Recently proposed tests for unit root and other nonstationarity of Robinson (1994a) are applied to an extended version of the data set used by Nelson and Plosser (1982). Unusually, the tests are efficient (against appropriate parametric alternatives), the null can be any member of the I(d) class, and the null limit distribution is chi-squared. The conclusions vary substantially across fourteen series, and across different models of the disturbances (which, also unusually, include the Bloomfield spectral model). Overall, the consumer price index and money stock seem the most nonstationary, while industrial production and unemployment rate seem the closest to stationarity.

Testing of Unit Root and Other Nonstationary Hypotheses in Macroeconomic Time Series - (Now published in 'Journal of Econometrics', 80, 1997, pp.241-268.)

COVID-19 lockdowns, stimulus packages, travel bans, and stock returns.

How COVID-19 drives connectedness among commodity and financial markets: Evidence from TVP-VAR and causality-in-quantiles techniques

Japanese currency and stock market—What happened during the COVID-19 pandemic?

The economics of COVID-19 pandemic: A survey

Motivated by the COVID-19 pandemic, we examine the role of uncertainty due to infectious diseases in predicting energy market volatility using the new dataset on Equity Market Volatility-Infectious Diseases (EMV-ID). We find that the new measure of market uncertainty is a good predictor of energy market volatility in both in-sample and out-of-sample tests. These results have implications for portfolio diversification strategies, which we set aside for future research.

/pdf/uncertainty-due-to-infectious-diseases-and-energy-market-59hv7lsyp1.pdf

Uncertainty Due to Infectious Diseases and Energy Market Volatility

Gold as a hedge against oil shocks: Evidence from new datasets for oil shocks

In this paper, we consider Google trends (G‐trends) as a measure of investors' attention in the predictability of stock returns across eleven major US sectors. The theoretical motivation for our paper is clear. In seeking information to guide investment decisions, investors' sentiments are shaped by news such as G‐trends that could induce changes in the prices of stocks. Thus, we construct a predictive model that incorporates G‐trends series as a predictor of stock returns and thereafter we account for evident asymmetry in G‐trends to analyze the signficance of positive‐ and negative‐worded news in the predictability of stock returns. We also compare single‐ and multi‐factor predictive models augmented with distinctive statistical effects against the baseline time series model. We highlight three key findings: (1) G‐trends record consistent negative correlations with stock returns across sectors. (2) The proposed predictive model with G‐trends outperforms the baseline (random walk) model. (3) The inclusion of asymmetry and macroeconomic variables improves the outperformance of G‐trends over the baseline model.

Stock-induced Google trends and the predictability of sectoral stock returns

This study contributes to the emerging literature offering alternative measures of uncertainty due to the COVID-19 pandemic. We combine both news-and macro-based trends to construct an index. The former involves the use of Google trends with plausible variants of words used to capture the pandemic, which are combined using principal components analysis to develop a news-based index. For the macro-based index, we identify global factors such as oil price, stock price, Dollar index, commodity index and gold price, and thereafter we obtain the macro-based uncertainty using variants of stochastic volatility models estimated with Bayesian techniques and using a dynamic factor model. Consequently, the new (composite) index is constructed by combining the news- and macro-based indexes using principal components analysis. Our empirical applications of the index to the stock return predictability of the countries hit worst by the pandemic confirm the superiority of the composite index over the existing news-based index in both the in-sample and out-of-sample forecast horizons. Our results are also robust to forecast horizons and competing model choices.

https://www.mdpi.com/2071-1050/13/6/3212/pdf

Idris A. Adediran

Papers

Uncertainty Due to Infectious Diseases and Energy Market Volatility

Gold as a hedge against oil shocks: Evidence from new datasets for oil shocks

Stock-induced Google trends and the predictability of sectoral stock returns

A New Index for Measuring Uncertainty Due to the COVID-19 Pandemic

Assessing the inflation hedging potential of coal and iron ore in Australia