Characteristics of Financial Volatility
Volatility is not constant. It breathes, jumps, and settles down. Understanding its "personality" is key to modeling it.
1. Volatility Clustering
"Volatility comes in bunches." (Mandelbrot).
- If today is volatile, tomorrow is likely to be volatile too.
- If today is calm, tomorrow is likely to be calm.
Implication for Modeling: We cannot use a constant Variance ($\sigma^2$). We need a model where variance changes over time (Heteroskedasticity).
2. Mean Reversion
Volatility tends to revert to a long-term average.
- High Volatility periods don't last forever. The market eventually calms down.
- Low Volatility periods eventually end with a spike.
- VIX Index: The "Fear Gauge" usually trades between 12 and 20. If it hits 80 (like in 2008), you know it will eventually come down.
3. Asymmetry (Leverage Effect)
Volatility reacts differently to good news vs bad news.
- Price Drop (Bad News): Volatility spikes implies fear/panic.
- Price Rise (Good News): Volatility may decrease or stay stable.
4. Co-Movement across Markets
When volatility spikes in the US (S&P 500), it almost immediately spikes in India (Nifty), Japan (Nikkei), and Europe. Volatility is contagious.
Trading Tip: Because volatility is mean-reverting, you can trade it! If VIX is extremely high, you might short volatility (betting things will calm down). If VIX is extremely low, you might buy volatility (hedging against a future storm).
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