This paper investigates multiscale dynamic interconnection between the five agricultural commodities – corn, wheat, soybean, rice and oats, covering more than 18 years period. For research purposes, two complementary methodologies were used – wavelet coherence and phase difference. Low coherence is present at shorter time-horizons, while at longer time-horizons high coherence areas are found, but they are not widespread in all wavelet coherence plots. These results speak in favour of diversification opportunities. Strong coherence in longer time-horizons indicates that common factors are likely to be the main determinants of the agricultural prices in the long-run. On the other hand, rare high coherence areas at lower scales suggest that monetary and financial activities are most likely the causes that have affected the comovements of the grain prices in the short-term horizons. Phase difference discloses a relatively stable pattern between corn-soybean, corn-wheat, rice-oats and oats-soybean in the longer time-horizons. Taking into account investors’ diversification benefits and the leading (lagging) connections in long-run, corn and oats are the most appropriate cereals to be combined in an n-asset portfolio, since these two cereals constantly and very steadily lag soybean, whereas strong coherence between corn and oats does not frequently occur in all wavelet scales.

This paper explores bidirectional linkage between inflation and its uncertainty by observing monthly data of 11 Eastern European countries. The methodological approach comprises two steps. First, inflation uncertainty series have been created by choosing an optimal Generalized Autoregressive Conditional Heteroskedasticity-(GARCH) type model. Subsequently, inflation and inflation uncertainty have been observed together by two models examining whether Friedman's and Cukierman-Meltzer's hypotheses hold for selected Eastern Europe Countries (EEC). Due to the heterogeneous behaviour of some series of inflation and inflation uncertainty, the unconditional quantile regression estimation technique has been applied because of its robustness to the particular non-normal characteristics and outliers' presence in the empirical data. According to the findings, both Friedman's and Cukierman-Meltzer's hypotheses have been confirmed primarily for the largest EEC with flexible exchange rate. In contrast, these theories are refuted in smaller, open economies with firm exchange rate regime.

This paper investigates whether the portfolio-balance approach or the flow-oriented theory better explains the connection between stocks and exchange rate in various time-horizons in the four East Asian countries — Indonesia, Thailand, South Korea and Japan. For the analysis, we use different approaches of the wavelet methodology — wavelet correlation, wavelet coherence and wavelet cross-correlation. Wavelet correlations suggest that negative correlation is dominant across the wavelet scales in the emerging East Asian markets, which indicates that the portfolio-balance approach, that is, capital mobility stands behind this nexus. For the Japanese case, we find positive wavelet correlation across the scales, which suggests that the flow-oriented model or current account explains the interlink. Results of wavelet coherence are in line with the wavelet correlation results, and these results provide an additional evidence that investors’ panic during World financial crisis was the main culprit behind the massive financial fund reallocation in the all emerging Asian markets.

This paper investigates the idiosyncratic volatility spillover effect from the Brent oil futures market to the 11 stock markets of Central and Eastern European economies. As volatility proxies, we use regime‐switching conditional volatilities, obtained from two‐states MS‐GARCH model. In order to determine the level of this effect in different market conditions and in different time‐horizons, we combine wavelet methodology with the quantile regression approach. Our results indicate that the volatility spillover effect is not particularly strong across the countries and the wavelet scales, except in those conditions when stock market volatility is exceptionally high. Also, the wavelet‐based quantile parameters report that the volatility transmission effect gradually subsides with the flow of time, and it applies for the majority of the indices. Romanian BET index experiences the strongest volatility spillover effect from oil in conditions when Romanian stock market is under extreme stress. The reason for this finding probably lies in the facts that Romania is the largest oil and gas producer among all CEECs, and oil and gas markets tend to comove strongly. Based on findings of wavelet quantile parameters and wavelet correlations, we can conclude that hedgers and portfolio managers can build their portfolio strategies, combining Brent oil futures with the CEE indices.