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Yayın Trading volume and stock market volatility: evidence from emerging stock markets(LLC CPC Business Perspectives, 2009-01-15) Gürsoy, Güner; Yüksel, Aslı; Yüksel, AydınBased on the 'mixture of distribution' hypothesis, this paper investigates the relationship between trading volume and conditional volatility of returns by using 12 emerging stock market indices over the period between January 2000 and August 2006. The results show that when total trading volume is included in the conditional volatility equation as a proxy for information flow, a moderate level of decline in volatility persistence was observed only for two stock markets. In four stock markets the decline in conditional volatility persistence is very small. On the other hand, for the remaining markets, total trading volume is a poor proxy for information flow. The findings are consistent with the findings of prior research, which suggest that volume may be a good proxy for stock-level analysis, but not for market-level analysis. Furthermore, following Wagner and Marsh (2005) and Arago and Nieto (2005) the relationship between unexpected trading volume (surprise trading volume as an alternative proxy for information flow) and conditional volatility is analyzed. The findings illustrate that for most of the markets, the relationship between surprise volume and conditional volatility is statistically significant.Yayın Volatility spillovers and structural breaks across traditional and digital assets: an econometric investigation (2020–2025)(Springer Nature, 2026-04) Özyeşil, Mustafa; Teker, Dilek; Teker, Suat; Tembelo, HavaneThe need to comprehend the linkages of volatility is more pronounced now owing to the rise of different asset classes, both traditional and digital. In this light, this study focuses on examining the volatility spillovers and structural breaks of four selected key financial instruments: S&P 500 Index, NASDAQ Composite Index, Gold Futures, and Bitcoin. Specifically, this research is designed to investigate how volatility is evolving and transmitting in the presence of economic shocks using a high-frequency dataset for the period January 2020 to May 2025. To capture the dynamic dependencies and regime shifts, sophisticated econometric methods such as GARCH models, the Diebold–Yilmaz spillover index, wavelet coherence, and structural break tests were applied. The results exhibit that Bitcoin is characterized by greater conditional volatility relative to traditional assets. In addition, there is strong volatility clustering across all series. Furthermore, strong volatility spillovers, especially from equities to crypto assets, were identified, and several structural breaks align with important macro-financial milestones such as the COVID-19 crisis and inflationary shocks. It’s shown that the interlinkages among financial markets appear to be on the rise, with asset class volatility increasingly transmitted across them freely. The traits exhibited by digital assets such as Bitcoin differ significantly from those of traditional financial instruments, highlighting the need for more sophisticated risk management strategies. This research fills the gap in the literature cross-market volatility with a time-domain, frequency-domain, and structural change approach. These findings are timely for digital finance in relation to portfolio diversification, strategic asset allocation, and instep with new policies on digital assets.
