We propose to pool alternative systemic risk rankings for financial institutions using the method of principal components. The resulting overall ranking is less affected by estimation uncertainty and model risk. We apply our methodology to disentangle the common signal and the idiosyncratic...

We develop a new targeted maximum likelihood estimation method that provides improved forecasting for misspecified linear autoregressive models. The method weighs data points in the observed sample and is useful in the presence of data generating processes featuring structural breaks, complex...

We assess whether climate transition risk is priced in Europe's equity market by analysing relative equity returns of high versus low CO2-emitting firms. We use a panel data set covering firm-specific carbon emissions of 1,555 European companies over the period 2005-2019. We add to the existing...

We develop a multivariate unobserved components model to extract business cycle and financial cycle indicators from a panel of economic and financial time series of four large developed economies. Our model is flexible and allows for the inclusion of cycle components in different selections of...

If there is exchange market pressure (EMP), monetary authorities can use the interest rate and official interventions to offset this depreciation tendency, or they can let the exchange rate change. We introduce a new approach to derive how these three variables should be combined to measure EMP....

This paper discusses identification, specification, estimation and forecasting for a general class of periodic unobserved components time series models with stochastic trend, seasonal and cycle components. Convenient state space formulations are introduced for exact maximum likelihood...