Monitoring daily unemployment at risk

Using a high-frequency framework, we show that the Auroba-Diebold-Scotti (ADS) daily business conditions index significantly increases the accuracy of U.S. unemployment nowcasts in real-time. This is of particular relevance in times of recession, such as the Global Financial Crisis and the Covid-19 pandemic, when the unemployment rate is prone to rise steeply. Based on our results, the ADS index presents itself as a better predictor than the financial indicators widely used by the literature and central banks, including both interest and credit spreads and the VXO

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

Vulnerable Funding in the Global Economy

We study the international propagation of financial conditions from the United States to global financial markets. The impact is highly heterogeneous alongside the quantiles of the distribution of the two major funding sources, credit and equity. Indeed, it is greater on the lower quantiles, which means that analogous to vulnerable growth episodes, examined by the past literature, there exist as well vulnerable funding periods of a global scale, originated from financial weakness in the US. These episodes are related to downside risk in terms of credit creation and firms’ market value around the world. Our estimates differentiate between first and second moment (i.e. uncertainty) shocks to financial conditions. This distinction proves to be relevant as it uncovers a complex propagation of shocks via different economic channels. On the one hand, credit growth largely responds to first moment shocks of US financial conditions four quarters after their occurrence, which is consistent with a credit view explanation of the transmission. On the other hand, stock markets react more sensitively and rapidly (mainly within a quarter) to second moment shocks, which can be theoretically associated with a portfolio channel underlying the shocks spread. We also document a heterogeneous impact across countries. In the case of credit growth this heterogeneity is better explained by the size or depth of the markets, while in the case of stock markets, the explanation is rooted on the strength of the financial connectedness with the US

Follow-up analyses to the O3 LIGO-Virgo-KAGRA lensing searches

Along their path from source to observer, gravitational waves may be gravitationally lensed by massive objects. This results in distortions of the observed signal which can be used to extract new information about fundamental physics, astrophysics, and cosmology. Searches for these distortions amongst the observed signals from the current detector network have already been carried out, though there have as yet been no confident detections. However, predictions of the observation rate of lensing suggest detection in the future is a realistic possibility. Therefore, preparations need to be made to thoroughly investigate the candidate lensed signals. In this work, we present some of the follow-up analyses and strategies that could be applied to assess the significance of such events and ascertain what information may be extracted about the lens-source system from such candidate signals by applying them to a number of O3 candidate events, even if these signals did not yield a high significance for any of the lensing hypotheses. For strongly-lensed candidates, we verify their significance using a background of simulated unlensed events and statistics computed from lensing catalogs. We also look for potential electromagnetic counterparts. In addition, we analyse in detail a candidate for a strongly-lensed sub-threshold counterpart that is identified by a new method. For microlensing candidates, we perform model selection using a number of lens models to investigate our ability to determine the mass density profile of the lens and constrain the lens parameters. We also look for millilensing signatures in one of the lensed candidates. Applying these additional analyses does not lead to any additional evidence for lensing in the candidates that have been examined. However, it does provide important insight into potential avenues to deal with high-significance candidates in future observations.Comment: 34 pages, 27 figure

Essays on Tail Risks in Macroeconomics

[eng] This thesis contributes to two problems identified in the literature: i) How do US financial conditions impact funding markets (credit and stocks) in a large set of countries around the world under different scenarios of macro-financial distress?; and ii) What role can be played by high-frequency data, real variables, and machine learning techniques in improving the forecasting performance of macroeconomic tail risk measures? In Chapter 2, I prove answers to the former question, while in Chapters 3, 4, and 5 I deal with the latter question. From a methodological perspective, I use time series econometrics, quantile regressions, mixed data sampling methods, machine learning models, and forecasts evaluation tests to address the various research questions. Furthermore, this dissertation has implications for risk management, monetary policy, financial stability, and forecasting. In Chapter 2, I systematically document vulnerable funding episodes in the world economy. That is, financial conditions in the United States have significant predictive power in the lowest quantiles of credit growth and stock market prices around the global economy. I also show that vulnerable funding can be explained, mainly contemporaneously, by the relative market size in the case of credit markets and by the financial links with the US (measured by the total direct investment of the US as a percentage of the country’s GDP) in the case of the stock market. The policy implication of this work is clear. I show that international funding markets are a source of persistence and amplification of financial conditions shocks across the global economy. This means that a deterioration of US financial conditions calls for policy actions in other economies around the world. In the second part of my dissertation, I tackle the problem of producing accurate, out-of-sample tail forecasts for output growth, unemployment and inflation. In Chapter 3, I show that both real and financial variables reported with a daily frequency provide valuable information for monitoring periods of economic vulnerability. I further show that is possible to provide an early warning of a downturn in GDP in pseudo real-time and that this framework works well during episodes of distress. The flexible approach reported allows me to emphasize the importance of both economic theory and economic intuition when interpreting the results of forecast combinations and for improving the point forecast itself. All in all, I contribute to a better understanding of the economic signals that can be extracted from this daily information when seeking to anticipate downturns in the economy. In Chapter 4, I construct daily unemployment at risk around consensus forecasts conditional on the Aruoba-Diebold-Scotti business conditions index, using a quantile mixed sampling model. My results suggest that this indicator has better nowcasting properties than those provided by other daily financial conditioning variables, and provides early signal of unemployment rate increases, especially during episodes of distress. The results are relevant for risk monitoring and nowcasting purposes of central banks and other institutions. In Chapter 5, I investigate potential future inflation risks in a large group of countries, using inflation density forecasts based on a set of global factors as predictors. I provides evidence that, in general, global inflation factors improve the accuracy of density forecasts. Also, I show that state-of-the-art machine learning techniques provide superior predictive performance. I document heterogeneous patterns of inflation risk measure across world regions. The results of this chapter are relevant from the perspective of a central bank or an international organization, as they often want to assess risk across different regions. In this regard, I find that global factors are generally robust predictors of density forecasts across countries. This also calls attention to a synchronized reaction of the largest central banks around the world, which is likely to contribute to sustain global price stability

Essays on Tail Risks in Macroeconomics

Programa de Doctorat en Economia[eng] This thesis contributes to two problems identified in the literature: i) How do US financial conditions impact funding markets (credit and stocks) in a large set of countries around the world under different scenarios of macro-financial distress?; and ii) What role can be played by high-frequency data, real variables, and machine learning techniques in improving the forecasting performance of macroeconomic tail risk measures? In Chapter 2, I prove answers to the former question, while in Chapters 3, 4, and 5 I deal with the latter question. From a methodological perspective, I use time series econometrics, quantile regressions, mixed data sampling methods, machine learning models, and forecasts evaluation tests to address the various research questions. Furthermore, this dissertation has implications for risk management, monetary policy, financial stability, and forecasting. In Chapter 2, I systematically document vulnerable funding episodes in the world economy. That is, financial conditions in the United States have significant predictive power in the lowest quantiles of credit growth and stock market prices around the global economy. I also show that vulnerable funding can be explained, mainly contemporaneously, by the relative market size in the case of credit markets and by the financial links with the US (measured by the total direct investment of the US as a percentage of the country’s GDP) in the case of the stock market. The policy implication of this work is clear. I show that international funding markets are a source of persistence and amplification of financial conditions shocks across the global economy. This means that a deterioration of US financial conditions calls for policy actions in other economies around the world. In the second part of my dissertation, I tackle the problem of producing accurate, out-of-sample tail forecasts for output growth, unemployment and inflation. In Chapter 3, I show that both real and financial variables reported with a daily frequency provide valuable information for monitoring periods of economic vulnerability. I further show that is possible to provide an early warning of a downturn in GDP in pseudo real-time and that this framework works well during episodes of distress. The flexible approach reported allows me to emphasize the importance of both economic theory and economic intuition when interpreting the results of forecast combinations and for improving the point forecast itself. All in all, I contribute to a better understanding of the economic signals that can be extracted from this daily information when seeking to anticipate downturns in the economy. In Chapter 4, I construct daily unemployment at risk around consensus forecasts conditional on the Aruoba-Diebold-Scotti business conditions index, using a quantile mixed sampling model. My results suggest that this indicator has better nowcasting properties than those provided by other daily financial conditioning variables, and provides early signal of unemployment rate increases, especially during episodes of distress. The results are relevant for risk monitoring and nowcasting purposes of central banks and other institutions. In Chapter 5, I investigate potential future inflation risks in a large group of countries, using inflation density forecasts based on a set of global factors as predictors. I provides evidence that, in general, global inflation factors improve the accuracy of density forecasts. Also, I show that state-of-the-art machine learning techniques provide superior predictive performance. I document heterogeneous patterns of inflation risk measure across world regions. The results of this chapter are relevant from the perspective of a central bank or an international organization, as they often want to assess risk across different regions. In this regard, I find that global factors are generally robust predictors of density forecasts across countries. This also calls attention to a synchronized reaction of the largest central banks around the world, which is likely to contribute to sustain global price stability