Volatility Spillover of Intraday Exchange Rates on Some Selected Asean Countries

Abstract

In this paper, we use hourly exchange rate data for selected ASEAN countries (Singapore, Indonesia, Malaysia, Thailand and the Philippines) to test the hypothesis that exchange rate own shocks dominate exchange rate volatility. We find strong evidence that own exchange rate volatility explains between 64% to 86% of their own exchange rate volatility movements. These results do not change when we include the Chinese CNY currency in the analysis. Moreover, we find that exchange rate shocks of ASEAN countries explain 36%, 24% and 23% of exchange rate volatility movements of Indonesia, Thailand, and Singapore, suggesting that for these countries are more synchronized.

Time and Frequency Dependency of Foreign Exchange Rates and Country Risk: Evidence From Turkey

“…For example, Onanuga and Shittu (2010), Maitra (2017), Obeng and Sakyi (2017), and Kartal (2020) examine the FX rates from the interest rates perspectives, while Hajilee and Al Nasser (2014), Korhonen (2015), Jebran and Iqbal (2016), Demir (2019), Kartal et al (2020), Narayan et al (2020), Depren et al (2021), and Kartal et al (2021a) examine the FX rates from stock market index perspectives, and Sujit and Kumar (2011), Wang and Chueh (2013), and Dinçer et al (2018) examines the FX rates from the gold prices. Moreover, the FX rates are examined in terms of the nexus with Credit Default Swap (CDS) spreads (Fontana and Scheicher, 2016;Hassan et al, 2017), economic policy uncertainty (Krol, 2014;Beckmann and Czudaj, 2017;Sharif et al, 2020), geopolitical risk (Iyke et al, 2022), foreign portfolio inflows (Kartal et al, 2020), monetary policy indicators like emission (Depren et al, 2021), oil prices (Kartal, 2021); sovereign credit risk (Augustin et al, 2020), and volatility (Depren et al, 2021;Devpura et al, 2021;Kartal et al, 2021a).…”

Has COVID-19 intensified the oil price–exchange rate nexus?

“…Sudden shocks in the economy, emanated from the factors like wars, terrorist attacks, political events and financial crisis seem to play a crucial role in affecting the predictability and thus changing the intensity of the spillover effect. Empirically, it has been obtained that the linkages between the return series are more profound and significant during the crisis periods than in normal situations ( Brayek et al, 2015 , Devpura et al, 2021 , Reboredo, 2012 , Turhan et al, 2014 , Yin and Ma, 2018 ). It is worth mentioning that previous studies only deal with the stability of the ‘return-spillover’ between the oil and exchange rate markets.…”

Spillover effects of crash and jump events: evidence from Chinese market

“…They found that the average cumulative abnormal returns (CARs) of rival firms in the same industry exhibit a positive and significant relationship with the CARs of announcing firms in the same country and industry. During periods of high past market volatility, earnings announcements demonstrate magnified information spillover effects on peer firms (Devpura et al ., 2021). Also, their results suggest that announcing firms with larger market capitalization generate greater intra-industry information transfer.…”