In this study, we propose a wavelet-copula-GARCH procedure to investigate the occurrence of cross-market linkages during the COVID-19 pandemic. To explore cross-market linkages, we distinguish between regular interdependence and pure contagion, and associate changes in the correlation between stock market returns at higher frequencies with contagion, whereas changes at lower frequencies are associated with interdependence that relates to spillovers of shocks resulting from the normal interdependence between markets. An empirical analysis undertaken on six major stock markets reveals evidence of long-run interdependence between the markets under consideration before the start of the COVID-19 pandemic in December 2019. However, after the health crisis began, strong evidence of pure contagion among stock markets was detected.

No abstract

Purpose This paper aims to investigate the nonlinear dynamics in the effects of oil price shocks on the exchange rate for a sample from the Group of Twenty (G20) over the period 1994:1-2019:1. Design/methodology/approach Using monthly time series data covering the period1994:1-2019:1, the author first use the non-parametric triples test of Randles et al. (1980) to ascertain the existence of asymmetric properties in the sample of exchange rates. Then the author used the nonlinear ARDL cointegration approach developed by Shin et al. (2014) to examine the reaction of these exchange rates to the oil price shocks. Findings This study has identified significant evidence that the exchange rate is asymmetrically distributed, with the effect that high appreciation of the exchange rate is followed by slower depreciation. The NARDL results support such asymmetry even more strongly because in the test the exchange rate is shown to react differently in the long term to positive and negative shocks in oil prices. Another major finding was that the speed of adjustment differed over the sample, as the cumulative dynamic multipliers effect highlighted. Research limitations/implications This change in direction and the employment of non-linear technique can be to obtain better insight into the model specification, which the author believes, will not only enhance the findings in the literature but also enhance forecasting and decision-making. Practical implications A practical implication of this change is the possibility that policymakers and participants concerned with exchange rate stability should intervene in the market to alleviate the unfavourable impact of oil price shocks on the exchange rate. Originality/value Addressing this nonlinear dynamic in the effects of oil price shocks on the exchange rate have at least the following two important reasons: asymmetry and regime change are types of nonlinearities that affect the market dynamics, especially, over marked sample period with such financial crises as the global financial crises of 2007, thereby violating the linear models. Adopting an asymmetric cointegration technique permits to incorporate cointegrated positive and negative components of the considered series.

This paper examines the asymmetric behaviour of house prices in large metropolitan areas. Using a sample of large cities, in several countries, it is shown that real estate prices cycles are largely nonlinear. It is found that dynamic asymmetries in the housing market cycle can well be modelled using a logistic smooth transition model (LSTAR). Further, it is shown that the LSTAR model has better forecasting properties with respect to a linear autoregressive model.

Understanding the Stock Return-Inflation Nexus is a continuing concern among scholars. The main goal of the current study was to critically examine the view that the relation between stock return and inflation is potentially asymmetric. To capture the possibility of dynamic nonlinearity and, in turn, asymmetry, the nonlinear Autoregressive Distributed lag model (NARDL) was deployed. This study has identified that the responses of stock return are generally asymmetric. In other words, the results suggest that contractionary time appears to reduce the stock returns more than expansionary time does.