Pricing of Merger and Acquisition Announcements During the COVID-19 Period: The Role of Volatility Regimes in Borsa Istanbul
DOI:
https://doi.org/10.63556/tisej.2026.1886Keywords:
Birleşme ve satın alma duyuruları, olay çalışması, kümülatif anormal getiriler, Volatilite rejimleri, Markov Geçişli GARCHAbstract
Market reactions to merger and acquisition announcements may vary depending on both the informational content of the announcement and the market conditions prevailing at the time of disclosure. This study examines whether the price effects of merger and acquisition announcements made in Türkiye during the Covid-19 period differ across volatility regimes. For this purpose, the event study methodology is applied to announcements made by firms listed on Borsa Istanbul, and cumulative abnormal returns are calculated. The regime structure of market volatility is identified using a two-state Markov-switching GARCH model estimated from BIST100 index returns. The resulting regime probabilities are matched with event dates, and announcement effects are compared across low- and high-volatility regimes. The findings indicate that average cumulative abnormal returns around merger and acquisition announcements are generally positive but statistically insignificant. The results from short-term event windows and winsorization analyses also support this finding. Regime-based analyses show that market reactions tend to be more favorable during low-volatility periods, whereas they weaken or turn negative during high-volatility periods. However, the difference between the regimes is not statistically significant. The EGARCH results reveal a positive relationship between the probability of the high-volatility regime and conditional volatility, thereby supporting the economic interpretability of the regime classification. In this respect, the study provides an integrated analytical framework for evaluating merger and acquisition announcements under different market conditions by combining event study methodology with regime-based volatility models.
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