This study investigates the dynamic relationship between the Russian exchange rate, stock returns, and gas prices over January 2020–December 2023, employing wavelet quantile correlation (WQC) with maximal overlap discrete wavelet transform (MODWT). Six independent structural break tests identify a regime shift within 10 trading days of 24 February 2022, without prior event conditioning. Before sanctions, exchange rate–stock correlations are negative at upper quantiles (τ = 0.75–0.90) across 2–16-day wavelet scales, consistent with portfolio-balance dynamics. After February 2022, the sign reverses (Fisher z = 2.84, p = 0.004, Bonferroni-corrected) as mandatory foreign-currency surrender severs the arbitrage channel. Opposing signs across regimes cancel under full-sample aggregation, resolving the exchange rate exposure puzzle as a measurement artefact. Gas price–stock co-movement at 8–32-day scales also reverses post-sanctions (Fisher z = 2.17, p = 0.030), as pipeline curtailment converts rising TTF prices from revenue signals into export volume loss signals. Independent wavelet quantile regression (WQR) confirms the time–frequency structure. These findings highlight the critical role of geopolitical shocks in fundamentally altering currency–equity linkages.

Alexey Mikhaylov is currently Associate Professor with the Financial Markets and Financial Engineering Department, Financial University under the Government of Russian Federation, Moscow. Author of over 200 scientific publications and conference proceedings indexed in Scopus and Web of Science and author of 8 scientific monographs. As of July 2025, he has the highest Hirsch index among Russian economists: according to Web of Science - 30, according to Scopus - 52, according to Google Scholar - 58. He is in the top 2 % of the most cited scientists (as of August 2024), is among the top cited young scientists according to MDPI (2024). Together with his team, he formed the theory of crypt asset prices and developed the theory of general artificial intelligence. With Y. N. Sotskov, he contributed to the development of the theory of schedules within the framework of the RNF project. He formed a database of renewable energy generation sources and carbon dioxide emissions used for analysis by artificial intelligence and fuzzy logic methods.