ALL THINGS BIG AND SMALL 2.0

End-Of-Day Prices as of 27 Sep 2025. We thank EODHD.com for making the Indices data available to us.  With EODHD.com You can be assured that the data is clean, reliable and of the highest quality.  Sure beats trying to fetch data from Yahoo Finance.   For methodology of this post please refer to our original blog post at  https://ngtiankhean.blogspot.com/2025/09/markov-regime-switching-model-for.html Below are the  Markov Regime Switching Risk-On/Risk-Off readings for SP500, DJI, Nasdaq100, STOXX 600, Nikkei225, CSI300 and HSI.  Major global equity markets have a significant positive correlation with the US equity market, as well as the US economy. The US economy is currently showing signs of being in a stagflation- while economic indicators such as payroll data, manufacturing output indicate a recessionary outlook, gold prices,  the CPI and bond yields indicate an inflationary outlook.  But one thing for sure is that the Fed's lowering of th...

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: ...