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In the aftermath of the financial crisis, the ECB has experienced an unprecedented deterioration in the level of trust. This raises the question as to what factors determine trust in central banking. We use a unique cross-country dataset which includes a rich set of socio-economic characteristics and supplement it with variables meant to reflect a country’s macroeconomic condition. We find that besides individual socio-economic characteristics, macroeconomic conditions play a crucial role in the trust-building process. Our results suggest that agents are boundedly rational in the trust-building process and that current ECB market operations may even be beneficial for trust in the ECB in the long-run.
We examine whether the robustifying nature of Taylor rule cross-checking under model uncertainty carries over to the case of parameter uncertainty. Adjusting monetary policy based on this kind of cross-checking can improve the outcome for the monetary authority. This, however, crucially depends on the relative welfare weight that is attached to the output gap and also the degree of monetary policy commitment. We find that Taylor rule cross-checking is on average able to improve losses when the monetary authority only moderately cares about output stabilization and when policy is set in a discretionary way.
In this paper, we propose a novel approach on how to estimate systemic risk and identify its key determinants. For all US financial companies with publicly traded equity options, we extract their option-implied value-at-risks (VaRs) and measure the spillover effects between individual company VaRs and the option-implied VaR of an US financial index. First, we study the spillover effect of increasing company risks on the financial sector. Second, we analyze which companies are most affected if the tail risk of the financial sector increases. We find that key accounting and market valuation metrics such as size, leverage, balance sheet composition, market-to-book ratio and earnings have a significant influence on the systemic risk profile of a financial institution. In contrast to earlier studies, the employed panel vector autoregression (PVAR) estimator allows for a causal interpretation of the results.
SAFE Newsletter : 2013, Q2
(2013)
SAFE Newsletter : 2013, Q1
(2013)