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n a contribution prepared for the Athens Symposium on “Banking Union, Monetary Policy and Economic Growth”, Otmar Issing describes forward guidance by central banks as the culmination of the idea of guiding expectations by pure communication. In practice, he argues, forward guidance has proved a misguided idea. What is presented as state of the art monetary policy is an example of pretence of knowledge. Forward guidance tries to give the impression of a kind of rule-based monetary policy. De facto, however, it is an overambitious discretionary approach which, to be successful, would need much more (or rather better) information than is currently available. In Issing's view, communication must be clear and honest about the limits of monetary policy in a world of uncertainty.
In the wake of the Global Financial Crisis that started in 2007, policymakers were forced to respond quickly and forcefully to a recession caused not by short-term factors, but rather by an over-accumulation of debt by sovereigns, banks, and households: a so-called “balance sheet recession.” Though the nature of the crisis was understood relatively early on, policy prescriptions for how to deal with its consequences have continued to diverge. This paper gives a short overview of the prescriptions, the remaining challenges and key lessons for monetary policy.
This paper studies the life cycle consumption-investment-insurance problem of a family. The wage earner faces the risk of a health shock that significantly increases his probability of dying. The family can buy term life insurance with realistic features. In particular, the available contracts are long term so that decisions are sticky and can only be revised at significant costs. Furthermore, a revision is only possible as long as the insured person is healthy. A second important and realistic feature of our model is that the labor income of
the wage earner is unspanned. We document that the combination of unspanned labor income and the stickiness of insurance decisions reduces the insurance demand significantly. This is because an income shock induces the need to reduce the insurance coverage, since premia become less affordable. Since such a reduction is costly and families anticipate these potential costs, they buy less protection at all ages. In particular, young families stay away from life insurance markets altogether.
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.
This paper makes a conceptual contribution to the effect of monetary policy on financial stability. We develop a microfounded network model with endogenous network formation to analyze the impact of central banks' monetary policy interventions on systemic risk. Banks choose their portfolio, including their borrowing and lending decisions on the interbank market, to maximize profit subject to regulatory constraints in an asset-liability framework. Systemic risk arises in the form of multiple bank defaults driven by common shock exposure on asset markets, direct contagion via the interbank market, and firesale spirals. The central bank injects or withdraws liquidity on the interbank markets to achieve its desired interest rate target. A tension arises between the beneficial effects of stabilized interest rates and increased loan volume and the detrimental effects of higher risk taking incentives. We find that central bank supply of liquidity quite generally increases systemic risk.
Panel Sample Selection ModelsThe empirical evidence currently available in the literature regarding the effects of a country's IMF program participation on its output growth is rather inconclusive. In this paper we propose and estimate a panel data sample selection model featuring state dependence. As in this model the output growth effects of program participation can be conditional on the realization of a state variable (conditional pooling), our framework may reconcile previous empirical evidence based on models without state-dependent effects. We find that the effects of IMF program participation on output growth vary systematically with an index reflecting a country's institutional record, and that output growth effects of program participation are significantly positive only if the program participation is coupled with sufficient improvement of the institutional record.
This paper makes a conceptual contribution to the effect of monetary policy on financial stability. We develop a microfounded network model with endogenous network formation to analyze the impact of central banks' monetary policy interventions on systemic risk. Banks choose their portfolio, including their borrowing and lending decisions on the interbank market, to maximize profit subject to regulatory constraints in an asset-liability framework. Systemic risk arises in the form of multiple bank defaults driven by common shock exposure on asset markets, direct contagion via the interbank market, and firesale spirals. The central bank injects or withdraws liquidity on the interbank markets to achieve its desired interest rate target. A tension arises between the beneficial effects of stabilized interest rates and increased loan volume and the detrimental effects of higher risk taking incentives. We find that central bank supply of liquidity quite generally increases systemic risk.
We explore the sources of household balance sheet adjustment following the collapse of the housing market in 2006. First, we use microdata from the Federal Reserve Board’s Senior Loan Officer Opinion Survey to document that banks cumulatively tightened consumer lending standards more in counties that experienced a house price boom in the mid-2000s than in non-boom counties. We then use the idea that renters, unlike homeowners, did not experience an adverse wealth shock when the housing market collapsed to examine the relative importance of two explanations for the observed deleveraging and the sluggish pickup in consumption after 2008. First, households may have optimally adjusted to lower wealth by reducing their demand for debt and implicitly, their demand for consumption. Alternatively, banks may have been more reluctant to lend in areas with pronounced real estate declines. Our evidence is consistent with the second explanation. Renters with low risk scores, compared to homeowners in the same markets, reduced their levels of nonmortgage debt and credit card debt more in counties where house prices fell more. The contrast suggests that the observed reductions in aggregate borrowing were more driven by cutbacks in the provision of credit than by a demand-based response to lower housing wealth.
We characterize optimal redistribution in a dynastic family model with human capital. We show how a government can improve the trade-off between equality and incentives by changing the amount of observable human capital. We provide an intuitive decomposition for the wedge between human-capital investment in the laissez faire and the social optimum. This wedge differs from the wedge for bequests because human capital carries risk: its returns depend on the non-diversi
able risk of children's ability. Thus, human capital investment is encouraged more than bequests in the social optimum if human capital is a bad hedge for consumption risk.
After the Global Financial Crisis a controversial rush to fiscal austerity followed in many countries. Yet research on the effects of austerity on macroeconomic aggregates was and still is unsettled, mired by the difficulty of identifying multipliers from observational data. This paper reconciles seemingly disparate estimates of multipliers within a unified and state-contingent framework. We achieve identification of causal effects with new propensity-score based methods for time series data. Using this novel approach, we show that austerity is always a drag on growth, and especially so in depressed economies: a one percent of GDP fiscal consolidation translates into 4 percent lower real GDP after five years when implemented in the slump rather than the boom. We illustrate our findings with a counterfactual evaluation of the impact of the U.K. government’s shift to austerity policies in 2010 on subsequent growth.