CBOE China ETF Volatility Index

CBOE Emerging Market Markets Volatility Index

How volatility evolves over time

How illiquidity evolves over time

{name} Model Equation

How returns affect tomorrow's volatility

How returns affect tomorrow's illiquidity

Spline Coefficients

Value at Risk (VaR) estimates the maximum loss at a given confidence level. Expected Shortfall (ES) estimates the average loss beyond VaR.

Help Improve V-Lab

Customize your privacy preferences by category. You can change these at any time from your privacy settings.

Privacy Preferences

Control how V-Lab uses cookies and similar technologies. Changes apply immediately.

Manage Privacy Preferences

We could not find the page you were looking for.

Loading title

V-Lab does not knowingly collect personal information from children under 13 years old. If you believe we have collected such information, please contact us immediately.

Children's Privacy

Contact Us

Stored locally:

Cookies and Similar Technologies

Data Collected (Anonymous):

Legitimate interest (GDPR Article 6(1)(f)) - necessary for website improvement and operational monitoring.

Legal Basis:

Data Retention:

You can object to this processing by enabling Do Not Track (DNT) in your browser or using an ad blocker.

Your Rights:

1. Basic Analytics (No Consent Required)

4. Chat Support (Consent Required)

You can withdraw consent at any time by visiting our {link} page.

2. Enhanced Analytics (Consent Required)

3. Error Monitoring (Consent Required)

Information We Collect

Data Security

Right to Access:

Right to Erasure:

Object to processing based on legitimate interests

Right to Object:

Right to Data Portability:

Right to Rectification:

Right to Restrict Processing:

Your Rights Under GDPR

Withdraw consent at any time via our {link}

Right to Withdraw Consent:

Introduction

You can review and change your consent choices at any time.

Manage Your Privacy Preferences

V-Lab Privacy Policy - Learn how NYU Stern's Volatility Laboratory collects, uses, and protects your data. GDPR-compliant with transparent data practices.

V-Lab: Privacy Policy

We may update this privacy policy from time to time. Significant changes will be communicated via a notice on our website. Continued use of V-Lab after changes constitutes acceptance of the updated policy.

Param	Value	t-stat
ωconst	0.9472	5.14***
αARCH	0.0679	5.59***
βGARCH	0.9180	68.09***

γ₁	-0.0442	-3.42***
γ₂	0.0688	3.50***
γ₃	-0.0304	-2.66***

Parameter	Value	t-statistic
ω const Unconditional variance weight	0.9472	5.14***
α ARCH Response to squared shocks	0.0679	5.59***
β GARCH Volatility persistence	0.9180	68.09***

γ₁	-0.0442	-3.42***
γ₂	0.0688	3.50***
γ₃	-0.0304	-2.66***

V-Lab

Shanghai Shenzhen CSI 300 Index Zero Slope Spline-GARCH Volatility Analysis

Date Range:

News Impact Curve

Volatility Forecast

Parameter Estimates

Other Shanghai Shenzhen CSI 300 Index Analyses