The effects of petroleum product price regulation on macroeconomic stability in China

Energy Policy 132 (2019) 96–105

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The effects of petroleum product price regulation on macroeconomic stability in China

T

Zanxin Wanga,∗, Wei Weib, Junwen Luoa, Margaret Calderonc a

School of Development Studies, Yunnan University, China Key Research Institute of Yellow-river Civilization and Sustainable Development, Henan University, China c Institute of Renewable Natural Resources, University of the Philippine Los Baños, Philippines b

ARTICLE INFO

ABSTRACT

Keywords: Oil price fluctuation Price regulation Price regime Macroeconomic volatility Stabilization effect

China has undertaken measures to regulate the prices of petroleum products since 1998 in order to deal with the world oil price shocks on its macro-economy. However, the effects of price regulation are yet unknown, especially when the world oil price fluctuates in different regimes. The study first analyses the mechanisms of petroleum product price (PPP) regulation (in the case of gasoline) and the crude oil-gasoline price fluctuation transmission, followed by the identification of regimes and their time intervals using regime-switching vector autoregressive model, and then estimates the effects of gasoline price regulation in reducing macroeconomic volatility. It is found that the world crude oil fluctuates in different regimes (the mild-fluctuation regime and the violent-fluctuation regime), the PPP regulation can reduce oil price volatility and then macroeconomic volatility, but it is more effective in the mild-fluctuation regime. The findings present a deeper understanding of the stabilization effect of PPP regulation on the macroeconomy, provide an evidence for sustaining China's PPP regulation for the purpose of macroeconomic stability, and offer policymakers new information for petroleum product pricing reforms.

1. Introduction Highly volatile economic fluctuations undermine economic growth, and it is vital for an economy to maintain its stability (Ramey and Ann, 1995; Hogan, 2015; Ferraro, 2017). Among the trigger factors of volatility, the world oil price fluctuation is considered a major external shock (Hamilton, 1983; Kilian and Vigfusson, 2011). Many studies show that the world crude oil price (WCOP) fluctuation has negative impacts on macroeconomic growth from both the supply and demand sides (Hamilton, 1983; Ramey and Ann, 1995; Jimenez-Rodriguez, 2008). The conventional explanation is that, on the one hand, oil price increases lower future GDP growth by raising production costs. On the other hand high oil price volatility may reduce aggregate output because it delays business investment in uncertainty or it induces costly sectoral resource reallocation（Pindyck (1991); Guo & Kliesen, 2005). China is a country with a low reserve of oil stock and a high dependence on oil imports. Since China implemented a market-based pricing mechanism in 1998, the WCOP fluctuations have exerted a high level of risk for China's macro-economy, and have negatively impacted the stock market, foreign trade, investment, consumption, industrial

production and macroeconomic growth rate (Du et al., 2010; Ou et al., 2012; Fang and You, 2014; Rafiq and Salim, 2014; Dong et al., 2017). According to China's market-based pricing mechanism, the petroleum products are priced based on the prices in foreign market but are subject to regulation by the government. Intuitively, the question that may be posed is whether the regulation played a crucial role in stabilizing China's macroeconomy? Despite existing literature on the economic impacts of WCOP fluctuation, only a few have assessed the effects of the petroleum product price (PPP) regulation in stabilizing China's macro-economy (Zhang and Xie, 2016; Ju et al., 2017; Shi and Sun, 2017). In particular, none of them quantified the effects of oil price regulation in reducing oil volatility, making the results inconclusive. This paper analyzes the effect of China's PPP regulation in reducing oil price volatility and macroeconomic volatility. The main contribution of this paper is threefold. First, based on a constructed “cost + profit” pricing model, the WCOP-PPP transmission mechanisms are analyzed, and the effect of PPP regulations in reducing oil price volatility are mathematically quantified. Second, it is difficult to isolate the effect of the PPP regulation from the integrated effect as the available data

Corresponding author. No. 2, Cuihubei Road, Kunming city, Yunnan province, 650091, China E-mail addresses: [email protected], [email protected] (Z. Wang), [email protected] (W. Wei), [email protected] (J. Luo), [email protected] (M. Calderon). ∗

https://doi.org/10.1016/j.enpol.2019.05.022 Received 28 November 2018; Received in revised form 8 May 2019; Accepted 14 May 2019 0301-4215/ © 2019 Elsevier Ltd. All rights reserved.

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reveals, and this paper proposes a new method to quantify the effect of PPP regulation in reducing oil price volatility. Third, no literature was found to study the effect of oil price regulation on the macroeconomic stability as the oil prices were in different regimes, and this study can contribute to the literature. From a novel regime-dependent perspective, the relationship between oil price regulation and macroeconomic volatility in different regimes was analyzed using regime-switching vector autoregressive models. The remainder of the paper has four sections. Section 2 introduces China's petroleum product pricing mechanism and quantitatively analyzes the mechanisms of PPP regulation (using the case of gasoline) and the WCOP-PPP volatility transmission mechanism. Section 3 presents the empirical methods, including Regime-Switching Vector Autoregressive Model and Multi-Regime Regressive Model. Section 4 introduces data sources and the empirical results, Section 5 discusses the conclusions and policy implications.

of China, 2008), and over a period of 10 working days since 2013 (Ju et al., 2017). Therefore, assessing the effect of the pricing mechanism requires isolating the effects of “regulation”. Both Ju et al. (2017) and Shi and Sun (2017) studied the role of energy price regulation on GDP growth. The former concluded that China's economic benefits from relative and moving distortions, and the absolute distortions of energy prices have negative impacts on economic growth, while the latter found that regulatory price distortion negatively affects output growth and justified the support to energy price deregulation in China. In their studies, the price distortions were measured relative to a benchmark price, the US gasoline price. They attributed all the price difference of gasoline between China and the USA to the effect of China's oil price regulation, and did not isolate the effect of price regulation in reducing PPP volatility. Another limitation of the two studies is that the selected benchmark price per se is not appropriate due to three reasons. First, China's market-based oil pricing mechanism uses a benchmark price based on the weighted average of WCOPs, including Brent, Dubai, and Minas prices, rather than the US gasoline price. Second, the US gasoline price itself was regulated too (Coady et al., 2017). Third, the US crude oil price and gasoline price are asymmetric, in a pattern of “rockets and feathers” (Bacon, 1991; Johnson, 2002), or a pattern of “balloons and rocks” (Bremmer and Kesselring, 2016). Since the presence of any asymmetries dictating the WCOP-PPP transmission mechanism could be primarily attributed to the presence of structural distortions in the fuel sector,i.e., oligopolistic behavior, inventory levels, production lags, and market competition structures (Apergis and Vouzavalis, 2018), the use of USA gasoline price as a benchmark is obviously not appropriate, considering the different market structures in the USA and China. Rather, the WCOP should be the benchmark for assessing the effect of PPP regulation. Oil price changes may affect aggregate economic activity through two channels: (i) the change in the price of oil (relative price change) and (ii) the increase in uncertainty about future prices (volatility) (Guo & Kliesen, 2005). While it may regulate oil price and its volatility, China's petroleum product pricing mechanism has two effects in terms of the two channels. Zhang and Xie (2016), Ju et al. (2017) and Shi and Sun (2017) attempted to assess the effect of price change although the former did not disaggregate the price changes, while the latter two quantified the price change using an unreasonable benchmark. Since the two effects are potentially important (Guo & Kliesen, 2005) and oil price shocks are a considerable source of volatility for GDP in China (Du et al., 2010), assessing the effect of pricing regulation in reducing PPP volatility and macroeconomic volatility is necessary.

2. Literature review According to Du et al. (2010), the impact of the WCOP on China's macro-economy is becoming more and more significant because of the country's increasing oil consumption, higher dependence on imported oil supply and the use of market-oriented oil pricing mechanisms. Since the1990s, however, the volatility of macroeconomic growth in China has been decreasing (He and Chen, 2014), starting at about the same time when China implemented the market-based pricing mechanism. Under this mechanism, oil product prices were not purely determined by the market, but, as in many countries (IEA, 2015), were regulated by the government. Through this, policymakers hope to insulate the domestic economy from the negative impacts of high oil prices and price volatility in the world market (Shi and Sun, 2017). As output volatility is detrimental to economic growth, stabilization policies are required to mitigate short-run economic fluctuations and contribute to long-run economic growth (Lin and Kim, 2014). 2.1. Effect of PPP regulation on China's macroeconomy A few articles have assessed the effects of the PPP regulation in stabilizing China's macro-economy (Zhang and Xie, 2016; Ju et al., 2017; Shi and Sun, 2017), although none of them quantified the effects of oil price regulation in reducing oil volatility. Zhang and Xie (2016) found that the effects of China's oil pricing mechanism on the macroeconomy are quite limited and claimed that deregulation may have little impact the economy. However, although the article's title implied that it aimed to assess the role of China's market-based pricing mechanism, it assessed the effects of domestic petroleum product price on the macroeconomy but did not isolate the effect of the pricing mechanism. First, the difference between the effects of the WCOP and the PPP is not the effect of the pricing mechanism because there is a disaggregation effect of crude oil refining. That is, the WCOP volatility is disaggregated and then transmitted to the rest of the national economy via gasoline, diesel kerosene and other products. The effect of pricing mechanism is overestimated if the disaggregation effect is ignored. Second, as the important purpose of China's oil pricing mechanism is to maintain a relatively stable oil price (IEA, 2016; Zhang and Xie, 2016; Ju et al., 2017), the effect of the pricing mechanism should be assessed in terms of its role in reducing oil price volatility and macroeconomic volatility. Third, it is not justified to claim that the role of the pricing mechanism is limited based on the finding that the effects of WCOP and PPP have a similar effect on the economy in terms of industrial value added and other variables. The reasons are that the PPP was only regulated when high fluctuation occurred according to the pricing mechanism, and thus the PPP follows a curve similar to that of WCOP in most time intervals. For instance, according to the State Council’s 2009 Announcement, PPPs were adjusted when WCOP fluctuates by more than 4% over a period of 22 working days (Government

2.2. Regimes of world crude oil price The WCOP may fluctuate with different characteristics in different time periods. The WCOP normally fluctuates in the range of ± 5 dollars per barrel. Although there were violent fluctuations in 2007–2009, the WCOP almost continuously remained at a high level in 2011–2015, fluctuating at around $100 per barrel with a magnitude of ±10 U.S. dollars/barrel (IEA, 2016). The oil price fluctuation that happened at a different time may represent different regimes (Kim, 2004; Sims and Zha, 2006), one in a violent-fluctuating regime while the other in a mild-fluctuating regime. Thus, world oil price is a “multi-regime” variable (Holm-Hadulla and Hubrich, 2017; Kirstin and Robert, 2015), and the shocks on a given economy may differ too (Kirstin and Robert, 2015; Holm-Hadulla and Hubrich, 2017). Studies in the EU and the USA revealed that the shock of oil price fluctuation is short and weak in the normal regime, but sustained and strong in the adverse regime (HolmHadulla and Hubrich, 2017; Kirstin and Robert, 2015). When there are more than two regimes in a system, it is inappropriate to study the internal relations of the system using a single regime model (Sims and Zha, 2006; Kirstin and Robert, 2015). The regime-switching process in some economic issues is usually analyzed using an extended ARIMA model, which integrates Markov 97

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switching into the ARIMA model at constant transition probabilities (Hamilton, 1989; Chauvet, 1998). This model is criticized for its independence of the transition probability on the economic state. To deal with this issue, two other approaches are developed to account for the time-variation of transition probability. The first is univariate or multivariate regression, which estimates the relationship between the transition probability and certain variables of interest (Kim, 2004; Amisano and Fagan, 2013). This approach is particularly useful when all variables are exogenous. However, it fails to account for the feedback effect among endogenous variables (Holm-Hadulla and Hubrich, 2017). However, this can be measured using the second approach, the RS-VAR, because it allows the variables in models to be endogenous (Hubrich et al., 2016). Thus, the RS-VAR is more advantageous in analysing regime-switching process. At present, there is no available literature on the various effects of China's PPP regulation in reducing oil price volatility as the WCOP fluctuates differently.

3.1. Mechanism of market-based oil product price regulation Since China's PPP regulation is mainly focused on gasoline and diesel, gasoline price is used as a surrogate variable of the PPP in the paper. Let Pot represent the price of crude oil at time t, Pgt the price of gasoline at time t, and Pt , a 4 × 1 vector, representing the prices of other products, including diesel, heavy oil, kerosene and naphtha at time t. Assume that 1 unit of crude oil can produce unit of gasoline and unit ( is a vector of 1 × 4) of other products, and the cost of other inputs (including capital and labor inputs) and tax is Ct . Further, assume that the entire time span can be divided into N ( n N ) intervals. Gasoline is priced at its “cost + profit”. Let the profit at time t be t times of the production cost, which means that t is the profit-cost ratio. Then, assuming the cost associated with crude oil is disaggregated and allocated to each product according to their market values, the relationship between gasoline price and crude oil price could be expressed as: Pgt =

3. China's oil pricing mechanism and oil-gasoline price volatility transmission

P ng P ng +

Pn

Pot + R + Ct + t

P ng P gn + Pn

(Pot+R) + Ct = (1 + t

P ng P ng +

Pn

(1)

(Pot+R) + Ct

where represents the mean price of gasoline in the nth period of a regime, R is a unit import tariff rate, and Pn the mean price of other

P ng

China's oil pricing mechanism has undergone three stages since 1949, including the uniform, dual-track, and market-oriented pricing mechanisms (Ju et al., 2017). Before China initiated its market-oriented economic reforms in 1979, the uniform pricing mechanism was applied in a planned economy and the impact of the WCOP shock on China's macro-economy was negligible (Wang, 1995; Du et al., 2010). In 1981, a dual-track pricing mechanism was adopted following China's opening and reform policy initiated in 1978. According to this mechanism, oil companies could sell a certain amount of oil in the international market at an international market price or in the domestic market at a planned price, after meeting the requirement that most of the oil (0.1 billion tons) was supplied to the domestic market at a planned price. Thus, the domestic PPPs were still planned, and the correlation between China's PPP and the WCOP was also low at this stage (Du et al., 2010). In 1998, China abolished the dual-track pricing mechanism, and started a market-based pricing mechanism. As a result, the monthly price of crude oil was set based on the average WCOP of similar quality in the previous month. Later on, the pricing mechanism was revised to highlight that petroleum products were to be priced at the Singapore market prices in the previous month (2000 and 2001), and according to a combination of the average prices of New York, Rotterdam and Singapore futures markets in the previous month (2001and 2006). From 2006 until the present, petroleum products have been priced based on the WCOP, processing cost, tax, and profit. Understanding the transmission mechanism of crude oil shocks to PPPs is of paramount importance for a country's energy policy making (Apergis and Vouzavalis, 2018). The world oil price shocks affect a national economy via the flow of oil as shown in Panel A of Fig. 1. After it is imported to an economy, crude oil can be distilled to produce gasoline, diesel, kerosene, chemical light oil, heavy oil and other products (Cheng and Liang, 2003), which are then distributed to meet the demands of consumers and producers subject to government regulation. Before the macroeconomic stabilization effect of PPP regulation is assessed, it is necessary to understand the regulation mechanism of PPP and the WCOP-PPP volatility transmission mechanism. The PPP is determined by the price of WCOP, other inputs and taxes in the refining process. Because the crude oil can be processed to yield gasoline, diesel, kerosene and other products, the WCOP volatility is disaggregated and then transmitted to the rest of the economy via these petroleum products. That is, because of the disaggregation effect of oil refining, only partial WCOP volatility was transmitted via any product (say, gasoline), which then exerts an effect on the macro-economy as petroleum products are used in production and consumption, shown in Panel B of Fig. 1.

products in the nth period; the term of

Pn g

P ng +

Pn

Pot is the portion of ga-

soline cost attributed to the input of crude oil. The price of petroleum product can be regulated by adjusting the profit-cost ratio, t . When the value of t is equal to the normal industrial profit-cost ratio, indicating that oil price is not regulated, equation (1) represents the normal price of gasoline. When the crude oil pricePot , deviates from the normal level, the price of petroleum product can be regulated by manipulating the value of t . According to “The Management of Oil Pricing Regulation” (2009), when the world market price of crude oil is equal to or below $80 per barrel, petroleum product is priced according to the normal industrial profits, which means that t is the value of the normal industrial profit-cost ratio or the PPP is free of regulation. If the world market price of crude oil is equal to or lower than $40 a barrel, the $40 per barrel is the floor price of crude oil in pricing petroleum product. If the world price of crude oil is higher than $80 per barrel, then the value of t is to be reduced until the profit of crude oil processor becomes zero. When the price of crude oil is equal to or higher than $130 per barrel, the appropriate fiscal policy is to be taken to sustain the production and supply of petroleum product to protect the interests of both producers and consumers. That is, when t is negative, the processors of crude oil are to be subsidized to supply petroleum product to the domestic market. 3.2. The transmission mechanism of oil price volatility Different measures for price volatility are used in the literature, including standard deviation (Ferderer, 1996) and realized volatility measure (Andersen et al., 2003; Guo & Kliesen, 2005). This study uses the change ratio of oil price to present oil price volatility. Its advantage over other measures is that it can be used to study the transmission of oil price volatility. For a long time horizon, the parameter is time varying because the profit of the petrochemical industry may vary on the one hand, and it is affected by the petroleum product price regulation on the other hand. However, the value of beta can be constant for some time intervals. In other words, the value of beta changes by time intervals. The reasons are three-fold. First, petrochemical firms will not continuously conduct financial assessment and the profit-cost ratio is usually calculated for a certain time period, say a quarter, half a year, or a year. Second, the profit-cost ratio itself will not change a lot within some time period. And third, the PPP is regulated over a certain period. This is consistent with the data characteristics of as used in the empirical analysis. 98

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Fig. 1. The flow of oil and the transmission of oil price volatility.

As shown in Panel B of Fig. 1, the WCOP volatility is passed through to PPP volatility. The transmission mechanism is derived as follows. For a time interval that is constant (represented by t ) , the transmitted volatilities can be obtained by differencing equation (1) and letting Ct = 0 (assuming others being constant).

Pgt =

(1 + P ng

+

n t )P g Pn

Pot

identify the number of regimes and their time intervals. Second, the effects of PPP regulation in different regimes are quantified according to the volatility transmission mechanism and results of regime analysis. Third, the effects of price regulation in different regimes on the macroeconomic volatility are assessed using Multi-Regime Regressive Models (MRR).

(2.1)

4.1. RS-VAR model

Dividing equations (2)–(1) on both hand-sides withP ng , we can have:

Pgt P ng

(1 +

=

where

P ng Pgt Pn g

+

n t )P o Pn

and

Pot

Pn o

Pot P no

The Regime-Switching Vector Autoregressive Model (RS-VAR) is characterized with an observable time-series variable, X, for a time period of T, which covers k regime (S), and the different autoregressive equation of X in different regimes (Kim, 2004; Sims and Zha, 2006; Holm-Hadulla and Hubrich, 2017). It is expressed as:

(2.2)

represent the fluctuation rates of gasoline price and

crude oil in the nth time period at t time, respectively. In equations (2)–(2), parameters and are constant at a given technology level, and P ng Pn and P no are means at a given regime and are constant. The volatility of PPP is mainly determined by

t

and

Pot

Pn o

.

X( ) =

Thus, the transmitted volatilities in each time interval, in which t is constant, can be obtained, and its time series data is composed of the estimated values in all time intervals. Thus, equations (2)–(2) represents the WCOP-PPP volatility transmission mechanism. Since (1 + t )P n o P ng + Pn

> 0 always holds, there are synchronized fluctuations between

(1 + t )P n o P ng + Pn

1 0

x 2t

=

2 0

+

n 1 1 x p= 1 p t p n 2 2 x p= 1 p t p

x kt =

k 0

+

n p= 1

+

+

1 t

(S = 1)

+ …… k k p xt p +

2 t

(S = 2)

k t

(S = k )

(3)

In equation (3), X= {x1t , x 2t ,…, x kt } is the vector of X, in which x1t , x 2t ,…, x kt are the values of X in regime 1, 2 … k, respectively. P denotes the lagging orders and n the total orders,

the PPP and the WCOP, ceteris paribus. When the oil price is not subject to regulation, the t value is the normal industrial profit-cost ratio, and the term (1-

x1t =

and =

1 1 k k 0 ,…, p ;…; 0 ,…, p

is a vector of estimated coefficients of the

autoregressive equation, in which

represents the disaggregation effect of oil re-

k p is the coefficient in the kth regime. 1 2 k T k t , t ,…, t ) , is composed of t re-

The vector of residuals, t = ( presenting the residual of the autoregressive equation in the kth regime, which is normally distributed ( t N0, std ) with a mean of zero and a standard deviation, STD. The regime switching was identified using the Markov chain, which is homogeneous. That is, the transition probabilities between regimes were regime-specific and time-specific. The switching probabilities between two regimes are represented by equations (4)–(1), in which pij is the switching probability from the regime i to regime j.

fining. When the WCOP deviates from its normal level, the PPP fluctuation can be regulated by manipulating the value of profit cost rate, t. 4. Methodology The WCOP volatility is a variable with multiple regimes. First, a Regime-Switching Vector Autoregressive Model (RS-VAR) is applied to 99

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Prob(St+ n+ 1 = j|St+n =i) = Prob(St+1 = j|St =i) = pij P=

p11

p k1

p1k

p kk

According to equations (2)–(2), the effect of PPP in reducing price volatility cannot be directly quantified because of the lack of data on t value. However, because the average normal industrial profit-cost ratio is relatively constant and free of the price regulation, the transmission relationship between the volatilities of PPP and WCOP can be presented

(4.1)

(4.2)

by an =

(4.3)

iniPt = [pt1 , pt2 ,…, ptk ] T

k

xt in regimes at time t, with i= 1 p1i = 1, which consists of ptk , the probability in regime k at time t. There exists the following relationship between the probabilities in regimes at time t+1 and t (Stewart, 2008):

(5)

Thus, if P and iniPt are known, the probabilities in different regimes at different time can be figured out according to equation (5), which is

Vct =

(6)

iniP = {iniP1, iniP2 ,…, iniPT}

E( t ) =

log(pti *f( ti))

where f(

T

k

E( t ) = t=1

log(pti *f( it))

Equation (8) is the likelihood function to be estimated, where the n ˆk ˆ k x k at time t in regime k. The Exresidual is kt = x kt 0 p= 1 p t p pectation Maximization Algorithm (EM algorithm) and the Monte-Carlo method can be used to obtain the optimal values of ˆ , and then the P and iniP , and finally the regime amount and the time interval of each regime in X can be found. 4.2. Effect of PPP regulation in reducing volatility

x˜1 =

Before the assessment of the effect of PPP regulation on macroeconomic stability, it was necessary to quantify the effects of PPP regulation in reducing price volatility. The quantification was carried out as follows. Pgt P Let y nt = n , and x tn = not . Using the statistical data of WCOP and Pg

0

(9)

where 0 and aˆ are the estimated coefficient, revealing the observed relationship between the volatilities of PPP and WCOP; µt is the residual. Note that aˆ is different from their latent relationship as re-

presented by the coefficient of

(1 + t )P n o n Pn g+ P

in equation (2).

The total effect of the disaggregation effects of oil refining and PPP regulation was measured as (1 aˆ)x nt , to isolate the effect of PPP regulation from the total effect. 1

f( ) is a normally distributed probability density function， = { 1, 2 ,…,

an)

Pot P no

(10)

abs[xt+1 mean[

x t] n]

;…; x˜m =

abs[xt+m x t+ m 1] mean[ n]

(11)

where [ denotes the value vector at the nth time interval, and abs means the absolute value. Let n = { x1, …, xm} represent the data set for the processed data in the nth time interval. Following the same rules, data at other time interval in different regimes were processed according to equation (11). Since each time interval is specific to a regime, it was necessary to categorize n in terms of the regime and time interval it belongs to. In a given regime, if there is no value for a specific time point, then a value of 0 will be assigned for this time point. Taking regime 1 as an 3, 1, 7 example, if belong to regime 1, then ˜ (1) = { 1, 0, 3, 0,0, 7, …} represents the volatility of X in regime 1, X obtained from the “multi-regime fluctuation” process. Note that 0 is a ˜ (1) contains a total of T observations. Similarly, X ˜ (2) , …, vector, and X ˜ (k) indicate respectively the volatility in regime 2, …, k. X

Po

+ aˆx nt + µt

Pot = (aˆ P no

n]

PPP, the relationship between the volatilities of PPP and WCOP could be estimated according to equation (9).

y nt =

an)]

4.3.1. “Multi-regime volatility” processing “Multi-regime volatility” processing consists of two steps. First, the regime amount and the time interval of each regime were identified according to a selectedbenchmark independent variable. Due to its multi-regime characteristics, the WCOP volatility was chosen as the benchmark independent variable. Second, based on the results of the RS-VAR analysis on the benchmark independent variables, all variables (including the benchmark independent variables) were further processed as follows. According to the results of the RS-VAR analysis, the whole time period (T) was divided into N intervals, where each regime corresponded to at least one interval. Let X represent the raw data set of the processed variables, i.e. X = {x1, x2, …, xT} , and n = {xt, xt+1, …, xt+m} be the values of the processed variable in the nth time interval starting n = X from time t, with m+1 observations and N . To obtain the n= 1 volatility data of the processed variable X, the data was processed according to equation (11).

(8)

t = 1 i= 1

(1

To analyze the impact of oil price regulation on macroeconomic fluctuations in different regimes, both dependent and independent variables were first subjected to “multi-regime volatility” analysis, and then a Multi-Regime Regressive Model (MRR) was estimated.

the normal probability density function. E( ) can be further expressed as:

T

aˆ)

[(1

4.3. “Multi-regime volatility” processing and MRR model

i t) is

E( ) =

is valued as the mean of the normal in-

< 0 holds, equation (10) reveals that the PPP regulation If aˆ can reduce the oil price volatility.

(7)

i= 1

t

an

The matrix iniP can be used to identify the regime of x at different times. For example, according to equations (4)–(3) and equation (6), when pt1 0.5, x is in regime 1 at time t. Furthermore, regime switching occurs at time t and t+1, if iniPt and iniPt+1represent different regimes. However, since P and iniPt are unknown and unobservable, it is necessary to use X to find the optimal values of P and iniPt . The optimal P and iniPt imply an optimal equation (3), in which the residual is near 0 , or f( ) is maximized.1 In equation (3), the equation for zero, each regime can occur at any time t, with a probability as shown in equations (4)–(3). Thus, the expected value E( t ) at any time t is: k

in which

dustrial profit-cost ratio and the parameters , are constant. The value of an can be estimated using the price means of crude oil and gasoline. Under the normal circumstance of no price regulation, the production of petroleum product is a value-added process, and we have an < 1. Thus, (1 an) is a measure of the disaggregation effect of oil refining. Therefore, the effect of PPP regulation in reducing price fluctuation at time t, Vct , was quantified as follows:

Equations (4)–(2) is a regime switching probability matrix k with i= 1 p1i = 1. In equations (4)–(3), iniPt is the probability vector of

iniPt+1 = P*iniPt

(1 + t )P n o , n Pn g+ P

4.3.2. MRR model Before an MRR model was estimated, the data of lnGDP (the

T} .

100

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logarithm of GDP) was subjected to multi-regime volatility processing. ˜ (k) represent the volatility of lnGDP in regime k, and X ˜ (k) be the Let Q observed PPP volatility Pg , or the price regulation effectVc , in the regime.2 Vc(k) and Pg(k) would not be simultaneously used as independent variables in the model. The MRR model is expressed as follows.

˜ (k) = Q

0 (k)

+

˜

1 (k) X (k)

+µ

price data was not converted to a constant price basis. In addition, because monthly data was used, and the Brent oil price and the # 93 gasoline price may vary to different scales, the maximum value in each month was taken as the month value of WCOP and PPP. This is reasonable because this paper targeted the price volatility. While there is no monthly GDP data available from the Chinese Bureau of Statistics, the monthly cumulative growth rate of industrial added value was used to estimate the monthly GDP, considering the close relationship between GDP and industrial added value. As it was recorded in terms of current values, the GDP data was converted to a constant price basis according to the accumulated CPI (the reference is January 1998). In addition, the monthly GDP data was subjected to smoothing treatment so as to remove seasonal impacts. Sourced from S&P Global Platts, the spot prices of petroleum products were used, including gasoline, diesel, kerosene, and chemical light oil. The data of the profit-cost ratio in crude oil processing was derived from China's 1997–2012 input-output table, and was estimated as the ratio of the sum of the net taxes on production and operating surplus over the sum of the direct consumption, depreciation, and wage and compensation. In addition, the data of the share of oil products in total petroleum commodities in calculating an was based on the “2016 Annual Report of Sinopec”. The density data used to convert the measurement units of oil products was obtained from Cheng and Liang (2003). To put in a simpler form, let Po be the WCOP, Pg the PPP, and Q the logarithm of GDP, lnGDP. After the data used in the MRR model was subjected to “multi-regime volatility” processing according to equation ˜ were used to represent the volatilities of the three (11), Po , Pg and Q variables, respectively.

(12)

where 0 (k) and 1(k) are a constant and coefficient to be estimated, respectively. ˜ (k) is the volatility of PPPs in regime k, 1(k) is expected to When X ˜ (k) represents be greater than zero. It is expected to be negative when X the PPP regulation effect, indicating that PPP regulation is expected to reduce macroeconomic volatility. Since the more volatile it is, the less stable an economy will be, a high absolute value of 1(k) represents a strong stabilization effect of PPP. For a given independent variable, the significance of the difference in the estimated coefficients for the two regimes can be checked using the t-statistic. The null hypothesis is that there is no significant difference between the two coefficients.

t

statistic =

1 (k) std[ 1 (k)

1 (k+1) 1 (k+ 1)]

(13)

In equation (13), 1(k) is the estimated coefficient of regime k, that of regime k+1, and std[ 1(k) 1 (k+1)] is the standard deviation of [ 1 (k) 1 (k+1)] can be easily 1 (k+ 1)]. The std[ 1 (k) obtained since the standard deviation of 1(k) and 1(k+ 1) can be obtained from the estimation of equation (12) with an observation of T (Stock & Watson, 2011). If the t statistic is greater than the critical value (5% significance level), there is a significant difference between the two coefficients; otherwise, the difference is not statistically significant. If the null hypothesis is rejected, the economic explanations of ˜ [ 1(k) 1 (k+ 1)] are in two aspects. First, when X is the volatility of PPP, it means that, in different regimes, the volatility of PPP has a significantly different marginal contribution to the GDP volatility. ˜ represents the PPP regulation effect, it reveals that PPP Second, when X regulation has significantly different stabilization effects on the macroeconomy in different regimes of oil price fluctuation. 1 (k+ 1)

5.1.2. Test of stationarity Stationarity is a prerequisite in conducting time series data analysis. The stationarities of the three variables, Po , Pg and Q, and their lagging order variables were checked using the DF-GLS (Dickey Fuller – Generalized Least Squares) test. According to Elliott et al. (1996), the DF-GLS test is approximately most powerful for the unit root testing problem although there is no uniformly most powerful test. In the DFGLS test, the null hypothesis is that there exists a unit root for a time series, and the alternative default hypothesis is that the time series data is stationary about a linear time trend. In the paper, the lagging orders in DF-GLS test are set by the default in the package of “urca” in R. The results (Table 1) show that the first order differences of Po , Pg and Q are stationary, i.e., they are all I(1)variables, and the three variables were all integrated of order zero after the “multi-regime volatility” processing in the two regimes. Therefore, the probability of false regression is very low in estimating the MMR model.

5. Data and results 5.1. Data and its processing 5.1.1. Data sources The WCOP data were from the U.S. Energy Information Administration and the Brent crude oil price data was used (unit: dollar/barrel). The reason is that China's crude oil is imported mainly from Middle East countries. Although Dubai crude oil is used as a benchmark price, 50% of the world oil trade volume was priced based on Brent. Further, the Dubai crude oil pricing is associated with Brent as well because the Brent crude oil market is the core market in the world (Wei and Lin, 2007; Zhao and Cao, 2014). The price data of petroleum product was from the China Development and Reform Commission (NDRC), and the #93 gasoline price (unit: RMB/liter) was used in the study because gasoline and diesel are the two major oil products in China, and their price fluctuations follow similar patterns (Zhang and Xie (2016). China's macroeconomic data was obtained from the China Bureau of Statistics, including GDP (quarterly data), CPI (monthly data) and industrial added value cumulative growth rate (monthly data). The data of the #93 gasoline price was from January 1998 to April 2017. Because this paper aims to study the volatility of oil prices, the oil

5.2. Empirical results The empirical analysis is composed of three steps, including the identification of the number of regimes and their time intervals, the quantification of the effect of PPP regulation in reducing oil price volatility, and the estimation of the stabilization effect of oil price regulation on the macroeconomy. 5.2.1. Identification of the amount of regimes and their time intervals The regime amount of crude oil price is unknown, although literature shows that there are at least two regimes in the world crude oil market (Holm-Hadulla and Hubrich, 2017). However, there are usually less than 4 regimes. In simulation analysis, 2–4 regimes were assumed. In addition, the lagging orders in the RS-VAR model were set to be 2–6, determined according to the significance of estimated coefficients in simulation analysis. While the Markov transfer probability matrix and parameters of RSVAR model are unknown, much computation is required when a conventional algorithm is applied. Thus, the Expectation Maximization

2 Multi-regime processing was applied to obtain aˆ value in equation (9), and thus is not required for Vc .

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“violent-fluctuation" one. The Markov transition probability (Table 3) shows that the higher the volatility is, the more unstable the regime is, and the greater the probability of transformation occurrence. The results show that when a price is currently in regime 1, it has a probability of 0.8550 in regime 1 and a probability of 0.1450 switching to regime 2 in the next time interval. However, if a price is currently in regime 2, it has a probability of 0.6457 staying in the same regime and a probability of 0.3543 switching to regime 1 in the next time interval. As the data reveals, regime 1 is more stable, with longer time intervals.

Table 1 Results of stationarity test. Variables

Differencing times

Lagging order

Test value

1% Critical value

Result

Po

Po(1)

0 1 0 1 0 1 0

4 4 4 4 4 4 4

−1.0211 −6.2678 0.2035 −7.0101 −1.1833 −4.1589 −6.2368

−2.5700 −2.5700 −2.5700 −2.5700 −2.5700 −2.5700 −2.5700

Accept Reject Accept Reject Accept Reject Reject

Pg(1) ˜ (1) Q

0

4

−6.5958

−2.5700

Reject

0

4

−5.9146

−2.5700

Reject

Pg Q

Po(2)

0

Pg(2) ˜ (2) Q

0

4 4

−6.8488 −5.5131

−2.5700 −2.5700

Reject

0

4

−6.9795

−2.5700

Reject

5.2.2. Quantification of the effect of PPP regulation in reducing oil price volatility Three steps were involved in quantifying the effect of PPP regulation in reducing price fluctuation. First, equation (9) was used to measure the total reduced price volatility, which includes the disagregration effect of oil refining and the effect of price regulation. Second, the marginal disagregration effect was estimated. Third, the effect of oil price regulation in reducing price volatility was estimated using equation (10). According to equation (9), aˆ (1) was obtained by regressing Pg1 over Po(1) in regime 1, and similarly aˆ (2) was obtained by regressing Pg2 over Po(2) in regime 2. aˆ (1) and aˆ (2) represent the relationships between the PPP volatility and that of WCOP in the two regimes. As shown in Table 4, aˆ (2)is greater than aˆ (1) , and the difference is statistically significant. Accordingly, (1-aˆ )is smaller in regime 2, meaning that the sum of disagregation and price regulation effect is higher in regime 1 and than in regime 2.

Reject

Algorithm (EM) under MSwM package in R was applied due to its advantage over conventional algorithm. First, it could be used to estimate the parameters of RS-VAR model using OLS method, and randomly generate a Markov transition probability matrix. Second, using the iterative algorithm, it enables the maximization and the convergence of the density probability of the total residual, resulting in a set of optimal parameter values. However, the major disadvantage of the EM algorithm is that the Markov transition probability matrix is randomly generated, and the final convergence value of the parameters will change as the random seeds change. The Monte-Carlo method was used to overcome this disadvantage. For each lagging order, 100 random experiments were conducted to obtain the optimal estimation of RSVAR model, using the following selection criteria: (1) a maximum amount of significant coefficients in the estimated RS-VAR model; and (2) a consistency of each regime's time interval with the actual data volatility, and the distinct boundaries between regimes. According to the method mentioned above, there are 3 possible regimes and 5 alternative lagging orders, yielding 15 combinations. One hundred Monte-Carlo experiments were then conducted for each combination, with a total of 1500 experiments simulated. Based on the selection criteria mentioned above, two regimes were identified and the lagging order was 3. As shown in Fig. 2, the whole time period is divided into several intervals, and the length of each interval (represented by rectangular shadow) is different. The upper panel shows all of the time intervals of regime 1, while the lower presents all of the time intervals of regime 2. For example, the WCOP was in regime 1 for 41 months from April 1998 to August 2001, and then entered regime 2 in August 2001 and lasted for 1 month before the next switching. The real polygonal line represents a combination of the difference of Po in two near months. The fluctuation amplitude of the polygonal line is small in regime 1 but much bigger in regime 2. The empirical results (Table 2) show that the differences between the two regimes were significant. In the two regime-based models, the estimated coefficients were significant at 1% level forPo (-1) and insignificant for Po (-2) and Po (-3). That is, only the lagging order 1of Po could effectively predict the current value of Po . According to the estimated results of regime 1, the estimated coefficient of Po (-1) is −0.0878. As Po measures price difference of any two continuous periods, a negative coefficient of Po (-1) means that the current value would be smaller when the value in the previous period is large. In other words, there is an endogenous effect, which inhibits the price to hike in regime 1. As shown in Fig. 2, fluctuations are relatively mild in regime 1 but violent in regime 2. The estimated coefficient of Po (-1) in regime 2 is 0.6931, revealing that the current value tends to become larger. That is, there is an endogenous effect in regime 2, which makes the price fluctuate to a great scale. Therefore, regime 1 is a " mild-fluctuation" one, while regime 2 is a

Based on an =

(1 + t )P n o , n Pn g+ P

the marginal disaggregation effect of oil

refining can be quantified in terms of (1-an) . The results reveal that, as shown in Table 4, the disaggregation effects in the two regimes are not statistically significantly different. Since the disaggregation effect is mainly determined by the technology and process of oil refining, this finding is consistent with the fact that the process and technology of oil refining do not vary as regime switches. Finally, the effect of price regulation in reducing oil price volatility was quantified according to equation (10). The results show that the effects are negative in two regimes, revealing that price regulation can reduce the volatility of oil prices. Furthermore, the effect is stronger in regime, and the difference of the effects in the two regimes is statistically significant. 5.2.3. The effect of oil price regulation in stabilizing the macroeconomy (1) The effect of PPP volatility on macroeconomic volatility The relationship between oil volatility and macroeconomic volatility was analyzed by regressing equation (12) using ordinary least squares method. The results are shown by model 1 and model 2 in Table 5. In both regimes, the coefficients of Pg are greater than 1, denoting that the transmission from the PPP fluctuation to the macroeconomy has an “enlargement effect”. That is, the PPP volatility tends to accelerate the fluctuation of the macroeconomy. However, this coefficient is larger in regime 2, which means that this enlargement effect is stronger in regime 2, representing a stronger effect of the PPP volatility on the macroeconomy. In other words, the higher the PPP volatility is, the stronger is its effect on the macroeconomic stability. Moreover, as revealed by the difference of 1(k) between the two regimes, the marginal effect of the PPP volatility on the macroeconomic volatility is statistically significant. (2) The effect of PPP regulation in reducing macroeconomic volatility

˜ as explanatory variables and explained variables, Using Vc and Q respectively, the stabilization effect of price regulation on 102

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Fig. 2. WCOP Regimes and their time intervals. Table 2 Estimated results of RS-VAR model. Variable

Po (-1) Po (-2) Po (-3) Constant R-square Observation Loglik

Table 4 Descriptive statistics of parameters and variables.

Regime 1

Regime 2

Coefficient (std)

T value (P value)

Coefficient (std)

T value (P value)

−0.0878*** (0.0272) −0.0510 (0.0829) −0.0510 (0.0576) 0.9656*** (0.2156) 0.0284 229 −663.1121

−3.2279 (0.0012) −0.6152 (0.5384) −0.2344 (0.8147) 4.4787 (0.0001)

0.6931*** (0.1447) 0.0360 (0.1305) 0.0057 (0.1063) −1.4556 (0.9741) 0.3837 229

4.7899 (0.0001) 0.2759 (0.7826) 0.0536 (0.9573) −1.4943 (0.1351)

Parameter /variable

Coefficient /mean

Regime 1

Regime 2

Difference

aˆ

Coefficient (STD) Mean (STD) Mean (STD)

0.2314*** (0.0866) 0.8253 (0.0686) −0.7077 (0.0256)

0.3430** (0.1539) 0.8447 (0.0411) −0.5930 (0.0206)0

−0.1116** (0.0498) −0.0194 (0.0196) −0.1246*** (0.0080)_

an Vc

Note:(1)***,** and * represent significance at 1%,5% and 10% levels.(2)the significance of the difference was tested using equation (13).(3) an and Vc are not regressors and thus there is no statistical significance. Table 5 Estimated results of MRR model.

Note:(1) ***,** and *represent significance at 1%,5% and 10% level。(2) Po (-1) ∼ Po (-3) are the variables Po with lagging order 1–3; dependent variable: Po .

Item

Table 3 Markov transition probability matrix.

Pg

Regime

Regime 1

Regime 2

Regime 1 Regime 2

0.8550 0.1450

0.3543 0.6457

Vc

(STD)

(STD) Constant (STD) F-Test (P value) R-square Adjust R-square Obvervation Difference in 1(k) T statistic (P value)

macroeconomy was estimated according to equation (12). Results are as shown in Table 5, in which model 3 and model 4 represent estimated results for regime 1 and regime 2, respectively. In the two models, the estimated coefficients of Vc are negative, with p-values less than 1%, which indicate that oil price regulation can contribute to macroeconomic stability in both regimes. Furthermore, the estimated coefficients also reveal that the price regulation in regime 1 has a greater effect in reducing macroeconomic volatility than in regime 2. In other words, the greater the PPP volatility is, the lesser is the stabilization effect of PPP regulation. The difference of 1(k) for the

Model 1

Model 2

Model 3

Model 4

Regime 1

Regime 2

Regime 1

Regime 2

1.2649* (0.6921)

2.8344*** (0.3429) −3.0183*** (0.6135) 0.1318*** (0.0189) 24.21 (0.0000) 0.0975 0.0935

−2.4529*** (0.1847) 0.0060* (0.0035) 176.4 (0.0000) 0.4373 0.4348

224 −0.5653*** −3.3669 (0.0000)

227

0.1804*** (0.0169) 3.34 (0.0689) 0.0147 0.0103

0.0143*** (0.0039) 68.30 (0.0000) 0.2313 0.2279

224 −1.5695*** −8.1015 (0.0001)

229

Nofte: ***,** and * represent significance at 1%,5% and 10% level, respectively.

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two regimes is −0.5653 with a p-value of 0.0000, showing that the differences in the stabilization effects of oil price regulation are statistically significant in the two regimes. In summary, the disaggregation effects of crude oil refining are not statistically different in the two regimes, but PPP regulation has different effects in reducing macroeconomic volatility. That is, PPP regulation is more effective in reducing price volatility in regime 1 than in regime 2. As a consequence, the macroeconomic stabilization effect of PPP regulation is stonger in regime than in regime 2. Therefore, when the WCOP volatility is strong, it is necessary to strengthen the PPP regulation to maintain macroeconomic stability.

volatility. Further studies are recommended to investigate the effect of PPP regulation on social welfare, especially when WCOP is in different regimes, so as to provide more information for PPP regulation reform.

6. Conclusion and policy implications

References

China started to take measures to regulate the PPP since the 1990s, but the effects of price regulation on macroeconomic stability have not been studied yet. To assess these effects, this paper first analyzed the mechanisms of PPP regulation in the case of gasoline and the transmission mechanism of WCOP-PPP volatility, quantified the effect of PPP regulation in reducing oil price volatility, and estimated the stabilization effect of PPP regulation on China's macroeconomy in different price regimes. An empirical study was carried out using the Regime-Switching Vector Autoregressive Model (RS-VAR) and the Multi-Regime Regressive Model (MRR). It was found that the world crude oil fluctuates in different regimes (the mild-fluctuation regime and the violentfluctuation regime), and the PPP regulation can reduce oil price volatility and can contribute to reducing macroeconomic volatility but is more effective in the mild-fluctuation regime. In conclusion, the PPP regulation is effective in stabilizing the macroeconomy in China but its effects are different when oil price is in different regimes. The above results have the following policy implications. First, as an important measure to deal with the shock of WCOP fluctuations, PPP regulation plays a significant role in reducing the volatility of (or in stabilizing) China's macroeconomy. Petroleum resources are becoming increasingly scarce and the world oil market is frequently subject to shocks resulting from political and economic events, such as regional conflicts and wars. From this viewpoint, the PPP regulation should be sustained to maintain China's macroeconomic stabilization. Second, the current pricing regulation of petroleum product focuses on the control of price rather than price volatility. According to the Chinese government document, “Measures for oil pricing regulation” (2009),the PPP regulation fails to respond to changes in WCOPs immediately because of the relatively long price adjustment cycle, i.e., 22 days between 2009 and 2013, and ten days since 2013. Therefore, to maintain macroeconomic stability, the PPP volatility should be a concern in the PPP regulation approach. For example, the volatility can be lowered by shortening the price adjustment cycle. Moreover, the difference of regime-specific PPP regulation effect should be considered in the process of China's oil pricing regulation reform. Since the regime switching of WCOP could not be observed directly, it is necessary to design tools that can effectively identify different regimes according to their volatility characteristics. Specifically, the tipping point as the WCOP switches from a mild-fluctuation regime to a violent-fluctuation one should be identified. This information is important for the state authority in taking timely responsive measures to deal with severe shocks from the world oil market, and to minimize its negative impact on the macroeconomy. The limitation of the paper is that it focuses on the effect of PPP regulation in reducing macroeconomic volatility. According to economic theories, government intervention is expected to distort the prices of products, resulting in a social welfare loss. However, empirical study literature shows that the economic growth rate is higher as the macroeconomic volatility is low, other factors being constant. That is, there is a trade-off to be made between price distortion and price

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