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HISTORICAL FALL IN CRUDE OIL PRICES AND VOLATILITY SPILL OVER TO SELECT ASIAN STOCK MARKETS

Author

Abstract

Oil industry recently faced the deepest downturn. This plunging price was first observed in June 2014 and it moved further down. This change affected the investment pattern of many companies and leads to a decline in corporate margins and influence investments in stock markets. Keeping in view this important relationship, here, we propose to study the movement of crude oil prices and volatility spill over to select major stock markets of Asia.

We have taken daily data for the period from June 01, 2014 to August 31, 2016 for the major Asian countries (China, Malaysia, Indonesia, South Korea, Singapore, Japan and India). One major stock index of each country has been taken to represent its stock market. We used GARCH (1,1) model to forecast volatility. Beside, doing descriptive statistics and correlation test, we put granger causality test to identify spill over of volatility.

Preliminary results suggested that apart from different degrees of correlations, return spill overs between India and its Asian counterparts are found to be significant and bi-directional. We found that there are some markets from where there is significant flow of volatility. Affect of historic crude price movement on stock markets is also significant.

Key Words: crude oil, stock market, GARCH model, causality, correlation

HISTORICAL FALL IN CRUDE OIL PRICES AND VOLATILITY SPILL OVER TO SELECT ASIAN STOCK MARKETS

1. Introduction

Oil is the fuel that forces world economies. The sharp increase in the price of oil and other energy products were the most severe supply shocks hitting the world economies since World War II. An oil shock may have a different impact on each of the countries due to various factors such as their relative position as oil importers or exporters, different tax structures etc.

In context to Asian countries, changes in oil prices are one of the most important factors which impact the overall inflation of the countries. The major producers of oil are Saudi Arabia, United States, Russia, China, Canada, Iran, UAE, Iran whereas the major consumers are United States, China, Japan, India, South Korea, Germany, Italy, France, Netherland and Singapore. This mismatch between the producers and consumers drives international trade in oil. Due to the rising oil demand in countries like China and India, and production cuts by OPEC countries, the price of oil rose significantly from 1999 to mid 2008 from $25 to $150 a barrel. In July 2008, it reached its peak of US $147.27 a barrel.

2. Conceptual Framework

The financial crises of 2007-2008 affected the oil price and underwent a significant decrease after July 11, 2008. On December 23,2008, it dropped below $30.28 per barrel which is lowest since financial crises. During the economic recovery, for about three and a half year the price remained from $90 to $120 a barrel. In mid of 2014, from a peak of $115 per barrel in June 2014 oil price started declining due to a significant increase in oil production in USA, and declining demand in other countries. By February 3, 2016 the price of oil was below $30 a barrel which is almost a drop 75% since mid-2014. This change affected the world economies to great extent. Many countries faced with the problem of unemployment. In USA 250,000 oil workers- roughly half of them lost their jobs. This change was also observed in stock market. The earnings are down for companies that made record profits in recent years whereas many companies have gone bankrupt. Thus it affected the investment in stock markets. This study is in the continuation of research based on the issue of fall in oil prices and its impact on stock market returns.

Review of Literature

4. Research Methodology

Research Methodology is presented as under:

4.1 Objectives

Objectives are put as follows:

1. To ascertain the correlation among oil price and other indices
2. To assess the direction of causality between oil price and other indices
3. To forecast volatility oil price and other indices

4.2 Data

We have taken daily data for the period from June 01, 2014 to August 31, 2016 for the major Asian countries (China, Malaysia, Indonesia, South Korea, Singapore, Japan and India). One major stock index of each country has been taken to represent its stock market i.e. for China(SSE COMPOSIE), Malaysia(FTSE ),Indonesia (JKSE), South Korea(KOSPI), Singapore(STI index), Japan(NIKKI 225) and India (S&P BSE). This data were taken from Yahoo Finance. We have also taken historical crude oil prices from Investing.com.

4.3 Tools

We used GARCH (1,1) model to forecast volatility and to develop residual series. Beside, doing descriptive statistics and correlation test, we put granger causality test to identify spill over of volatility.

4.4 Hypotheses

In order to meet the objectives following Null Hypotheses are proposed:

H₀₁: There is no correlation among oil price and other indices

H₀₂: There is no causality between price and other indices.

H₀₃: There is no volatility persistence in oil price and other indices

5.Analysis

Analysis is presented as under:

Descriptive Statistics

With the help of descriptive statistics we are describing the various features of the oil price and other indices. Here, we have taken indices of China(SSE COMPOSIE), Malaysia(FTSE ),Indonesia(JKSE), South Korea(KOSPI), Singapore(STI index), Japan(NIKKI 225) and India (S&P BSE). It helps in summarizing a sample’s detail. Following table shows the result of descriptive statistics of the variables.

Table 01 About here

Descriptive Statistics means describing the data in quantitative terms. It summaries about the sample and the observation we have made. Here there are 4440 observations (555*8) of China, India, Indonesia, Japan, Malaysia, Singapore, South Korea and crude oil prices. FTSE is least volatile as compared to other indices as the standard deviation is least with .639 per cent and crude oil price is considered to be highest volatile as its standard deviation is 2.979 per cent. As Skewness measures the asymmetry of the probability distribution of variables. Here all variables are negatively skewed. Jarque- bera test is used to check the normality of the distribution. Hypothesis of normality is rejected here, in all the cases.

Correlation Test

In statistical terms, correlation measures how two variables move in relation with each other. Table 3 provides summary of the correlation among China(SSE COMPOSIE),Malaysia(FTSE ),Indonesia(JKSE), South Korea(KOSPI), Singapore(STI index), Japan(NIKKI 225) and India (S&P BSE).

Table 02 About here

Correlation is a statistical tool which measures the fluctuations between two or more variables. The value of correlation can be positive or negative. There is a positive correlation when an increase in one variable, increases the other variable. Here, values of correlation are ranging from -0.0921 to 1 which means they are negatively and positively correlated with each other.

GARCH Model

Past variances are considered to explain the future variances under this model. The result of GARCH model reflected by mean and variance equation are presented in Table 3

Table 03 About here

In the table 3 Alpha (α) indicates the ARCH affect and Beta (β) indicates the GARCH affect.

In all cases ie oil prices and other indices, the value of probability of GARCH coefficient (β) is 0.000, which is less than the critical value 0.05. Thus GARCH is significant for oil prices and other indices which mean that past deviation in values can affect the values in future.

Granger Causality Test

This test involves examining whether lagged values of one series have significant explanatory power for another series. They have null hypotheses of no granger causality. The results of this test are summarized in Table 4, and it indicates whether there exists significant Granger Causality and if it exists, then in which direction such causality exists between oil returns and stock returns

Table 04 About here

The results of tables 4 indicates that null hypothesis is rejected for oil and other indices as all indices and oil does not Granger Cause each other, that is even short-term causality does not exist between oil and index series.

6. Conclusion

This study is in the continuation of research based on the issue of fall in oil prices and its impact on stock market returns. For depicting the issue of interrelation and interdependency between the indices, we used Descriptive Statistics, Correlation Analysis. We used GARCH (1,1) model to forecast volatility and to develop residual series. We put granger causality test to identify spill over of volatility.

The key findings of the study are –

FTSE is least volatile as compared to other variables as the standard deviation is least with .639 per cent and crude oil price is considered to be highest volatile as its standard deviation is 2.979 per cent. As values of correlation are ranging from -0.0921 to 1 which means they are negatively and positively correlated with each other. GARCH is significant for oil prices and other indices which mean that past deviation in values can affect the values in future. The results of granger causality

This study is helpful to all individual/ institutional investors, portfolio managers, corporate executives, policy makers and practitioners may draw meaningful conclusions from the findings of this study while operating in stock markets. Our research may help stakeholders in management of their existing portfolios as their portfolio management strategies may be, up to some extent, dependent upon such research work.

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Other Sources

• http://www.indexmundi.com/g/r.aspx?v=93&t=10
• http://www.nytimes.com/interactive/2016/business/energy-environment/oil-prices.html?_r=0.
• https://in.finance.yahoo.com/
• http://www.investing.com/
• http://www.investopedia.com/ask/answers/030315/why-did-oil-prices-drop-so-much-2014.asp
• https://www.weforum.org/agenda/2016/03/what-s-behind-the-drop-in-oil-prices/

TABLE 01

Descriptive Statistics

TABLE 02

CORRELATION

TABLE 03

GARCH MODEL

TABLE 4

Granger Causality Test