European Government Bond Markets and Monetary Policy Surprises: Returns, Volatility and Integration

In this paper we investigate the response of bond markets to euro area and US monetary policy shocks. Specifically, we analyze the effect of unexpected changes in interest rates implemented by the European Central Bank -ECB- and the Federal Open Market Committee -FOMC- not only on the returns, but also on the volatility and the integration of European government bond markets. For all three characteristics our results show that the response to monetary policy surprises varies across groups of countries -EMU EU-15 central, EMU EU-15 peripheral, non-EMU EU-15 and non-EMU new EU-. We also find that the effects of monetary policy announcements on the level of integration are more pronounced than those on returns and volatility. Finally, our results paint a complex picture of the effects of monetary policy news releases on the level of integration. The effect of ECB monetary policy surprises differs across old and new European Union members, while the effect of FOMC monetary policy surprises differs across EMU and non-EMU members

Volatility Transmission between the stock and Currency Markets in Emerging Asia: the Impact of the Global Financial Crisis

This paper examines volatility spillovers between the stock and currency markets of ten Asian economies in the period 2003 to 2014. To carry out this analysis, a multivariate asymmetric GARCH model is used. In general, our results present evidence of bidirectional volatility spillovers between both markets, independently of the individual country’s level of development. Additionally, our findings show that the global financial crisis has had mixed effects on the volatility transmission patterns. Overall, our results suggest that exchange rate policies and investment decisions should not be implemented without first taking into consideration the links between the stock and currency markets

European government bond market integration in turbulent times [WP]

In this paper we investigate the dynamics of European government bond market integration during the financial crisis and, subsequently, during the European sovereign debt crisis. Based on the approach developed by Bae et al. -2003-, we adopt an intuitive measure of integration: the higher the number of joint extreme price rises or falls -coexceedances-, the higher the degree of integration. We also analyse the underlying determinants of the dynamics of integration using a binomial logistic regression. Our results reveal that the level of integration of European government bond markets with the euro area has changed over time, with notable differences between the financial and the European sovereign debt crises. We find that the Euribor, unexpected monetary policy announcements from the ECB and both regional and international volatility play an important role in determining the level of integration, and that, in general, the relevance of these factors does not change between the financial and the sovereign debt crises

Expected, Unexpected, Good and Bad Uncertainty

By distinguishing between four general notions of uncertainty (good expected, bad-expected, good-unexpected, bad-unexpected) within a common and simple framework, we show that it is bad-unexpected uncertainty shocks that generate a negative reaction of macroeconomic variables (such as investment and consumption), and asset prices. Other notions of uncertainty might produce even positive responses in the macroeconomy. We also show that small uncertainty shocks might have larger impacts on economic activity and financial markets than bigger shocks between one to three years after its realization. We explore the time and magnitude of uncertainty shocks by means of a novel distributed lag nonlinear model. Our results help to elucidate the real and complex nature of uncertainty, which can be both a backward or forward-looking expected or unexpected event, with markedly different consequences for the economy. They have implications for policy making, asset pricing and risk management

Measuring Uncertainty in the Stock Market [WP]

We propose a daily index of time-varying stock market uncertainty. The index is constructed after first removing the common variations in the series, based on recent advances in the literature that emphasize the difference between risk (expected variation) and uncertainty (unexpected variation). To this end, we draw on data from 25 portfolios between 1926 and 2014, sorted by size and book-to-market value. This strategy considerably reduces information requirements and modeling design costs, compared to previous proposals. We compare our index with indicators of macrouncertainty and estimate the impact of an uncertainty shock on the dynamics of variables such as production, employment, consumption, stock market prices and interest rates. Our results show that, even when the estimates can be considered as a measure of stock market uncertainty (i.e., financial uncertainty), they perform very well as indicators of the uncertainty of the economy as a whole

Spillovers from the United States to Latin American and G7 stock markets: A VAR quantile analysis [WP]

We estimate multivariate quantile models to measure the responses of the six main Latin American (LA) stock markets to a shock in the United States (US) stock index. We compare the regional responses with those of seven developed markets. In general, we document weaker tailcodependences between the US and LA than those between the US and the mature markets. Our results suggest possible diversification strategies that could be exploited by investing in Latin America following a sizable shock to the US market. We also document asymmetrical responses to the shocks depending on the conditioning quantile at which they are calculated

Daily Growth at Risk: financial or real drivers? The answer is not always the same

We estimate Growth-at-Risk (GaR) statistics for the US economy using daily regressors. We show that the relative importance, in terms of forecasting power, of financial and real variables is time varying. Indeed, the optimal forecasting weights of these types of variables were clearly different during the Global Financial Crisis and the recent Covid-19 crisis, which reflects the dissimilar nature of the two crises. We introduce the LASSO and the Elastic Net into the family of mixed data sampling models used to estimate GaR and show that these methods outperform past candidates explored in the literature. The role of the VXO and ADS indicators was found to be very relevant, especially in out-of-sample exercises and during crisis episodes. Overall, our results show that daily information for both real and financial variables is key for producing accurate point and tail risk nowcasts and forecasts of economic activity

Monitoring time-varying systemic risk in sovereign debt and currency markets with generative AI

We propose generative artificial intelligence to measure systemic risk in the global markets of sovereign debt and foreign exchange. Through a comparative analysis, we explore three novel models to the economics literature and integrate them with traditional factor models. These models are: Time Variational Autoencoders, Time Generative Adversarial Networks, and Transformer-based Time-series Generative Adversarial Networks. Our empirical results provide evidence in support of the Variational Autoencoder. Results here indicate that both the Credit Default Swaps and foreign exchange markets are susceptible to systemic risk, with a historically high probability of distress observed by the end of 2022, as measured by both the Joint Probability of Distress and the Expected Proportion of Markets in Distress. Our results provide insights for governments in both developed and developing countries, since the realistic counterfactual scenarios generated by the AI, yet to occur in global markets, underscore the potential worst-case scenarios that may unfold if systemic risk materializes. Considering such scenarios is crucial when designing macroprudential policies aimed at preserving financial stability and when measuring the effectiveness of the implemented policies

Mortality and longevity risks in the United Kingdom: Dynamic factor models and copula-functions

We present a methodology to forecast mortality rates and estimate longevity and mortality risks. The methodology uses Generalized Dynamic Factor Models fitted over the differences of the log-mortality rates. We compare prediction performance with models previously proposed in the literature, such as the traditional Static Factor Model fitted over the level of log-mortality rates. We also construct risks measures by the means of vine-copula simulations, taking into account the dependence between the idiosyncratic components of the mortality rates. The methodology is implemented to project the mortality rates of the United Kingdom, for which we consider a portfolio and study longevity and mortality risks