are arranged in ascending or descending...
170

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Endovascular treatment for the common femoral artery: is there a challenger to open surgery?

The Journal of cardiovascular surgery·2018
Same author

Bayesian meta-analysis for longitudinal data models using multivariate mixture priors.

Biometrics·2003
See all related articles

Related Experiment Video

Updated: Jul 2, 2025

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

12.1K

Stochastic Volatility Models with Skewness Selection.

Igor Martins1, Hedibert Freitas Lopes1

  • 1Insper Institute of Education and Research, Rua Quatá 300, São Paulo 04546-042, Brazil.

Entropy (Basel, Switzerland)
|February 23, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a flexible stochastic volatility model that dynamically adjusts skewness, preventing overparameterization. Results show dynamic skewness explains interest rate cycles but not currency carry crashes, which stem from volatility surges.

Keywords:
skewnesssparsitystochastic volatility

More Related Videos

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K
A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

4.6K

Related Experiment Videos

Last Updated: Jul 2, 2025

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

12.1K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K
A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

4.6K

Area of Science:

  • Quantitative Finance
  • Econometrics
  • Financial Modeling

Background:

  • Traditional stochastic volatility models often struggle to capture market asymmetry.
  • Imposing dynamic skewness can lead to model overparameterization and reduced interpretability.

Purpose of the Study:

  • To develop a novel stochastic volatility model that allows for time-varying skewness without prior imposition.
  • To mitigate overparameterization risks through a data-driven, sparsity-inducing framework.
  • To empirically investigate the role of dynamic skewness in bond yields and currency markets.

Main Methods:

  • Implementation of sparsity-inducing priors within a stochastic volatility framework.
  • Automatic selection of the skewness parameter (dynamic, static, or zero) based on data.
  • Empirical analysis using bond yield and currency return data.

Main Results:

  • Dynamic skewness effectively captures interest rate cycles, influenced by central bank policies.
  • In currency markets, the carry factor exhibits no significant skewness after accounting for stochastic volatility.
  • Evidence suggests currency 'carry crashes' are driven by volatility surges, not dynamic skewness.

Conclusions:

  • The proposed model offers a flexible and parsimonious approach to modeling time-varying skewness in financial data.
  • Dynamic skewness is relevant for understanding interest rate dynamics but not the primary driver of currency carry crashes.
  • Volatility surges, rather than dynamic skewness, are identified as the key factor in currency carry crashes.