This paper aims to enrich the understanding and modelling strategies for cryptocurrency markets by investigating major cryptocurrencies´ returns determinants and forecast their returns. To handle model uncertainty when modelling cryptocurrencies, we conduct model selection for an autoregressive...

The accuracy of variance prediction depends on both the specification and the accuracy of parameter estimation. To predict stock return variance in a large and ever-changing universe, this paper proposes to replace the classic time-series dynamics specification per each name with a...

We augment the existing literature using the Log-Periodic Power Law Singular (LPPLS) structures in the log-price dynamics to diagnose financial bubbles by providing three main innovations. First, we introduce the quantile regression to the LPPLS detection problem. This allows us to disentangle...

The role of the housing market in the everyday life of society is difficult to overestimate. The housing rents and prices directly affect standard of living of virtually every person. Housing loans constitute the largest liability of households and account for a large proportion of bank lending....

In this paper, we make multi-step forecasts of the annual growth rates of the real GDP for each of the 16 German Länder (states) simultaneously. Beside the usual panel data models, such as pooled and fixed-effects models, we apply panel models that explicitly account for spatial dependence...

We evaluate the performance of inflation forecasts based on the open-economy Phillips curve by exploiting the spatial pattern of international propagation of inflation. We model these spatial linkages using global inflation and either domestic slack or oil price fluctuations, motivated by a...

Forecasting regional variables provides very important information for political, institutional and economic agents. However, in the present context characterized by important decline of economies, heterogeneous data and regional interdependencies, it is even more difficult to carry out accurate...

Poverty in low-income countries is usually measured with large and infrequent household surveys. A challenge is to find methods to measure poverty more frequently. The objective of this study is to test a method for predicting poverty, based upon a statistical model utilizing consumption surveys...

We study the relationship between conditional quantiles of returns and the long-, medium- and short-term volatility in a portfolio of financial assets. We argue that the combination of quantile panel regression and wavelet decomposition of the volatility time series provides us with new insights...

In this paper, we evaluate the forecasting ability of 145 indicators and ten types of forecast combination schemes to predict housing prices and rents in 71 German cities. We are interested in whether local business confidence indicators facilitate substantial improvements of the forecasts,...