Why does volatility clustering occur in markets according to Markov models?

Conditions

• The underlying process follows a Markov chain with distinct states.
• Returns within each state follow the same distribution.

Reasoning, step by step

1. Identify the key insight: clustering does not require fat tails or complex dynamics.
2. Recognize that clustering can arise purely from simple state dependence.
3. Observe that high self-transition probabilities cause the system to stay in its current state.
4. Conclude that this persistence naturally creates runs of low or high volatility.

Example

The video states: 'Clustering does not require fat tails or complex dynamics. It can arise from simple state dependence.'

Common misconceptions

• Assuming that volatility clustering inherently requires heavy-tailed distributions.
• Believing that complex non-linear mechanisms are necessary to explain clustering.

Watch the explanation

Explore next

• How do transition probabilities generate volatility persistence?
• What is the stationary distribution?
• What are hidden states in practical applications?