Remittance,Institutions and Investment Volatility Interactions: An Intercontinental Analysis

Generating massive investment for growth and development has been one of the main policy goals of most economies around the globe. Countries, most especially developing ones, are highly susceptible to investment volatility owing largely to the fragile nature of their economies as well as weaknesses in terms of dysfunctional institutions. Therefore, sound economic management suggests the need to better understand possible sources for mitigating the adverse effects of investment volatility. Remittances have been identified as important capital flows which do a good job of dousing macroeconomic volatilities. It is on this basis that the study sought to uncover the causal relationship between remittances and investment volatility via the intermediating role of institutions. Using a panel of 70 countries and the system Generalized Method of Moments (GMM) estimator, three insightful outcomes come to the fore. First, remittances played countercyclical roles across the estimated regressions. Second, institutional quality had no significant role in mitigating investment volatility and lastly, the interactive terms of both remittances and institutions significantly mitigated the negative impacts of investment volatility with the exception of the political component of the institutional architecture. Policy suggestions are drawn based on our results.     

Tales from tails: On the empirical distributions of forecasting errors and their implication to risk

When evaluating the performances of time series extrapolation methods, both researchers and practitioners typically focus on the average or median performance according to some specific error metric, such as the absolute error or the absolute percentage error. However, from a risk-assessment point of view, it is far more important to evaluate the distributions of such errors, and especially their tails. For instance, a lack of normality and symmetry in error distributions can have significant implications for decision making, such as in stock control. Moreover, frequently these distributions can only be constructed empirically, as they may be the result of a computationally-intensive non-parametric approach, such as an artificial neural network. This study proposes an approach for evaluating the empirical distributions of forecasting methods and uses it to assess eleven popular time series extrapolation approaches across two different datasets (M3 and ForeDeCk). The results highlight some very interesting tales from the tails.     

Nowcasting and forecasting GDP in emerging markets using global financial and macroeconomic diffusion indexes

This paper contributes to the nascent literature on nowcasting and forecasting GDP in emerging market economies using big data methods. This is done by analyzing the usefulness of various dimension-reduction, machine learning and shrinkage methods, including sparse principal component analysis (SPCA), the elastic net, the least absolute shrinkage operator, and least angle regression when constructing predictions using latent global macroeconomic and financial factors (diffusion indexes) in a dynamic factor model (DFM). We also utilize a judgmental dimension-reduction method called the Bloomberg Relevance Index (BRI), which is an index that assigns a measure of importance to each variable in a dataset depending on the variable’s usage by market participants. Our empirical analysis shows that, when specified using dimension-reduction methods (particularly BRI and SPCA), DFMs yield superior predictions relative to both benchmark linear econometric models and simple DFMs. Moreover, global financial and macroeconomic (business cycle) diffusion indexes constructed using targeted predictors are found to be important in four of the five emerging market economies that we study (Brazil, Mexico, South Africa, and Turkey). These findings point to the importance of spillover effects across emerging market economies, and underscore the significance of characterizing such linkages parsimoniously when utilizing high-dimensional global datasets.     

Correlated daily time series and forecasting in the M4 competition

We participated in the M4 competition for time series forecasting and here describe our methods for forecasting daily time series. We used an ensemble of five statistical forecasting methods and a method that we refer to as the correlator. Our retrospective analysis using the ground truth values published by the M4 organisers after the competition demonstrates that the correlator was responsible for most of our gains over the naïve constant forecasting method. We identify data leakage as one reason for its success, due partly to test data selected from different time intervals, and partly to quality issues with the original time series. We suggest that future forecasting competitions should provide actual dates for the time series so that some of these leakages could be avoided by participants.     

Predicting bank insolvencies using machine learning techniques

Proactively monitoring and assessing the economic health of financial institutions has always been the cornerstone of supervisory authorities. In this work, we employ a series of modeling techniques to predict bank insolvencies on a sample of US-based financial institutions. Our empirical results indicate that the method of Random Forests (RF) has a superior out-of-sample and out-of-time predictive performance, with Neural Networks also performing almost equally well as RF in out-of-time samples. These conclusions are drawn not only by comparison with broadly used bank failure models, such as Logistic, but also by comparison with other advanced machine learning techniques. Furthermore, our results illustrate that in the CAMELS evaluation framework, metrics related to earnings and capital constitute the factors with higher marginal contribution to the prediction of bank failures. Finally, we assess the generalization of our model by providing a case study to a sample of major European banks.     

Modeling the time varying volatility of housing returns: Further evidence from the U.S. metropolitan condominium markets

This study extends the literature on modeling the volatility of housing returns to the case of condominium returns for five major U.S. metropolitan areas (Boston, Chicago, Los Angeles, New York, and San Francisco). Through the estimation of ARMA models for the respective condominium returns, we find volatility clustering of the residuals. The results from an ARMA‐TGARCH‐M model reveal the absence of asymmetry in the conditional variance. Dummy variables associated with the housing market collapse unique to each metropolitan area were statistically insignificant in the conditional variance equation, but negative and statistically significant in the mean equation. Condominium markets in Los Angeles and San Francisco exhibit the greatest persistence to volatility shocks.     

Intraday return and volatility spillover mechanism from Chinese to Japanese stock market

We analyze the mechanism of return and volatility spillover effects from the Chinese to the Japanese stock market. We construct a stock price index comprised of those companies that have substantial operations in China. This China-related index responds to changes in the Shanghai Composite Index more strongly than does the TOPIX (the market index of the Tokyo Stock Exchange). This result suggests that China has a large impact on Japanese stocks via China-related firms in Japan. Furthermore, we find evidence that this response has become stronger as the Chinese economy has gained importance in recent years.     

Search efficiency,wage dynamics and welfare

Advances in information technology have improved the job-search process in the labor market. We analyze the effects of this improvement by constructing a search-and-matching model with two sectors: a risky sector with firm-specific productivity shocks and a risk-free sector. The risky sector is characterized by a low level of commitment between employers and workers – either party can end the employment relationship. We show that a better job-search process generates more job matches in the risky sector, and this benefits workers by improving their outside options. The effect on employers is subtle: while it is easier to fill vacancies, workers become more expensive. At the same time, the ease of finding new workers makes it harder for employers to keep their wage promises to workers and increases wage volatility. Our paper contributes to the literature by offering a novel explanation for the observed rise in wage volatility.