We use transfer entropy to quantify information flows between financial markets and propose a suitable bootstrap procedure for statistical inference. Transfer entropy is a model-free measure designed as the Kullback-Leibler distance of transition probabilities. Our approach allows to determine,...

We use transfer entropy to quantify information flows between financial markets and propose a suitable bootstrap procedure for statistical inference. Transfer entropy is a model-free measure designed as the Kullback-Leibler distance of transition probabilities. Our approach allows to determine,...

We investigate the determinants of a household's decision on whether to invest in risky financial assets. Financial theory suggests that with increasing labor income risk, the reluctance of households to hold stocks increases. We propose to measure income risk as the observed variation of...

We apply the concept of transfer entropy to quantify information flows between financial time series. Transfer entropy is a model-free measure designed as the Kullback-Leibler distance of transition probabilities. This approach allows to determine information transfer without being restricted to...

We propose to estimate the variance of a time series of financial returns through a quantile autoregressive model (QAR) and demonstrate that the return QAR model contains all information that is commonly captured in two separate equations for the mean and variance of a GARCH-type model. In...