Browsing by Author "Lee, Chi-Chuan"

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Markov-switching multifractal volatility spillovers among European stock markets during crisis periods
(Applied Economics, 2025) Tiwari, Aviral Kumar; Abakah, Emmanuel Joel Aikins; Dwumfour, Richard Adjei; Lee, Chi-Chuan
This research investigates time-varying volatility spillovers and connectedness among European stock markets during the COVID-19 pandemic and the Russia – Ukraine war, two events that destabilized global markets. With data from 20 European stock markets spanning 17 December 2019, to 17 March 2022, we employ the TVP-VAR model and estimate volatility using the Markov-switching multifractal volatility technique. Findings from log-volatility estimates suggest that markets are highly connected, with price movement driven mainly by spillover effects from other markets in the same region. Most emerging markets are net receivers of volatility, with most of Europe’s major markets being net transmitters of shocks. The COVID-19 pandemic appears to have impacted European stock markets more than the Russia – Ukraine war. Shifting to the results obtained based on MSM volatility estimates, we find that markets strongly correlate for both high and low volatility. In the case of a high volatility regime, we document the dominance of Finland, Denmark, and Iceland over major European markets. In contrast, under a low volatility regime, we note the dominance of major markets, including the UK and France, over emerging markets in Europe. The findings reveal the diversification potential of emerging European stock markets.
Quantile correlation between fintech stocks and crypto-assets
(Applied Economics, 2024-11-11) Abakah, Emmanuel Joel Aikins; Tiwari, Aviral Kumar; Karikari,Nana Kwasi; Agbloyora,Elikplimi Komla; Lee, Chi-Chuan
This research explores the dependence, directional predictability and dynamic co-movement between fintech and cryptocurrency markets from July 2016 to March 2021 using a series of quantile-based coherency techniques. The causality-in-quantiles results show a considerable difference between causality-in-mean and in-variance under different market conditions. For cross-quantilogram analysis, we observe minimal directional predictability between cryptocurrencies and fintech both in the short-run and in the long-run under bearish and bullish market states. From wavelet multiple cross-correlation models, we show that cryptocurrencies maximize multiple correlation compared to fintech across all time scales, denoting that cryptocurrencies are most dependent on fintech for all wavelet scales.