Effects of macroeconomic factors on stock prices for BRICS using the variational mode decomposition and quantile method

Based on the variational mode decomposition and quantile model, this article examines the response of BRICS stock prices to shocks of internal and external macroeconomic factors in different market states and over various investment horizons. The results of quantile regression show that the influence of each factor is complex and changeable across countries, market states, and time horizons, thus exhibiting obvious differences. Nevertheless, these coefficients also show a certain degree of similarity. Besides, we find the relationship between stock prices and macroeconomic variables behaved notably differently during the financial crisis in 2008 compared to other periods. Therefore, paying attention to the investment horizon and market state has extraordinary significance for various market participants.     

Computing equilibria of semi-algebraic economies using triangular decomposition and real solution classification

In this paper, we are concerned with the problem of determining the existence of multiple equilibria in economic models. We propose a general and complete approach for identifying multiplicities of equilibria in semi-algebraic economies, which may be expressed as semi-algebraic systems. The approach is based on triangular decomposition and real solution classification, two powerful tools of algebraic computation. Its effectiveness is illustrated by three examples of application.     

Forecasting China's foreign exchange reserves using dynamic model averaging: The roles of macroeconomic fundamentals,financial stress and economic uncertainty

We develop models for examining possible predictors of growth of China's foreign exchange reserves that embrace Chinese and global trade, financial and risk (uncertainty) factors. Specifically, by comparing with other alternative models, we show that the dynamic model averaging (DMA) and dynamic model selection (DMS) models outperform not only linear models (such as random walk, recursive OLS-AR(1) models, recursive OLS with all predictive variables models) but also the Bayesian model averaging (BMA) model for examining possible predictors of growth of those reserves. The DMS is the best overall across all forecast horizons. While some predictors matter more than others over the forecast horizons, there are few that stand the test of time. The US–China interest rate differential has a superior predictive power among the 13 predictors considered, followed by the nominal effective exchange rate and the interest rate spread for most of the forecast horizons. The relative predictive prowess of the oil and copper prices alternates, depending on the commodity cycles. Policy implications are also provided.