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.

In this paper we propose a novel nonlinear model to capture asymmetries in real estate cycles. The approach involves a particular parametrization of the transition function used in the transition equation of a smooth transition autoregressive model which improves the fit in the non-central probability region. The dynamic symmetry in house price cycles is strongly rejected for the housing markets taken into consideration. Further, our results show that the proposed model performs well in a out of sample forecasting exercise