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Mit einem um die Behandlungskapazität des Gesundheitssystems erweiterten epidemiologischen SIRD-Modell werden Mechanismen und Dynamik einer Virusepidemie wie Corona anhand von stilisierten politischen Reaktionsmustern (Ignore, Shutdown, Ignore-Shutdown-Relax) simuliert. Ferner werden aus dem Modell Lehren für die statistische Analyse von Corona gezogen, wie die Aussagekraft publizierter Verdopplungszeiten und Reproduktionszahlen. Die Dunkelziffer unbestätigter Fälle und die im Epidemieverlauf variable Genauigkeit von medizinischen Infektionstests werden diskutiert. Zur Messung der medizinischen Kosten von Corona sowie für regionale und internationale Vergleiche wird ein Schadensindex der verlorenen Lebenszeit vorgeschlagen. Zuletzt geht die Arbeit kurz auf die ökonomischen Kosten von Corona in Deutschland ein.
We use a novel disaggregate sectoral euro area data set with a regional breakdown to investigate price changes and suggest a new method to extract factors from over-lapping data blocks. This allows us to separately estimate aggregate, sectoral, country-specific and regional components of price changes. We thereby provide an improved estimate of the sectoral factor in comparison with previous literature, which decomposes price changes into an aggregate and idiosyncratic component only, and interprets the latter as sectoral. We find that the sectoral component explains much less of the variation in sectoral regional inflation rates and exhibits much less volatility than previous findings for the US indicate. We further contribute to the literature on price setting by providing evidence that country- and region-specific factors play an important role in addition to the sector-specific factors, emphasising heterogeneity of inflation dynamics along different dimensions. We also conclude that sectoral price changes have a “geographical” dimension, that leads to new insights regarding the properties of sectoral price changes.
This paper addresses the open debate about the usefulness of high-frequency (HF) data in large-scale portfolio allocation. Daily covariances are estimated based on HF data of the S&P 500 universe employing a blocked realized kernel estimator. We propose forecasting covariance matrices using a multi-scale spectral decomposition where volatilities, correlation eigenvalues and eigenvectors evolve on different frequencies. In an extensive out-of-sample forecasting study, we show that the proposed approach yields less risky and more diversified portfolio allocations as prevailing methods employing daily data. These performance gains hold over longer horizons than previous studies have shown.
