Stock Prices of Renewable Energy Firms: Are There Asymmetric Responses to Oil Price Changes?

Abstract

This article revisits the question of whether crude oil prices have a positive effect on stock the prices of renewable energy firms. To examine this question carefully, we allow for the asymmetric effects of oil price changes in our modeling process, using the nonlinear autoregressive distributed lag (ARDL) approach. We find that changes in oil prices indeed have a significant, positive short-run effect on renewable energy stock prices in an asymmetric manner. However, this short-run effect does not appear to last in the long-run.

1. Introduction

The conventional media wisdom suggests that the price rise of crude oil tends to positively impact stock prices of renewable energy firms because rising oil prices make renewable energy more competitive against petroleum in the energy market. Is the proposed link empirically valid? In this short article, we employ one of the recently most widely used time series models—an autoregressive distributed lag (ARDL) approach1—to show that there is some evidence that the hike in the price of crude oil increases renewable energy stock prices, holding other factors (e.g., stock prices of technology firms and interest rates) fixed. Our main contribution to the existing literature is to pay close attention to the issue of whether oil prices have an asymmetric or symmetric effect on stock prices of renewable energy firms.

Many studies have investigated the independent effect of crude oil prices on stock prices of renewable energy firms over the past decade. Examples includes, but are not limited to, Henriques and Sadorsky (2008), Kumar et al. (2012), Sadorsky (2012), Broadstock et al. (2012), Managi and Okimoto (2013), Cummins et al. (2014), Bondia et al. (2016), Reboredo (2015), Reboredo et al. (2017). The empirical evidence provided by those researchers is rather mixed. For example, Henriques and Sadorsky (2008) and Sadorsky (2012) report that oil price shocks have little effect on the stock prices of renewable energy firms. Reboredo (2015), on the other hand, provides evidence that oil prices indeed have a positive effect on renewable energy firms’ stock prices.

The main factor contributing to the mixed findings may be closely associated with neglecting the possibility of the asymmetric effects of oil price shocks on stock prices of renewable energy firms, although investors’ expectations and responses to an oil price hike are very likely to differ from their expectations and responses to an oil price drop in the real world.2 In other words, the existing studies do not directly allow for the asymmetrical effects of oil prices in the modeling process and cast doubts on their conclusions. Hence, further testing of the topic is warranted, which is what we intend to do in this article.

It should be emphasized at the onset that many studies use time series methods in examining the oil price stock market nexus and our paper is essentially part of them. Given the modeling approach, these studies can be classified into two groups. The first group uses nonstructural VAR/VEC models in tackling the issue (e.g., Huang et al. 1996; Sadorsky 1999; Papapetrou 2001; Park and Ratti 2008; Cong et al. 2008; Masih et al. 2011; Cunado and Perez de Gracia 2014). For example, Huang et al. (1996) and Sadorsky (1999) use a VAR model to analyze the effect of oil prices on U.S. stock markets. They conclude that oil price movements are important in explaining stock returns. Masih et al. (2011) and Bondia et al. (2016) confirm the findings of Huang et al. (1996) and Sadorsky (1999) using a VEC model. The second group addresses the oil price stock market nexus using a structural VAR model (e.g., Kilian and Park 2009; Basher and Sadorsky 2006; Wang et al. 2013; Fang and You 2014; Kang et al. 2015). For example, Kilian and Park (2009) use a structural VAR model and show that U.S. stock returns differ substantially depending on whether oil price changes are caused by supply or demand shocks in the oil market. However, a few studies have sought to address the possibility of the asymmetrical effects of oil prices.

The paper is organized as follows. The models and applications of an ARDL method are presented first. This is followed by a description of the data which consists of monthly observations over the period January 2002 to December 2016. Our empirical results are then discussed. In the final section, we provide our summary and conclusion.

2. Models and Methods

When examining the effect of oil prices on stock prices of renewable energy firms, we rely on an empirical framework developed by Henriques and Sadorsky (2008), and Managi and Okimoto (2013). In its basic form, stock prices of renewable energy firms are determined by the model:

$${spr}_t={\beta }_0+{\beta }_{1}co{p}_t+{\beta }_{2}sp{t}_t+{\beta }_{3}in{t}_t+u_t$$

(1)

where spr_t is the natural log of stock prices of renewable energy firms; cop_t is the natural log of crude oil prices; spt_t is the natural log of stock prices of technology firms; int_t is the interest rate; and u_t is the error term. If rising oil prices tend to encourage the use of renewable energy sources as a viable alternative for petroleum and increase stock prices of renewable energy firms, then β₁ > 0. Given the fact that stock investors tend to view renewable energy firms as identical with high technology firms, stock prices of technology firms are likely to have a significant influence on stock prices of renewable energy firms. By including spt_t explicitly in the model, therefore, we are able to control for its effect on spr_t. If the two stock prices move in the same direction, then β₂ > 0. Finally, if stock prices move inversely to changes in interest rate as economic theory suggests, then β₃ < 0.

Notably, the underlying assumption in Equation (1) is that changes in crude oil prices have a symmetrical effect on the renewable energy stock prices. In other words, if a one percent increase in oil prices increases the stock price by, say, x%, a one percent decrease in oil prices should decrease it by x% as well. If not, then oil price changes are likely to have asymmetrical effects. To address asymmetrical effects of oil price changes adequately, Umekwe and Baek (2017) decompose oil price changes into two variables—one involving only oil price increases (POS_t) and the other one involving only oil price decreases (NEG_t):

$$PO{S}_t={\displaystyle \sum _{j=1}^t\Delta \ln co{p}_j^{+}={\displaystyle \sum _{j=1}^t\max }}(\Delta \ln co{p}_j,0)$$

(2)

$$NE{G}_t={\displaystyle \sum _{j=1}^t\Delta \ln co{p}_j^{-}={\displaystyle \sum _{j=1}^t\min }}(\Delta \ln co{p}_j,0)$$

(3)

In order to perform the ARDL modeling that allows for asymmetrical effects of oil price changes, we first replace variable cop_t in Equation (1) by POS_t and NEG_t, and then transform the original model into an error-correction model by incorporating the short-run dynamics among the variables.3 Hence, the model could give the results of both short-run dynamics and long-run dynamics.

$$\begin{array}{l}\Delta sp{r}_t={\beta }_0+{\displaystyle \sum _{k=1}^p{\beta }_{k1}}\Delta sp{r}_{t-k}+{\displaystyle \sum _{k=0}^p{\beta }_{k2}}\Delta PO{S}_{t-k}+{\displaystyle \sum _{k=0}^p{\beta }_{k3}}\Delta NE{G}_{t-k}+{\displaystyle \sum _{k=0}^p{\beta }_{k4}}\Delta sp{t}_{t-k}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\displaystyle \sum _{k=0}^p{\beta }_{k5}}\Delta in{t}_{t-k}+{\theta }_{0}sp{r}_{t-1}+{\theta }_{1}PO{S}_{t-1}+{\theta }_{2}NE{G}_{t-1}+{\theta }_{3}sp{t}_{t-1}+{\theta }_{4}in{t}_{t-1}+{\xi }_t\end{array}$$

(4)

Since the inclusion of POS_t and NEG_t incorporates nonlinearity into the analysis, Equation (4) is labeled as the nonlinear ARDL model (Shin et al. 2014). The coefficients of the first differences represented by β_k1 to β_k4 allow us to study the short-run dynamics in the relationship among the variables. The coefficients of θ₁ to θ₃ by normalizing the coefficient of θ₀ describe the long-run stock price relationship.4 After Equation (4) is estimated, short- and long-run asymmetrical effects are said to be present if ${\displaystyle \sum {\beta }_{k2}}\ne {\displaystyle \sum {\beta }_{k3}}$ and ${\theta }_1/{\theta }_0\ne {\theta }_2/{\theta }_0$, respectively. For this purpose, we carry out the Wald test.

3. Data

Monthly data relating to the period of January 2002 to December 2016 are used for estimating Equation (4).5 The WilderHill New Energy Global Innovation Index (NEX) is used as a proxy for stock prices of renewable energy firms (spr_t) and is taken from Bloomberg. NEX represents a global index of 94 companies listed on 28 exchanges in 22 countries whose technologies focus on the generation and use of cleaner energy. The index currently comprises companies from all around the world, 39.0% of Americas, 31.5% of Europe, Middle East & Africa, and 29.5% of Asia & Oceania. In terms of the business sector, the index is composed of 32.2% of energy efficiency, 30.8% of wind power, and 19.1% of solar power. The West Texas Intermediate (WTI) spot prices (cop_t) are used as a proxy for crude oil prices and is obtained from the U.S. Energy Information Administration (EIA). The NYSE Arca Tech 100 Index is used as a proxy for stock prices of technology companies (spt_t) and is collected from Bloomberg. The index is a price-weighted index of 100 technology-related companies in 16 different industries such as computer hardware, software, semiconductors, telecom, data storage, electronics & healthcare equipment. The 10-year treasury constant maturity rate is used as a proxy for interest rates (int_t) and is taken from the Federal Reserve Bank of St. Louis. Finally, descriptive statistics of data are summarized in

Table 1. Descriptive Statistics.

	Mean	Standard Deviation	Min	Max
sprt	199.869	78.956	95.280	455.190
copt	67.074	26.764	19.720	133.880
sptt	1104.542	496.679	385.730	2153.540
intt	3.347	1.084	5.280	1.500

.

4. The Results

In this section, we test whether changes in oil prices have an asymmetric effect on stock prices of renewable energy firms. For this purpose, we first impose a maximum lag length of 3 months on each of the first-differenced variables in Equation (4). The Akaike Information Criterion (AIC) then determines the ARDL (2, 1, 3, 1, 1) as the optimal model specification.

Table 2. Results of nonlinear ARDL model.

Panel A: Short-Run Coefficient Estimates
Independent Variables	Dependent Variable: sprt
	Lag Order
	0	1	2	3
Δ(sprt)	0.076
	(1.556)
Δ(POSt)	0.243
	(2.914) **
Δ(NEGt)	0.116	0.026	−0.152
	(1.652) *	(0.366)	(−2.349) **
Δ(sptt)	1.202
	(17.231) **
Δ(intt)	−0.019
	(−1.096)
Panel B: Long-Run Coefficient Estimates
POSt	NEGt	sptt	intt	Constant
0.037	0.143	1.579	0.696	−8.252
(0.111)	(0.430)	(1.677) *	(3.066) **	(−1.310)
Panel C: Diagnostic Statistics
ect−1	LM	Wald-S	Wald-L	Adj. R2
−0.036	8.433	4.176	0.507	0.692
(−1.947) **	(0.750)	(0.041) **	(0.476)

Notes: Numbers inside the parentheses and brackets are t-statistics and p-values, respectively. ** and * demarcate significance at the 5% and 10% levels, respectively. ec_t−1 denotes an error-correction term. LM represents the Lagrange multiplier test of serial correlation. Wald-S and Wald-L are the Wald tests for short-run asymmetry and long-run asymmetry, respectively.

contains the short-and long-run results.

Our primary interest is in what happens to the coefficients on oil price increases (POS_t) and decreases (NEG_t). In the short run, the estimated elasticity of oil price increases is 0.243, indicating the immediate 0.243% increase in renewable energy stock prices given a 1% increase in oil prices (Panel A). This is strongly significant with t = 2.914 and verifies a positive relationship between rising oil prices and renewable energy stock prices in the short-run. The estimated elasticities of oil price decreases, on the other hand, give mixed results. Even though ∆NEG_t and ∆NEG_t−1 have positive coefficients, only ∆NEG_t has a marginally significant t statistic (two-sided p-value = 0.10), this is 0.116, suggesting the immediate 0.116% decrease in renewable energy stock prices given a 1% decrease in oil prices. For ∆NEG_t−2, however, the coefficient becomes −0.152 and is very significant with t = −2.349, indicating the 0.152% increase in renewable energy stock prices after 2 months given a 1% decrease in oil prices. Given different signs and magnitudes of the coefficients in ∆POS_t and ∆NEG_t, therefore, the results appear to support short-run asymmetric effects of oil price changes. Indeed, this short-run asymmetric effect is confirmed by the Wald test. The Wald test statistic is 4.176, and this is the outcome of a ${\chi }_1^2$ random variable (Panel C). The p-value is less than 0.05. This rejects the null hypothesis of no short-run asymmetry at the 5% level, providing evidence of short-run asymmetrical effects. Do short-run effects last into the long-run? This does not seem to be the case in the long-run. Neither POS_t nor NEG_t are statistically significant in the long run, although the estimated elasticities are both positive (Panel B). Thus, there is little evidence supporting a positive relationship between rising oil prices and renewable energy stock prices in the long-run. Further, the Wald test produces ${\chi }_1^2$ = 0.507, and the associated p-value is 0.476 (Panel C). This fails to reject the null of no long-run asymmetry, thereby providing no evidence of the long-run asymmetrical effects.

The estimated elasticity of stock prices of technology firms for the short-run is 1.202 and for the long-run it is 1.579 (Panels A and B), so that, holding oil prices and interest rates fixed, a 1% increase in tech stock prices is associated with a 1.202% (1.579%) increase in renewable energy stock prices in the short-run (long-run). The short- and long-run coefficients are both statistically significant, the latter at just about the 10% level against a two-sided alternative (p-value = 0.096). With regard to the interest rate, the estimated coefficient for the short-run is -0.019, with t = −1.096 (Panel A). This is not significant and suggests that interest rates have little effect on stock prices of renewable energy firms in the short-run. The estimated coefficient for the long-run, however, becomes positive (0.696) and the effect is statistically significant with a t statistic of 3.066 (Panel B). Therefore, a higher interest rate increases renewable energy stock prices. This seems counterintuitive. One possibility is that, since higher interest rates are most likely a sign of an economic boom and hence a surging stock market, changes in interest rates may have a positive effect on renewable energy stock prices.

Before leaving this section, we demonstrate that since all the series in Equation (4) are cointegrated, the discussed estimation results do not suffer from the spurious regression problem. For this purpose, we calculate the F-statistic to test the null hypothesis that all the five variables have no effect on stock prices of renewable energy firms. This is stated as H₀: θ₀ = θ₁ = θ₂ = θ₃ = θ₄ = 0. We would reject the null and support cointegration if the computed value of the F-statistic exceeds the upper critical value given by Pesaran et al. (2001). In fact, our calculated F-statistics is 3.82. The 10% upper critical value is about 3.52, and so we soundly reject the null. Therefore, there is evidence of cointegration among the variables.6 We also test serial correlation in the error term of Equation (4) in order to detect dynamic misspecification and validate statistical testing (Panel C). Using the usual Lagrange Multiplier (LM) statistic, we obtain LM = 8.433; in a chi-square distribution with 12 degrees of freedom, this yields a p-value = 0.750. Thus, we do not reject the null and there is little evidence of serial correlation in the residuals, so our model specification and statistical testing are well justified. Finally, stability tests such as cumulative sum (CUSUM) and cumulative sum of squares (CUSUMSQ) reveal that our model performs very well (Figure 1).

5. Concluding Remarks

In this article, we revisit one of the rapidly growing empirical questions in energy economics: Is there a positive relationship between crude oil prices and stock prices of renewable energy firms? To explore this question thoroughly, we take specific account of the asymmetrical effects of oil price changes in our modeling process, using the nonlinear ARDL model of Shin et al. (2014). It was hoped that this effort would lend confidence to the robustness and reliability of our findings. Our results show that rising oil prices appear to increase the stock prices of renewable energy firms in the short-run, but not in the long-run. We also find that oil price changes have asymmetrical effects on renewable energy stock prices in the short-run. However, there is no evidence that the short-run asymmetrical effects last into the long-run. Thus, we conclude that potential asymmetrical effects of any changes in the price of crude oil on renewable energy stock prices are a short-run rather than long-run phenomenon. From the findings, we can further make a couple of statements regarding the issue. First, from a methodological perspective, when examining the oil price—renewable stock price nexus, economists need to incorporate the (short-run) asymmetry of oil price fluctuations; otherwise, the empirical models are likely to be misspecified, thereby providing misleading results. Second, from a policy perspective, given the significant short-run (asymmetric) effect of oil prices on renewable stock prices, renewable stocks would be traded more widely and potentially pushing up stock prices and making it more attractive to investors as a short-term investment as the price of oil rises. Thus, it seems safe for us to conjecture that the recent spike in oil prices from as low as $30 per barrel to more than $70 is expected to attract more investors towards renewables, at least in the short-run.