A firm-level analysis of the upstream-downstream dichotomy in the oil-stock nexus

In this paper, we query whether the stock prices of nonintegrated firms in the upstream and downstream sectors of the global oil supply chain respond symmetrically to changes in oil prices. This inquiry relates to the “homogenous expectation” assumption among investors and fund managers pertaining to the returns and variances of assets of specialized firms operating in upstream and downstream sectors of the supply chain. Motivated by the Arbitrage Pricing Theory, we formulate a Panel Autoregressive Distributed Lag (PARDL) model, which explains the possible macroeconomic factors in the oil-stock nexus as well as any inherent persistence and heterogeneity effects due to large cross-sections and time. In accordance with the Shin et al. (2014) approach, a Nonlinear Panel ARDL model is also formulated to test for possible asymmetric responses of the nonintegrated oil firms to positive and negative changes in the oil price. Our findings indicate that the stock prices of upstream and downstream firms move in contrasting directions in response to changes in the benchmark crude oil prices in the long-run. Specifically, we show that the stock prices of upstream sector firms increased in response to an increase in oil prices, while the reverse holds for the stock prices of downstream firms. In the short run, returns on the stock of firms in both sectors increase following an increase in oil prices; however, downstream firms’ stock returns decreased in response to negative oil price shocks. The findings further show that both sectors respond differently to episodic changes in market conditions that emanated from the global financial crisis. However, upstream firms show a stronger response to changing market conditions than their downstream counterparts

PALM OIL PRICE–EXCHANGE RATE NEXUS IN INDONESIA AND MALAYSIA

In this study, we extend the literature analyzing the predictive content of commodity prices for exchange rates by examining the role of palm oil price. Our analysis focuses on Indonesia and Malaysia, the two top producers and exporters of palm oil, and utilizes daily data covering the period from December 12, 2011 to March 29, 2021, which is partitioned into two sub-samples based on the COVID-19 pandemic. Relying on a methodology that accommodates some salient features of the variables of interest, we find that on average the in-sample predictability of palm oil price for exchange rate movements is stronger for Indonesia than for Malaysia. While Indonesia’s exchange rate appreciates due to a rise in palm oil price regardless of the choice of predictive model, Malaysia’s exchange rate only appreciates after adjusting for oil price. However, both exchange rates do not seem to be resilient to the COVID-19 pandemic as they depreciate amidst dwindling palm oil price. Similar outcomes are observed for the out-of-sample predictability analysis. We highlight avenues for future research and the implications of our results for portfolio diversification strategies.In this study, we extend the literature analyzing the predictive content of commodity prices for exchange rates by examining the role of palm oil price. Our analysis focuses on Indonesia and Malaysia, the two top producers and exporters of palm oil, and utilizes daily data covering the period from December 12, 2011 to March 29, 2021, which is partitioned into two sub-samples based on the COVID-19 pandemic. Relying on a methodology that accommodates some salient features of the variables of interest, we find that on average the in-sample predictability of palm oil price for exchange rate movements is stronger for Indonesia than for Malaysia. While Indonesia’s exchange rate appreciates due to a rise in palm oil price regardless of the choice of predictive model, Malaysia’s exchange rate only appreciates after adjusting for oil price. However, both exchange rates do not seem to be resilient to the COVID-19 pandemic as they depreciate amidst dwindling palm oil price. Similar outcomes are observed for the out-of-sample predictability analysis. We highlight avenues for future research and the implications of our results for portfolio diversification strategies

Dynamic spillovers between stock and money markets in Nigeria: A VARMA-GARCH approach

This study examines probable dynamic spillover transmissions between the Nigerian stock and money markets using the multivariate volatility framework that simultaneously accounts for both returns and shock spillovers. Based on relevant pre-tests, the VARMA-CCC-GARCH framework is selected and consequently employed to model the spillovers. The study finds significant cross-market return and shock spillovers between the two markets. Thus, a shock to one market is more likely to spill over to the other market. It is also observed that shocks have persistent effects on stock market volatility but transitory effects on money market volatility. In other words, shocks to the money market die out over time while shocks to stock market tend to persist over time. In addition, including lagged own shocks and lagged own conditional variance when forecasting the future volatility of both return series may enhance their forecast performance. An alternative approach proposed by Diebold and Yilmaz (2012) is also employed for robustness and the results are consistent with those obtained from the VARMA-CCC-GARCH model

The U.S. Nonfarm Payroll and the out-of-sample predictability of output growth for over six decades

We examine the predictive prowess of the U.S. Nonfarm Payroll (USNFP) for output growth in the U.S. covering over six decades from 1947 to 2021. Using two different measures of output growth (with Gross Domestic Product growth being used for the main analysis and growth in Industrial Production Index for robustness check), our predictability results show that the U.S. Nonfarm Payroll offers some predictive information for output growth in the U.S. and the out-of-sample forecast results equally attest to the superiority of the USNFP-based model over the model that ignores it. Our findings have implications for policy directions in the U.S. and various national and regional governments, multilateral agencies and investors whose economic and financial conditions are directly or indirectly linked with the U.S. economy.http://link.springer.com/journal/111352023-02-13hj2023Economic

Improving the predictability of the oil–US stock nexus: The role of macroeconomic variables

In this study, we revisit the oil–stock nexus by accounting for the role of macroeconomic variables and testing their in-sample and out-of-sample predictive powers. We follow the approaches of Lewellen (2004) and Westerlund and Narayan (2015), which were formulated into a linear multi-predictive form by Makin et al. (2014) and Salisu et al. (2018) and a nonlinear multi-predictive model by Salisu and Isah (2018). Thereafter, we extend the multi-predictive model to account for structural breaks and asymmetries. Our analyses are conducted on aggregate and sectoral stock price indexes for the US stock market. Our proposed predictive model, which accounts for macroeconomic variables, outperforms the oil-based single-factor variant in forecasting aggregate and sectoral US stocks for both in-sample and out-of-sample forecasts. We find that it is important to account for structural breaks in our proposed predictive model, although asymmetries do not seem to improve predictability. In addition, we show that it is important to pre-test the predictors for persistence, endogeneity, and conditional heteroscedasticity, particularly when modeling with high-frequency series. Our results are robust to different forecast measures and forecast horizons

Comparative Performance of Volatility Models for Oil Price

  In this paper, we compare the performance of volatility models for oil price using daily returns of WTI. The innovations of this paper are in two folds: (i) we analyse the oil price across three sub samples namely period before, during and after the global financial crisis, (ii) we also analyse the comparative performance of both symmetric and asymmetric volatility models for the oil price. We find that oil price was most volatile during the global financial crises compared to other sub samples. Based on the appropriate model selection criteria, the asymmetric GARCH models appear superior to the symmetric ones in dealing with oil price volatility. This finding indicates evidence of leverage effects in the oil market and ignoring these effects in oil price modelling will lead to serious biases and misleading results. Keywords: Crude oil price; Volatility modelling; Global financial crisis JEL Classifications: C22; G01; Q4

Oil-price uncertainty and the U.K. unemployment rate : a forecasting experiment with random forests using 150 years of data

We analyze the predictive role of oil-price uncertainty for changes in the UK unemployment rate using more than a century of monthly data covering the period from 1859 (when the drilling of the first oil well started at Titusville, Pennsylvania, United States) to 2020. To this end, we use a machine-learning technique known as random forests. Random forests render it possible to model the potentially nonlinear link between oil-price uncertainty and subsequent changes in the unemployment rate in an entirely data-driven way, where it is possible to control for the impact of several other macroeconomic variables and other macroeconomic and financial uncertainties. We estimate random forests on rolling-estimation windows and find evidence that oil-price uncertainty predicts out-of-sample changes in the unemployment rate, especially at longer (six and twelve months) forecast horizons. Moreover, the relative importance of oil-price uncertainty has undergone substantial swings during the history of the modern petroleum industry. Relative importance was high in the 1970s and the 1980s, and it was higher than the relative importance of changes in the oil price itself for most of the sample period. We also find that oil-price uncertainty has predictive value for changes in the unemployment rate when we use a Lasso estimator, where random forests have a superior forecasting performance relative to the Lasso forecasts.http://www.elsevier.com/locate/resourpolhj2023Economic

A note on uncertainty due to infectious diseases and output growth of the United States : a mixed-frequency forecasting experiment

Utilizing a mixed data sampling (MIDAS) approach, we show that a daily newspaper-based index of uncertainty associated with infectious diseases can be used to predict, both in- and out-of-samples, low-frequency movements of output growth for the United States (US). The predictability of monthly industrial production growth and quarterly real Gross Domestic Product (GDP) growth during the current period of heightened economic uncertainty due to the COVID-19 pandemic is likely to be of tremendous value to policymakers.http://www.worldscientific.com/worldscinet/afehj2023Economic