Domestic Volatility Transmission on Jakarta Stock Exchange: Evidence on Finance Sector

Transcription

1 Domestic Volatility Transmission on Jakarta Stock Exchange: Evidence on Finance Sector Nanda Putra Eriawan & Heriyaldi Undergraduate Program of Economics Padjadjaran University Abstract The volatility is very important to investigate because volatility is source of information that many investors considered. We used all of sectors on Jakarta Stock Exchange in order to convince the volatility transmission of every sector, becauses it is influential for investors to adjust risk within organizing their portfolio. This studies empirically measured volatility transmission across domestic stock market by using GARCH and VAR model. The study used daily data through ten sector indices such as finance sector, mining sector, property sector, agricultural sector, basic industry sector, miscellaneous industry sector, consumer sector, trade sector, manufacture sector and infrastructure sector from 2009 to Modeling volatility was measured by GARCH model to investigate volatility transmission through VAR system. The results showed there was volatility spillover from finance sector to all sectors on Jakarta Stock Exchange. This is occurs because of finance sector is the resource of fund as a main function to enhancing economic activity. Keywords: GARCH, Vector auto regression, volatility transmission, stock market volatility, Jakarta Stock Exchange JEL Code: G11, G32

2 Introduction Portfolio management is a key success to get value of return while considering the risk. Nowadays knowledge of volatility transmission between assets and international financial market are important in order to maintain portfolio decisions. Volatility means source of information that often reflected into market price. Investors main objective is to minimize risk exposure while keep their expected return. According to (Fleming et al. 1998) studies, as a portfolio manager considers the correlation between asset returns, it will lead to take position as a hedge his speculative position. It is very important to considering volatility linkages in domestic financial market. Furthermore, examining from finance sector is important because as a large market capitalization proportion on Jakarta Stock Exchange and banking system is a main function to enhancing economic activity. Several empirical studies have considered volatility transmission across international financial markets (Clements et al. 2013); (Gallali & Kilani 2010); (Huang & Yang 2002); (Worthington & Higgs 2004). While other studies have also focused on volatility transmission across markets in the same economy (see for example, (Gencer 2014); (Kearney 2000); (Duncan & Kabundi 2013). There is only one research explained volatility transmission across domestic market (Ali 2014). All of majority studies were conducted mainly transmission on international financial market and one studies about domestic volatility transmission. The evidence on domestic volatility transmission is not available on Indonesia cases. The main objective of this study is to divergence the findings already convince on international financial market transmission in order to measure volatility transmission on Jakarta Stock Exchange. This study is very important to hedge against risk. According to provide volatility transmission, a shock in finance sector could have effect on another sector. For example, if finance sector has been shocked what is the impact transmission other sector. Thus, domestic volatility transmission will lead to risk sharing, maintain portfolio selection and hedging for minimize risk. Finally, on section 2, we present brief literature reviews which contains of studies volatility transmission. Section 3 describes methodology and data for analysis. Section 4 presents empirical results of this study, and section 5 final remarks. 1. Brief Literature Review While a considerable of empirical research have investigated volatility transmission across international markets, few studies have examined volatility transmission across markets in the same economy capacity, and fewer studies have investigated domestic volatility transmission. One of recent studies of volatility transmission across international market is (Gallali & Kilani 2010), which analyze stock market s volatility and international diversification. The study focused on the seven developed (G7) countries. The result indicates there is a significant effect of individual volatilities and correlations between the US and the other markets and that volatility of these markets has an impact on increase in correlations between the G7 countries. Their result also show that own stock market spillovers in this case US market were generally higher than cross volatility spillovers for international financial market. as well as (Clements et al. 2013) stated that volatility and news spillovers are

3 found to occur on the same trading day between Japan, Europe, and the United States. All markets exhibit significant degrees of asymmetry in terms of the transmission of volatility associated with good and bad news. There are also strong links between diffusive volatilities in all three markets, whereas jump activity is only important within the equity markets. Similarly, as further research by (Sakthivel et al. 2012) that the arrival of volatility transmission by US and Japan stock markets then transmitted to other Asian and European stock market. The results showed bidirectional volatility spillover between US market and Indian market and unidirectional volatility spillover from Japan and United Kingdom to Indian market Furthermore, International market were linkages on each other market because of the economies are strongly integrated through international trade and investment. Several studies showed there is strong evidence on volatility transmission from three stock markets are significantly interdependent: Tokyo leads London and New York; London leads New York and Tokyo; and New York leads Tokyo and London. In particular, the tie between London and New York is the strongest (Gallali & Kilani 2010). Other studies of volatility transmission a cross countries include (Worthington & Higgs 2004). On the other side, those are contrast by examining volatility transmission on Islamic emerging equity markets. The estimation results of the three models show that the US and Islamic emerging equity markets are weakly correlated over time. No sheer evidence supports that the US market spills over into the Islamic emerging equity markets (Majdoub & Mansour 2014). Despite of examining volatility transmission across different countries, previous studies also examining volatility linkages between stock market and future commodity prices. (Almohaimeed & Harrathi 2013) investigated the volatility transmission effect and conditional correlations among crude oil, stock market and sector stock indexes in Saudi Arabia. The results showed the presence of volatility transmission between stock market and sector stock market returns. Furthermore, the research convinced oil prices changes affect Saudi stock market. This contrasts with (Kang & Yoon 2013) they found no significant influence of oil futures price returns on Asian stock returns. However, strong volatility spillover was observed from oil futures price shocks and volatility to counterpart volatilities. As (Gencer 2014) investigate volatility transmission between oil price and equity returns in Europe and the United States at the sector level, and find significant evidence of return and volatility spillovers. As well as (Malik & Ewing 2009) find evidence of significant transmission of shocks and volatility between oil prices and some of the examined US market sectors. Similarly, as further feature on dynamic conditional correlation (DCC) GARCH methodology, they show that the correlations between commodity and stock markets evolve through time and are highly volatile, particularly since the financial crises. The latest has played a key role, emphasizing the links between commodity and stock markets, and underlining the financialization of commodity markets (Creti et al. 2013). Some empirical literate exploring volatility linkages in financial markets across different countries, researchers have also studied the volatility linkages between domestic markets and, even sectors of an economy. Our results confirm a bidirectional shock and volatility transmission between gold prices and Turkish stock market, whereas we document a unidirectional transmission from gold asset to

4 Turkish government bonds (Gencer 2014). (Fu et al. 2011) found that news shocks in the Japanese currency market account for volatility transmission in eight of the ten industrial sectors considered. Evidence was also found of significant asymmetric effects in five of these industries. As well as (Kearney 2000) investigated of volatility transmission from interest rate, inflation, exchange rate among Europe country from He proved world equity market volatility is caused mostly by volatility in Japanese/US markets and transmitted to European markets, and second, changes in the volatility of inflation are associated with changes of the opposite sign in stock market volatility in all markets where a significant effect is found to exist. To the extent that the volatility of inflation is positively related to its level, this implies that low inflation tends to be associated with high stock market volatility. Other studies of volatility transmission on domestic economy include (Duncan & Kabundi 2013). The few literature review examining domestic volatility transmission, (Ali 2014) convince that the results indicate existence of unidirectional shock and volatility transmission from the banking sector to the consumer goods sector and the Shari ah compliant equities sector, and bidirectional shock and volatility transmission between the consumer goods and the Shari ah compliant equities sectors of the NSE. 3. Data and Methodology 3.1 Data The study used all index sectors in Jakarta Stock Exchange that consist of daily data JSX finance index to capture finance sector includes insurance company and other financial institution. The JSX consumer index to provide consumer goods sector consist of pharmaceuticals, tobacco manufactures, cosmetic households and Housewares Company. The Basic industries index captures cement, chemical and the others. The mining sector tracks the performance coal, Oil Company in Jakarta Stock Exchange. The property index provides property and construction company. The infrastructure index captures transportation, Telecommunication Company. The Trade, Services and Investment Index contains all listed companies that are engaged in Indonesia's Trade, Services and Investment sector. The Agriculture Index consists of Agriculture Company that listed in Jakarta Stock Exchange. The miscellaneous index captures all listed companies that are engaged in Jakarta Stock Exchange. We collected the daily data from Indonesia stock exchange. We used period ranges from 05 January 2009 to 31 December 2014, totaling 1466 observations for each index. This daily data and time period was chosen because to examine sensitivity of volatility transmission. All the indexes which we used should have converted into daily returns: = 100 ( ) Where return of daily index is, is differences, is price of index at time t and is price of index at previous day. 3.2 Methodology The study used the Augmented Dickey Fuller to prove that data has stationer and employs GARCH (1,1) model to examine forecasting volatility for each sectors. The GARCH ( 1,1) models explain variance of each model has homoskedasticty because financial time series has volatility clustering. It means that periods of high

5 variance tend to group up together. The study finally used VAR model to establish the volatility transmission between finance sector and other sectors. The objective of this study is to examine volatility transmission across domestic stock market on Jakarta Stock Exchange. The volatility is very important to be concern because volatility is a risk parameter for fund manager to adjusting their portfolio. This research is using two step procedures. The first step we use GARCH (1,1) to estimation residual and the second step we use VAR to see significant interaction on volatility transmission. Furthermore, although the study focuses on second model rather than first model, the residual from GARCH must be included in the VAR model. To examine volatility model on each sectors we use GARCH (1,1) to get residual. GARCH (1, 1) model can be written as follows: = ( ) +...(1) = + + h.(2) Where (1) is return of the indices and ( ) as a function of independent variable on mean equation. Variance equation (2) can be defined by is long run average variance, error terms and is past conditional variance. GARCH(1,1) model solves for the conditional variance that consist of previous variance, previous squared return, and long-run variance. So the variance from that model has homoskedasticity. Volatility transmission between sectors can be prove by employs vector auto regression (VAR) system. We can see significant of finance sector that influence other sector.,, = +,,,,,, +,, Where, is volatility from finance sector and, is volatiity from others sector. We regress partially because we want to know how finance sector volatility can influence other sector indices. The volatility spillover can be capture by the parameter, and,.. A significant interaction is evidence that volatility transmission occurs. 4. Empirical Results The summary of statistics for all indices we can see on table 1. The daily mean returns are positive for all indices. The standard deviation of miscellaneous industry index is higher comparing with other indices. It means there is higher volatility and the most risky rather than others. The measures for skewness captures that the distributions of returns for all indices is positively skewed. It indicates all of data is not distribution normally.

6 Index Mean Maximum Minimum Std.Dev. Skewness Finance Consumer Property Infrastructure Agriculture Trade Mining Manufacture Basic Industry Miscellaneous Industry Table 2 present the results of Augmented Dickey Fuller test show that all sector indices are non-stationary at level since null hypothesis is accepted in all cases. Furthermore, all stock prices are stationary in first difference. Index Augmented Dickey-Fuller Test Levels P.Value First Differences P.Value Finance *** Consumer *** Property *** Infrastructure *** Agriculture *** Trade *** Mining *** Manufacture *** Basic Industry *** Miscellaneous Industry *** Note : *** indicates 1% level of significant The volatility model we convince on table 3. We used GARCH (1,1) in order to estimated volatility for each sector indices. All of sectors have fulfilled the parameters requirement. The index that we convince has daily parameter sum of and less than 1. It indicates the predictions of volatility aren t explosive and all of sectors have 1% statistically significant level on each parameter. These results shows only finance sector ( ), infrastructure sector ( ), agriculture sector ( ) and mining sector ( ) were has persistent volatility (influence by past volatility) which has coefficient greater than 0.85.

7 Index GARCH (1,1) Finance 6.53e-06*** *** *** Consumer 8.83e-06*** *** *** Property 9.17e-06*** *** *** Infrastructure 4.67e-06*** *** *** Agriculture 7.67e-06*** *** *** Trade 9.69e-06*** *** *** Mining 7.42e-06*** *** *** Manufacture 6.73e-06*** *** *** Basic Industry *** *** *** Miscellaneous Industry 7.48e-06*** *** *** Note : *** indicates 1% level of significant The results of domestic volatility transmission on bivariate Vector Auto Regression are reported in tables 4 to 12. The results convince that there are positive significant volatility transmission from finance index to consumer index on lag -3 ( ) and from consumer index to finance index there is no significant result. It indicates there is volatility transmission between finance index to consumer index. This is due to the fact that co-movement information flows exist between prices in the two indices. Table 4 Results of VAR model Finance Index to Consumer Index VAR Finance Consumer Constanta *** *** Finance (-1) *** Finance (-2) Finance (-3) *** *** Consumer (-1) *** Consumer (-2) *** Consumer (-3) *** Table 5 present how finance index interact with trade index, all of lag from lag 1 until 3 on finance index has significant results but only lag -1 ( ) and -3 ( ) has positive coefficient spillover. Furthermore there is volatility transmission from trade index on lag -2 ( ) to finance sector. There is has bidirectional volatility transmission from these index. We examine the volatility transmission from finance sector to infrastructure index on table 6. There is have positive significant coefficient from lag -2 ( ) and lag -3 ( ). The infrastructure index has positive significant on lag -1 ( ), -2 ( ) and lag -3 ( ) to transform volatility to finance sector. We can conclude these indices have bidirectional volatility transmission.

8 Table 5 Results of VAR model Finance Index to Trade Index VAR Finance Trade Constanta *** *** Finance (-1) *** *** Finance (-2) *** Finance (-3) *** *** Trade (-1) *** Trade (-2) * *** Trade (-3) Table 6 Results of VAR model Finance Index to Infrastructure Index VAR Finance Infrastructure Constanta *** *** Finance (-1) *** Finance (-2) *** Finance (-3) *** *** Infrastructure (-1) ** *** Infrastructure (-2) *** Infrastructure (-3) ** * Tables 7 represent volatility transmission between finance sector and agriculture sector. The agriculture sector volatility is statistically significant influence by finance sector from lag -2 ( ) and lag -3 ( ). The finance sector was influenced by agriculture sector on lag -3 ( ). It is indicate there are bidirectional volatility transmission between finance sector and agriculture sector. Table 8 examines volatility transmission among finance sector and mining sector. There have positive coefficient and statistically significant from finance sector into mining sector on lag -3 ( ) vice versa. The mining sector give positive coefficient on lag -2 ( ) and -3 ( ). Tables 9 convince that volatility transmission occurs from finance sector to manufacture sector. The finance sector transforms their volatility on lag -2 but negative coefficient ( ). The manufacture sector doesn t transform their volatility into finance sector.

9 Table 7 Results of VAR model Finance Index to Agriculture Index VAR Finance Agriculture Constanta *** *** Finance (-1) *** Finance (-2) *** Finance (-3) *** *** Agriculture (-1) *** Agriculture (-2) *** Agriculture (-3) *** *** Table 8 Results of VAR model Finance Index to Mining Index VAR Finance Mining Constanta *** *** Finance (-1) *** Finance (-2) Finance (-3) *** *** Mining (-1) *** Mining (-2) ** *** Mining (-3) * Table 9 Results of VAR model Finance Index to Manufacture Index VAR Finance Manufacture Constanta *** ** Finance (-1) *** Finance (-2) ** Finance (-3) *** Manufacture (-1) -7.18e *** Manufacture (-2) 1.61e Manufacture (-3) 3.95E *** The basic industries sector has volatility transmission from finance sector. It represent on table 10 convince that finance sector has positive coefficient and statistically significant to basic industries sector on lag -3 ( ). The basic industries sector does not have implication on volatility transmission into finance sector. Tables 11 represent volatility transmission between finance sector and miscellaneous industries. The finance sector has positive coefficient on lag -3 ( ). Further results suggested that there is reverse volatility spillover from miscellaneous industry sector to finance sector on lag -2 ( ) and lag -3 ( ). Tables 12 convince that finance sector transform volatility into property sector. The finance sector has negative coefficient on lag -2 ( ) and positive

10 coefficient on lag -3 ( ). Moreover, property sector volatility contributes to finance sector volatility on lag -3 ( ). Table 10 Results of VAR model Finance Index to Basic Industry Index VAR Finance Basic Industry Constanta *** Finance (-1) *** Finance (-2) Finance (-3) *** *** Basic Industry (-1) *** Basic Industry (-2) Basic Industry (-3) *** Table 11 Results of VAR model Finance Index to Miscellaneous Industry Index VAR Finance Miscellaneous Industry Constanta *** *** Finance (-1) *** Finance (-2) Finance (-3) *** *** Miscellaneous Industry (-1) *** Miscellaneous Industry (-2) * Miscellaneous Industry (-3) ** Table 12 Results of VAR model Finance Index to Property Index VAR Finance Property Constanta *** *** Finance (-1) *** Finance (-2) ** Finance (-3) *** * Property (-1) *** Property (-2) Property (-3) ** *** 5. Final Remarks Examining domestic volatility transmission between all sectors on Jakarta Stock Exchange give some references to academics, investors and financial market regulators in order to minimizing their risk through organizing portfolio and optimal hedging strategy. As a result, this research evaluates domestic volatility transmission

11 from banking sector ranging from 05 January 2009 to 31 December 2014 using GARCH (1.1) VAR system. The result of GARCH (1.1) VAR system examined that finance sector volatility has positively significant affect the all of sector indices in Jakarta Stock Exchange. on the other hand, only Trade indices, Infrastructure indices, Agricultural indices, Mining indices, Miscellaneous industry indices and Property indices whose transmitted their volatility into finance sector. The implication of this research is to obtain domestic portfolio management through hedging and risk management opportunities in all of sectors on Jakarta Stock Exchange. Overall, this study convinces important and useful information for building accurate asset pricing models, risk management, and forecasting future sector return volatility. Finally, since many different financial assets are traded based on the indexes examined, it is important for financial market participants to understand the volatility transmission mechanism over time and across series in order to make optimal portfolio allocation decisions.

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