Marknadsdivergens som en regimsignal: En probabilistisk MS-GARCH modell för trendföljande exponering

This thesis investigates whether a regime-switching time series model can identify latent market conditions relevant to the performance of Commodity Trading Advisors (CTAs). Specifically, it applies a Markov-Switching GARCH (MS-GARCH) model to the Market Divergence Indicator (MDI), a proprietary index developed by RPM Risk & Portfolio Management, that measures the intensity of market trends across a broad set of futures markets. While MS-GARCH models are typically used to model volatility in return series, this application instead treats volatility as a latent signal to infer underlying regime dynamics in the MDI. The aim is to determine whether such regimes can be robustly extracted and used to identify market regimes to optimize CTA exposure. The model successfully uncovers three economically meaningful regimes that differ in volatility and persistence. In-sample analysis shows that a regime based allocation rule based on these classifications can outperform a passive CTA benchmark in terms of risk-adjusted returns. However, out-of-sample performance is hindered by excessive regime switching and delayed detection, limiting real-time applicability. The identified regimes also exhibit a pattern of increasing correlation between the MDI and CTA index returns, both in-sample and out-of-sample.