A Markovian Analysis to Investigate Inter-Asset Market Linkages for Drawdown Transitions

Authors

https://doi.org/10.22105/tqfb.v3i2.87

Abstract

This research investigates the stochastic transition dynamics of financial volatility utilizing a distinct-state first-order Markov chain model. While volatility clustering and regime persistence have been thoroughly investigated, there still exists a methodological gap in estimating transition behavior under time-homogeneous and random assumptions. To simulate regime shifts, the study utilizes Markovian transition mapping, which employs empirically derived transition matrices, stationary distributions, and absorption probabilities. Chi-square and likelihood-ratio tests have been used for diagnostic validation, ensuring precise stage classification and a sufficient level of constant transition probabilities. A comparative analysis of higher-order, non-homogeneous, and Hidden Markov Model (HMM) extensions reveals accurate regime-dependent and covariate-sensitive structures representing time-varying dynamics. At the same time, first-order chains adequately capture short-term persistence. The results show that predicted recovery time, first-passage probability, and sojourn lengths are all important for understanding how long assets will last and how quickly they will recover. The present research presents a robust probability framework that improves volatility forecasting, strategic planning, and portfolio restructuring, while developing a methodological basis for transforming traditional Markov processes into more adaptive stochastic models of financial risk.

Keywords:

Stock market, Volatility, Markovian transition mapping

References

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2026-05-29

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Vol. 3 No. 2 (2026): Transactions on Quantitative Finance and Beyond

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Bhattacharjee, S., & Biswas, S. (2026). A Markovian Analysis to Investigate Inter-Asset Market Linkages for Drawdown Transitions. Transactions on Quantitative Finance and Beyond, 3(2), 128-145. https://doi.org/10.22105/tqfb.v3i2.87
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