Knowing the market’s current risk state is foundational for portfolio decisions. Basic Markov chain models are a good starting point, but they fall short on three realities of financial data. Using as case study China's CSI300 Index, here’s how our model resolves each one and delivers a robust, reproducible Risk‑On/Risk‑Off signal. 1) Beyond “memoryless”: markets have persistence and aging Problem: Standard Markov chains assume memorylessness—the next state depends only on the current state, not on how long we’ve been there. Markets don’t behave that way; regimes “age” and persistence changes with time. Our solution: We use a hidden semi‑Markov model (HSMM) with explicit duration modeling. Each regime has a duration distribution (e.g., Negative Binomial), and transitions are allowed to depend on time spent in the state. This breaks the memoryless assumption, curbs flicker, and captures realistic regime persistence. 2) Heavy‑tailed, skewed returns are the norm—not Gaussian Problem: ...
These ten silhouettes of famous personalities were found on a wall outside a stationery store in Melbourne, Florida. I think most people can identify at least three of them. See if you can identify all ten.
Comments
Post a Comment