Strategy #0000000136 · Machine learning
Markov regime-switching scenarios
Enter prior up-regime probability, transition probabilities, observation likelihoods, threshold and state-return assumptions.
Markov-switching models treat returns or volatility as arising from latent regimes with different statistical properties, changing according to transition probabilities. Estimation requires choices about state count, distributions and parameters; short samples, parameter drift and label switching can destabilise results. This teaching calculator performs a one-step probability update only and does not estimate a model.
No history is used to estimate parameters, regime count, likelihoods or forecast performance.
Three ways in
Understand the story. Then inspect the risk.
Start with a familiar situation, check the payoff, then test how the numbers change.
The idea · in two minutes
How do transition and observation probabilities update regime odds, and do they cross a position threshold?
Markov-switching models treat returns or volatility as arising from latent regimes with different statistical properties, changing according to transition probabilities. Estimation requires choices about state count, distributions and parameters; short samples, parameter drift and label switching can destabilise results. This teaching calculator performs a one-step probability update only and does not estimate a model.
Enter prior up-regime probability, transition probabilities, observation likelihoods, threshold and state-return assumptions.
No history is used to estimate parameters, regime count, likelihoods or forecast performance.
- Input assumptionsEnter prior up-regime probability, transition probabilities, observation likelihoods, threshold and state-return assumptions.
- Model outputNo history is used to estimate parameters, regime count, likelihoods or forecast performance.
Illustrative research examples only. They are not live signals or a recommendation.
The mechanics · payoff and exposure
p_t^- = p_{t-1}p_{UU}+(1-p_{t-1})p_{DU},\quad p_t=\frac{p_t^-L_U}{p_t^-L_U+(1-p_t^-)L_D}
Transition probabilities, observation likelihoods, state returns and exposure threshold are entered manually.
Greeks: the directional intuition
- 模型
- Measures sensitivity to implied volatility.
- 合約
- Measures sensitivity to implied volatility.
- 執行
- Measures sensitivity to implied volatility.
p_t^- = p_{t-1}p_{UU}+(1-p_{t-1})p_{DU},\quad p_t=\frac{p_t^-L_U}{p_t^-L_U+(1-p_t^-)L_D}
Practical context · published observations
Markov regime-switching scenarios: read the record with its caveats.
The chart uses the published figures or explicitly labelled study inputs below. A historical index or model replay is not the same as an investor’s realised account return.
No history is used to estimate parameters, regime count, likelihoods or forecast performance. Source ↗ · Methodology ↗
Change assumptions and inspect the scenario
Inputs illustrate the strategy mechanics; they are not live quotes, forecasts or investment advice.
Variants · same family, different risk levers
Models, contract terms and implementation can change results.
Compare relevant mechanisms and execution assumptions.
| Related strategy | Relationship | What changes | New cost or risk |
|---|---|---|---|
| Base scenario | Manual inputs | Markov-switching models treat returns or volatility as arising from latent regimes with different statistical properties, changing according to transition probabilities. Estimation requires choices about state count, distributions and parameters; short samples, parameter drift and label switching can destabilise results. This teaching calculator performs a one-step probability update only and does not estimate a model. | No history is used to estimate parameters, regime count, likelihoods or forecast performance. |
| Conservative version | Higher cost/loss | Stress higher costs, larger losses or lower liquidity. | May lower scenario returns. |
| Validation version | Data review | Test model inputs with consistent contracts and timestamps. | Needs licensed, representative data. |
No history is used to estimate parameters, regime count, likelihoods or forecast performance.
Read beyond the summary
References
Primary sources and research used to describe the strategy and its limits.