Understanding the Interface

The volatility analysis page is organized around several key components that work together to provide comprehensive volatility insights:

Chart Controls and Navigation

• Model Selection

  Choose from multiple GARCH-family models (GARCH, GJR-GARCH, EGARCH, etc.) using the dropdown menu to compare different modeling approaches

• Adding Series

  Add additional volatility series for comparison by entering ticker symbols in the "Other Analyses" section with autocomplete suggestions

• Time Period Controls

  Adjust time periods using preset buttons (6M, 1Y, 2Y, 5Y, 10Y, All) or use date range selectors for custom periods

• Chart Positioning

  Position the legend either inside or outside the chart using Key Position settings and apply window options to focus on recent observations

Volatility Prediction Display

The prominent prediction display at the top of the page shows the key forecast:

• Vol Prediction

  One-step ahead volatility prediction for the next trading day, including the prediction date and absolute change from the previous day's forecast

Summary Table Column Definitions

The volatility summary table below the chart displays key metrics for the selected asset:

• Price

  Last recorded daily closing price for the asset

• Return

  Last recorded daily return (percentage change from previous close)

• Avg Week Vol

  Average annualized volatility over the last week (5 trading days)

• Avg Month Vol

  Average annualized volatility over the last month (22 trading days)

• Min Vol

  Minimum volatility observed over the full estimation period

• Max Vol

  Maximum volatility observed over the full estimation period

• Avg Vol

  Average volatility over the full estimation period

• Vol of Vol

  Standard deviation of volatility over the estimation period, measuring how much volatility itself fluctuates

• 1 Week Pred

  Predicted average volatility over the next 5 trading days

• 1 Month Pred

  Predicted average volatility over the next 22 trading days

• 6 Month Pred

  Predicted average volatility over the next 126 trading days

• 1 Year Pred

  Predicted average volatility over the next 252 trading days

Plot Options

The volatility chart provides several customization options:

• Date Range

  Select a custom date range using the pop-up calendar controls. The default view shows a two-year period.

• Window

  Focus on the most recent observations within your selected date range for detailed short-term analysis

• Compare

  Overlay additional series including VIX or other asset volatilities. Enter ticker symbols in the 'Other Analyses' text box with autocomplete support. Click 'Add' or press Enter to include, use the 'X' button to remove individual series, or 'Clear All' to remove all comparison series.

• Sub Plot

  Add supplementary information below the main chart: choose daily returns, daily prices, both, or no sub plot

• Line Style

  Adjust line style for the volatility series. 'Lines Points' helps identify specific data points in shorter time periods, while plain lines work better for larger datasets.

• Key Position

  Position the chart legend either outside the graph or inside at any of the four corners. Note: placing the key outside the graph will remove any sub plots.

Statistical Output Interpretation

The volatility analysis page provides comprehensive statistical information to help you understand model performance and forecast reliability:

• Volatility Prediction Summary

  Next-day volatility forecasts with predicted changes from previous levels. Increases shown in red, decreases in green

• Model Parameter Table

  Estimated GARCH parameters with t-statistics to assess model fit and significance, revealing volatility dynamics

• Summary Statistics

  Key metrics including average weekly/monthly volatility, min/max volatility, volatility of volatility, and multi-horizon forecasts

Reading Volatility Patterns

Volatility analysis reveals crucial risk characteristics that inform investment decisions:

• Volatility Clustering

  GARCH models capture tendency for high volatility periods to cluster together - expect persistence in volatility regimes

• Asymmetric Volatility Response

  GJR-GARCH models capture the fact that negative price shocks typically increase future volatility more than positive shocks

• Mean Reversion

  In GARCH models, volatility clusters but still mean-reverts to a long-run average when α + β < 1, with β reflecting persistence from past variance and α from recent shocks.

Forecast Interpretation Framework

Understanding different forecast horizons helps you apply volatility predictions effectively:

• Short-term Forecasts (1-day, 1-week)

  Most reliable for immediate risk management decisions, position sizing, and options trading strategies

• Medium-term Forecasts (1-month, 6-month)

  Useful for portfolio rebalancing decisions and strategic asset allocation adjustments based on expected volatility regimes

• Long-term Forecasts (1-year)

  Converge toward unconditional volatility levels, providing baseline expectations for long-term portfolio planning

Practical Applications

• Risk Management

  Dynamic position sizing - reduce exposure when forecasts indicate elevated volatility, increase when volatility is expected to be subdued

• Portfolio Optimization

  Incorporate volatility forecasts into mean-variance optimization, adjusting expected returns based on predicted volatility regimes

• Options Strategy

  Compare model-implied volatility with market-implied volatility to identify potential mispricings and time volatility strategies

GARCH Model Framework

V-Lab employs sophisticated econometric models from the GARCH family that capture three key empirical facts about financial returns: volatility clustering, excess kurtosis (fat tails), and asymmetric volatility responses to positive vs. negative shocks.

Available Model Types

• GARCH

  Standard model capturing volatility clustering and persistence

• GJR-GARCH

  Asymmetric model allowing different responses to positive/negative shocks - preferred for equity analysis

• EGARCH

  Exponential model ensuring volatility remains positive

• Advanced Variants

  AGARCH, APARCH, GAS-GARCH-T, MEM, MF2-GARCH, SGARCH for specialized applications

Data Updates and Methodology

Models are re-estimated daily using rolling windows of recent data, ensuring parameters reflect current market dynamics. Volatility is calculated as annualized standard deviation of returns, assuming 252 trading days per year.

Common Questions

Why do GARCH forecasts seem to lag major market moves?

GARCH models are designed to capture typical volatility patterns, not predict sudden structural breaks. They excel at forecasting volatility persistence after shocks occur, but cannot anticipate completely unexpected events.

How should I choose between different GARCH models?

Start with GJR-GARCH as it captures asymmetric volatility effects common in financial markets. Compare models using information criteria (AIC/BIC) shown in parameter tables, considering your specific application.

What causes parameter instability I sometimes observe?

Parameter estimates can change significantly during structural breaks or regime changes. This indicates changing volatility dynamics - valuable information, though it may reduce short-term forecast reliability.

Why don't volatility forecasts capture extreme events well?

GARCH models are designed for normal volatility dynamics, not crisis prediction. They provide excellent forecasts during typical conditions but cannot anticipate black swan events or structural breaks.

Data Limitations

Model assumptions include stable relationships between past and future volatility. During unprecedented conditions, these relationships may temporarily break down. Forecast accuracy decreases with horizon - one-day forecasts are most reliable.

User Questions from V-Lab Community

Based on technical questions from V-Lab users:

Why do my GARCH model parameters change significantly over time?

Parameter instability often indicates structural breaks in volatility dynamics. This is valuable information showing changing market conditions, though it may reduce short-term forecast reliability. Consider using shorter estimation windows during volatile periods.

How do I choose optimal lag lengths for my GARCH specification?

Use information criteria (AIC, BIC) to select lag lengths, but also check residual diagnostics for remaining autocorrelation. Start with GARCH(1,1) as it captures most volatility clustering, then test higher orders only if needed.

Why does my volatility model show poor performance during crisis periods?

Standard GARCH models assume stable volatility processes and may struggle with structural breaks. During crises, consider regime-switching models or allow for time-varying parameters. Model failure often signals important regime changes.

What causes negative volatility forecasts in some model specifications?

Negative forecasts indicate model misspecification or numerical estimation issues. Check parameter constraints, ensure positive definiteness conditions are met, and consider alternative specifications like EGARCH that guarantee positive volatility.

How can I download volatility data from this analysis?

Users can download volatility data for individual analyses directly from this analysis page after registering for a V-Lab account. Simply select the 'Download Data' menu option from the 'DOWNLOAD' menu after logging in.

Analysis Workflow

• Start with Model Selection

  Begin with GJR-GARCH for most assets as it captures asymmetric volatility effects

• Examine Recent Patterns

  Focus on recent volatility evolution to understand current regime

• Compare Forecasts

  Use multiple forecast horizons to distinguish short-term volatility from long-term expectations

• Validate with Parameters

  Review parameter estimates and t-statistics to assess model reliability

• Context with History

  Place current volatility and forecasts in historical context using full time series

Best Practices for Decision Making

• Combine with Fundamental Analysis

  Use volatility analysis as part of comprehensive risk management rather than standalone tool

• Regular Model Comparison

  Periodically compare different GARCH specifications as market conditions evolve

• Forecast Validation

  Track forecast accuracy over time to build confidence and identify performance deterioration

• Risk Budget Adjustment

  Use forecasts to dynamically adjust risk budgets - scale positions based on expected volatility

• Regime Awareness

  Pay attention to volatility regime changes indicated by parameter shifts or forecast pattern changes