Econometric modelling on tourist arrivals has concentrated mostly on forecasting. We abstract and assess the impact on arrivals to Korea from China, Hong Kong, Indonesia, Japan, Taiwan, Singapore and Malaysia using a number of macroeconomics variables (unemployment, inflation, GDP and exchange rate) sentiment, mood and most importantly ‘Hallyu’ (Korean cultural wave) which is quantified for the very first time. Effectively, we test how each variable ‘measures up’ against all other variables. Our analysis is twofold. Firstly, we use impulse response functions to assess the risk, magnitude and duration that each shock has on short-term arrivals from each country separately. Secondly, we concentrate on systemic effects and use causality tests to examine the effect of each variable on arrivals for all countries simultaneously. We find that the effect of our variables is not uniform but most importantly that ‘Hallyu’ cannot be ignored.

Frontier markets have become increasingly investible, providing diversification opportunities; however, there is very little research (with conflicting results) on the relationship between Foreign Exchange (FX) and frontier stock markets. Understanding this relationship is important for both international investor and policymakers. The Markov-switching Vector Auto Regressive (VAR) model is used to examine the relationship between FX and frontier stock markets. There are two distinct regimes in both the frontier stock market and the FX market: a low-volatility and a high-volatility regime. In contrast with emerging markets characterised by “high volatility/low return”, frontier stock markets provide high (positive) returns in the high-volatility regime. The high-volatility regime is less persistent than the low-volatility regime, contrary to conventional wisdom. The Markov Switching VAR model indicates that the relationship between the FX market and the stock market is regime-dependent. Changes in the stock market have a significant impact on the FX market during both normal (calm) and crisis (turbulent) periods. However, the reverse effect is weak or nonexistent. The stock-oriented model is the prevalent model for Sub-Saharan African (SSA) countries. Irrespective of the regime, there is no relationship between the stock market and the FX market in Cote d’Ivoire. Our results are robust in model selection and degree of comovement.

The Theory demonstrates that oil price and oil volatility (OVX) are significant determinants of economic activity; however, studies seldom consider both variables in the oil–exchange rate nexus and ignore the distributional heterogeneity of the exchange rate. We investigate their joint effect and employ both the quantile regression and Markov switching models to address this. We differentiate between positive/negative shocks and control for the effect of the global financial crisis in 2008 and the COVID-19 pandemic in 2020. We observe that OVX shocks significantly impact the exchange rate for all countries whereas, oil price shocks only affect the exchange rate of oil importing countries. Rising (falling) OVX causes the local currency to depreciate (appreciate). The impact of rising or falling OVX is the same for oil importing and oil exporting countries whereas the impact of rising and falling oil price varies. The impact of oil price and OVX on exchange rate is affected by market conditions. The exchange rate responds to oil price and OVX mostly at lower quantiles (bearish markets) for all countries, which reveals investors sensitivity. In contrast, a weak to no significant response is observed at the higher quantiles (bullish market). Our results are robust in model selection (Markov switching models).

We look into determinants (volatility, crises, sentiment and the U.S. ‘fear’ index) of herding using BRICS as our sample. Investors herd selectively to crises and herding is a short-lived phenomenon. Herding was highest during the global financial crisis (only China was affected). There was no herding during the European debt crisis and COVID. With regard to the relationship between volatility and CSAD (cross sectional absolute deviation)/herding, a lower CSAD (movement in a specific direction) brings about less volatility. However, a high volatility amplifies herding (reduces CSAD), especially in China. Russia and South Africa are unresponsive to volatility levels (low/high) and herding. We also observe volatility heterogeneity. Different volatility measures have different effects on different markets. There is limited evidence to suggest that sentiment (based on principal component) Granger causes herding/CSAD. Herding is a period and market variant and unrelated to crises. The U.S. ‘fear’ index has a short-lived, limited effect on CSAD/herding (during COVID only) for all countries except China. In addition, Granger causality analysis indicates a two-way relationship between the U.S. ‘fear’ index and CSAD/herding, unrelated to crises.

The recent financial crisis has made (il)liquidity research more significant than ever. Galariotis and Giouvris (Int Rev Financ Anal 38:44–69, 2015) find evidence that market liquidity may contain information for predicting the state of the economy. Similar to (il)liquidity, oil is an important indicator of the future state of the economy (GDP). We consider five predictive variables, namely national/global illiquidity, foreign exchange, Baltic Dry, and oil. Our findings show that (1) global illiquidity provides greater overall explanatory power compared to national illiquidity (even for developed oil exporters: Norway, Canada, and Denmark). (2) Oil is the most important predictive variable for oil exporters (especially for emerging oil exporters suggesting over-reliance), while Baltic Dry appears to be more important for oil importers. (3) FX has extra power over financial variables mainly for emerging oil exporters. Finally, there is a two-way causality between GDP and our predictive variables: (4) For oil exporters, the two-way causality between oil and GDP remains, while for net oil importers, we observe a one-way causality from GDP to oil.