Generalized Market Uncertainty Measurement in European Stock Markets in Real Time

We estimate generalized market uncertainty indicators for the stock markets of eight European countries greatly affected by the recent Covid-19 crisis and the economic measures implemented for its containment and mitigation. Our statistics emphasize the difference between risk and uncertainty, in the aggregate, and provide readily and easily interpretable estimates, in real time, which are relevant for market participants and regulators. We show that generalized uncertainty in Europe was, indeed, at historically high levels in the wake of the recent public health crisis before the large interventions by the European Central Bank, the Fed, and the Bank of England, but also that, for some markets, recently recorded uncertainty levels were still lower than those recorded during the Global Financial Crisis, which puts things into perspective. We also show that uncertainty shocks are extremely persistent, but such persistence varies greatly across countries. The period needed for the markets to absorb half of the shock lies between less than a year and two and a half years

Price Bubbles in Lithium Markets around the World

The global energy transition to low-carbon technologies for transportation is heavily dependent on lithium. By leveraging the latest advances in timeseries econometrics we show that lithium prices (carbonate and hydroxide) have recently experienced market bubbles, particularly from the end of 2015 to the end of 2018, although in the case of European hydroxide we also date a bubble as recently as September 2020. Bubbles are accompanied by market corrections and extreme uncertainty which, in the case of lithium, may put at risk the future continuous supply needed for manufacturing lithium-based batteries for the electric vehicle. Governments and private stakeholders could reduce uncertainty imposed by these speculative dynamics, for instance, by establishing public stabilization funds and setting up capital buffers that help to diversify operational and market risks induced by a bubble bursting. Such funds should be ideally located in portfolios, such as the global stock markets or other energy commodities, which exhibit idiosyncratic bubbles unsynchronized with the bubbles observed in lithium Market

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

Rethinking Asset Pricing with Quantile Factor Models

Traditional empirical asset pricing focuses on the average cases. We propose a new approach to analyze the cross-section of the returns. We test the predictive power of market-beta, size, book-to-market ratio, profitability, investment, momentum, and liquidity, across the whole conditional distribution of market returns. Our results indicate that the relevant characteristics to explain the winners tail, the losers tail and the center of the distribution, in a given period, differ. Indeed, some characteristics can be discarded from our specification if our main interest is to model expected extreme losses (such as traditional momentum), and some others should be kept even if they do not seem particularly significant for the average scenario, because they become significant at the tails (such as size). Book-to-market is mainly a left tail factor, in the sense that it explains to a greater extent the losers tail than either the center or the tail of the winners. On the contrary, liquidity and investment are right-tail factors, because they explain in greater proportion the winners tail than the rest of the distribution. Market beta is relevant throughout the whole cross-section, but affect winners and losers in diametrically opposite ways (the effect is positive on the right tail and negative on the left tail). We show that the practice of adding characteristics to our pricing equation should be clearly informed by our particular interests regarding the cross-sectional distribution of the returns, that is, whether we are more interested in a certain fragment of the distribution than in other parts. Our results emphasize the need to consider carefully what factors to include in the pricing equation, which depends on the dynamics that one wants to understand and even on one attitude towards risk. In short, not all factors serve all purpose

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

Uncertainty, Systemic Shocks and the Global Banking Sector: Has the Crisis Modified their Relationship?

We estimate the impact of equity market uncertainty and an unobservable systemic risk factor on the returns of the major banks in the global banking sector. Our estimation combines quantile regressions, structural changes, and factor models and allows us to explore the stability of systemic risk propagation among financial institutions. We find that risk propagation has remained stable over the last decade, and we report evidence indicating that equity market uncertainty is a major systemic factor for the global banking system. Additionally, we provide a new simple tool for measuring the resilience of financial institutions to systemic shocks

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

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 tail-codependences 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. (C) 2017 Elsevier B.V. All rights reserved

Measuring uncertainty in the stock market

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 sorted by size and book to-market value. This strategy considerably reduces information requirements and modeling design costs, compared to previous proposals. We also compare our index with indicators of macro-uncertainty and estimate the impact of an uncertainty shock on the dynamics of macroeconomic variables

Trends in the quantiles of the life-table survivorship function

We offer a new approach for modeling past trends in the quantiles of the life table survivorship function. Trends in the quantiles are estimated, and the extent to which the observed patterns fit the unit root hypothesis or, alternatively, an innovative outlier model, are conducted. Then a factor model is applied to the detrended data, and it is used to construct quantile cycles. We enrich the ongoing discussion about human longevity extension by calculating specific improvements in the distribution of the survivorship function, across its full range, and not only at the central-age ranges. To illustrate our proposal, we use data for the UK from 1922 to 2013. We find that there is no sign in the data of any reduction in the pace of longevity extension during the last decades