Regime Discovery and Intra-Regime Return Dynamics in Global Equity Markets
🤖 AI Summary
This study addresses the pronounced heterogeneity in return dynamics across global equity markets under varying volatility regimes, necessitating data-driven approaches to identify states and characterize their evolution. The authors propose a novel framework that employs amplitude-modulated energy (AME) from holistic Hilbert spectral analysis as a volatility intensity metric, leveraging the Hilbert–Huang transform to precisely segment market states. Within each state, discretized returns are modeled via variable-length Markov chains to capture multi-day transition structures. The findings reveal that developed markets exhibit lower volatility and more complete recoveries during normal states, whereas emerging markets display persistently higher volatility and downside persistence even in normal conditions. Notably, entropy peaks during high-volatility periods, indicating maximal unpredictability at intermediate volatility levels. This approach effectively uncovers structural differences between developed and emerging markets in terms of volatility intensity and tail dependence.
Application Category
📝 Abstract
Financial markets alternate between tranquil periods and episodes of stress, and return dynamics can change substantially across these regimes. We study regime-dependent dynamics in developed and developing equity indices using a data-driven Hilbert--Huang-based regime identification and profiling pipeline, followed by variable-length Markov modeling of categorized returns. Market regimes are identified using an Empirical Mode Decomposition-based Hilbert--Huang Transform, where instantaneous energy from the Hilbert spectrum separates Normal, High, and Extreme regimes. We then profile each regime using Holo--Hilbert Spectral Analysis, which jointly resolves carrier frequencies, amplitude-modulation frequencies, and amplitude-modulation energy (AME). AME, interpreted as volatility intensity, declines monotonically from Extreme to High to Normal regimes. This decline is markedly sharper in developed markets, while developing markets retain higher baseline volatility intensity even in Normal regimes. Building on these regime-specific volatility signatures, we discretize daily returns into five quintile states $\mathtt{R}_1$ to $\mathtt{R}_5$ and estimate Variable-Length Markov Chains via context trees within each regime. Unconditional state probabilities show tail states dominate in Extreme regimes and recede as regimes stabilize, alongside persistent downside asymmetry. Entropy peaks in High regimes, indicating maximum unpredictability during moderate-volatility periods. Conditional transition dynamics, evaluated over contexts of length up to three days from the context-tree estimates, indicate that developed markets normalize more effectively as stress subsides, whereas developing markets retain residual tail dependence and downside persistence even in Normal regimes, consistent with a coexistence of continuation and burst-like shifts.
Problem
Research questions and friction points this paper is trying to address.
regime discovery
return dynamics
equity markets
volatility intensity
market regimes
Innovation
Methods, ideas, or system contributions that make the work stand out.
Hilbert–Huang Transform
Holo–Hilbert Spectral Analysis
Variable-Length Markov Chains
Regime Identification
Amplitude-Modulation Energy
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