Volatility Transmission of Global Main Stock return to indonesia
2.1. Modeling Stock Market Volatility
Volatility in financial markets illustrates the fluctuations in the value of an instrument within a certain period. In statistics terms, volatility is defined as the change in the value of the average fluctuation of a financial time series.
Their volatility will lead to risks and uncertainties faced by the market players riskier, so that the interest of market participants to invest become unstable.
Moreover, the existence of volatility also impacted on the existence of global financial markets as it relates to the notion of risk.
Kind of volatility is often observed in the stock market is the volatility of stock prices and the volatility of return stock. The stock price volatility is describing a change in closing price of a stock or a stock index that occurred during a particular period of observation. The fluctuations of stock’s closing price can occur due to the internal factors and external factors (Ajireswara, 2014). Internal factors which cause the closing price fluctuations are associated with stock listed companies are concerned, for example, the change in the company’s profit level. In addition, when viewed from the external factors that occur as shocks on the foreign stock markets, macroeconomic factors;
such as exchange rates and interest rates, as well as the issues emerging in the stock market itself. Stock price volatility is very important to observe for investors, as the basis for calculating the volatility of return stock Volatility of return stock describe a fluctuation of difference in daily price observations within a specified observation period.
Fluctuations in the value of an instrument in the stock market one of which may be due to the influence of irrational factors that influence the supply and demand of a market (Maskur, 2009). This irrational factor may be the rumors that are developing in a market, to follow a dream, whisper friends, or the price game occured. An efficient market is a stable market where there is no significant fluctuations due to irrational actions. Measurement of volatility of stock price is useful to indicate whether the asset is “excessive movement” of actors irrational market participants, in which speculators along frenzy (emotional) investor regulate or affect the stock price movement of stocks, so the movement that are not happening because of fundamental reasons. The stock market that influenced by speculators and frenzy investors have a persistent and high volatility.
Financial time series has given rise time-varying volatility or
“heteroscedasticity” of the data. Model linear trend, exponential smoother, or ARIMA models have failed to observe the phenomenon of their high volatility (increased variance), because the model assumes a constant residual variance (Montgomery et al., 2007). Over the past three decades, many studies conducted to modelling volatility, especially in the financial markets.
Bollerslev (1986) proposed a model generalized autogressive conditional heteroscedasticity (GARCH) with order k and l; GARCH(l,k).
GARCH represents that the current conditional variance is also dependent on the previous conditional variances and residual squared lag. GARCH models indicate that the volatility of returns asset depict clustering volatility views from the lagged variances.
The classical model ARCH and GARCH work for the assumption that all the effects of shocks on volatility has a symmetric distribution. But in fact, returns asset do not always have a symmetrical distribution but also an asymmetrical distribution represented asymmetric GARCH models.
Characteristics that often appear in the observation data volatility in the financial sector is the existence of asymmetric volatility. The classical model of GARCH model ignore a phenomena of asymmetric volatility that are better suited for modeling the volatility of return stock, because it captures the leverage effect; the negative correlation between volatility and return at the last period. Asymmetrical conditions generally arise where the stock market is in conditions crash, i.e. when a drop in stock price will give further effect to a significant increase in the volatility of the stock (Wu, 2001). Thus, causing negative events have greater effect than positive events towards the volatility of the asset. Engle and Ng (1993) also explains that the positive and negative information has a different impact on volatility; where bad news is likely to have a higher impact on volatility than the good news.
It is important to know that one country against the other country has different performance to capturing the leverage effect, so that the various of specifications of asymmetric GARCH models should be chosen to make the models more accurate (Yalama and Sevil, 2008). Specifications for asymmetric GARCH models among Exponential-GARCH (EGARCH) proposed by Nelson (1991), Threshold-GARCH (TGARCH) proposed by Zakoian (1994), GJR proposed by Glosten et al. (1993), Integrated-GARCH (IGARCH) by Engle
Volatility Transmission of Global Main Stock Return to Indonesia and Bollerslev (1986), Component-GARCH by Engle and Lee (1993), Assymetric power ARCH (APARCH) by Ding et al. (1993), and others.
The study of the data that containing the effects of asymmetric volatility has a lot to do, such as Engle and Ng (1993), Nelson (1991), Zakoian (1994), Glosten et al. (1993), Engle and Bollerslev (1986), Ding et al. (1993), Engle and Lee (1993), and several other related research. The reality of the existence of the volatility in the stock market, both at the corporate level, local or global, such as Gokbulut and Pekkaya (2014), Wu (2001), Awartani and Corradi (2005), Yalama and Sevil (2008), Mishra et al. (2007), Booth et al. (1997), Lestano and Sucito (2010), and Miran and Tudor (2010).
Gokbulut and Pekkaya (2014), examined the ability of symmetric and asymmetric GARCH for estimating and forecasting volatility of the stock market, the exchange rate, and the interest rate on the Turkish financial market. The main obtained results from these study indicate that there are asymmetric effects on each market. Asymmetric GARCH models currently used in the estimation and forecasting time series data of the financial markets showed a better performance in describing the volatility compared to the classical model.
Research conducted by Awartani and Corradi (2005) using stock index S&P-500 to test the predictive ability of GARCH samples of 10 different models. They found that the model of asymmetric GARCH plays a crucial role in predicting volatility. GARCH model weak when compared with the asymmetric GARCH model to describing volatility. In addition, return stock combining leverage effect,so that the asymmetric behavior of volatility can provide more accurate predictions. Yalama and Sevil (2008) also studied the 7 differences GARCH to perform forecasting on daily data of 10 different countries. Based on the research result that GARCH models have performance differences from one country to another country and the performance of EGARCH, PARCH, TARCH, IGARCH, GARCH and GARCH-M is a better model to estimating the volatility.
Engle and Ng (1993) defines the news impact curve which measures how new information is incorporated into the estimation of volatility. Specifications model are used to modelling the unpredictable return (residual), such as GARCH, EGARCH, Asymmetric-GARCH, VGARCH, Nonlinear-Asymmetric- GARCH,GJR-GARCH, and Partial nonparametric (PNP) ARCH. Selection
model is made to find a model that fits in modeling daily returns stock of Japan’s stock market from 1980 to 1988. The results of the model tests indicate that there are types of asymmetric effects of news on volatility. All models are tested to find results that negative shocks more volatile than shocks positive.