Do persistently low nominal interest rates mean that governments can safely borrow more? To addresses this question, I extend the model of Ghosh et al.  to allow for persistent stochastic changes in nominal interest and growth rates. The key model parameter is the long-run difference between nominal interest and growth rates; if negative, maximum sustainable debts (debt limits) are unbounded. I show how both VAR- and spectral-based methods produce negative point estimates of this long-run differential, but cannot reject positive values at standard significance levels. I calibrate the model to the UK using positive but statistically plausible average interest-growth differentials. This produces debt limits which increase by only around 5% GDP as interest rates fall after 2008. In contrast, only a tiny change in the long-run average interest-growth differential – from the 95th to the 97.5th percentile of the distribution – is required to move average debt limits by the same amount.
Diebold and Yilmaz (2015) recently introduced variance decomposition networks as tools for quantifying and ranking the systemic risk of individual firms. The nature of these networks and their implied rankings depend on the choice decomposition method. The standard choice is the order invariant generalized forecast error variance decomposition of Pesaran and Shin (1998). The shares of the forecast error variation, however, do not add to unity, making difficult to compare risk ratings and risks contributions at two different points in time. As a solution, this paper suggests using the Lanne-Nyberg (2016) decomposition, which shares the order invariance property. To illustrate the differences between both decomposition methods, I analyzed the global financial system during 2001 – 2016. The analysis shows that different decomposition methods yield substantially different systemic risk and vulnerability rankings. This suggests caution is warranted when using rankings and risk contributions for guiding financial regulation and economic policy.
Christoph Aymanns, Carlos Caceres, Christina Daniel, and Miss Liliana B Schumacher
Understanding the interaction between bank solvency and funding cost is a crucial pre-requisite for stress-testing. In this paper we study the sensitivity of bank funding cost to solvency measures while controlling for various other measures of bank fundamentals. The analysis includes two measures of bank funding cost: (a) average funding cost and (b) interbank funding cost as a proxy of wholesale funding cost. The main findings are: (1) Solvency is negatively and significantly related to measures of funding cost, but the effect is small in magnitude. (2) On average, the relationship is stronger for interbank funding cost than for average funding cost. (3) During periods of stress interbank funding cost is more sensitive to solvency than in normal times. Finally, (4) the relationship between funding cost and solvency appears to be non-linear, with higher sensitivity of funding cost at lower levels of solvency.
This paper develops a structural macroeconometric model of the world economy, disaggregated into forty national economies. This panel dynamic stochastic general equilibrium model features a range of nominal and real rigidities, extensive macrofinancial linkages, and diverse spillover transmission channels. A variety of monetary policy analysis, fiscal policy analysis, macroprudential policy analysis, spillover analysis, and forecasting applications of the estimated model are demonstrated. These include quantifying the monetary, fiscal and macroprudential transmission mechanisms, accounting for business cycle fluctuations, and generating relatively accurate forecasts of inflation and output growth.
Mr. Jorge I Canales Kriljenko, Mehdi Hosseinkouchack, and Alexis Meyer-Cirkel
Sub-Saharan African countries are exposed to spillovers from global financial variables, but the impact on economic activity is more significant in more financially developed economies. Generalized impulse responses from a GVAR exercise demonstrate how the CBOE volatility index (VIX) and credit conditions around the globe impact a subset of sub-Saharan African economies and regions. The estimated relationships suggest that the effect of global uncertainty is more pervasive in exports, with the impact on economic and lending activities being mixed. The channels of transmission include the effects of global financial variables on commodity prices and on trading-partner’s macroeconomic and financial variables. The analysis suggests that shocks to credit conditions in the euro area and the U.S. have not significantly affected local lending conditions or economic activity in sub-Saharan Africa during 1991-2011, except perhaps in South Africa.
This paper quantifies the economic impact of uncertainty shocks in the UK using data that span the recent Great Recession. We find that uncertainty shocks have a significant impact on economic activity in the UK, depressing industrial production and GDP. The peak impact is felt fairly quickly at around 6-12 months after the shock, and becomes statistically negligible after 18 months. Interestingly, the impact of uncertainty shocks on industrial production in the UK is strikingly similar to that of the US both in terms of the shape and magnitude of the response. However, unemployment in the UK is less affected by uncertainty shocks. Finally, we find that uncertainty shocks can account for about a quarter of the decline in industrial production during the Great Recession.