pISSN: 2723 - 6609 e-ISSN: 2745-5254
Vol. 5, No. 4 April 2024 http://jist.publikasiindonesia.id/
Indonesian Journal of Social Technology, Vol. 5, No. 4, April, 2024 1798
Risk Premium and Volatility Analysis on the Indonesia Stock
Exchange
Syanindita Prameswari
1*
, Adler Haymans Manurung
2
Universitas Trisakti Jakarta, Indonesia
1
, Universitas Bhayangkara Jaya Jakarta,
Indonesia
2
1*
2
*Correspondence
ABSTRACT
Keywords:
Risk Premium; Market
volatility; Stock Indices.
Market risk premium and market volatility are essential in
investment decisions. Volatility is a vital variable in
derivative securities that measures changes in stock returns.
The research focuses on stock return volatility, research that
points to a risk premium in emerging markets. This study
aims to explain the relationship between market equity
premium and volatility using GARCH (1.1) on the Indonesia
Stock Exchange. This research uses daily closing price data
of the Indonesia Stock Exchange Composite Index (JCI).
The result of this study is that there is a relationship between
risk premium and volatility in the Indonesian stock market.
This study's conclusion is to test whether there is a
relationship between the volatility of return and risk
premium in the Indonesian stock market. Using the daily
trend of the Indonesian stock market (IDX) from January
2010 to September 2023.
Introduction
Uncertainty is a condition that occurs due to high financial fluctuations, thus
creating a situation where economic actors cannot predict what will happen in the future.
The uncertainty associated with returns can often be observed through levels of volatility.
In a financial context, volatility reflects fluctuations or changes in the value of a stock
and indicates the risks investors face. High volatility is a characteristic of high risk and
high potential returns. Understanding the volatility of an investment asset gives investors
the insight to manage their portfolios more closely and achieve optimal investment
returns.
Market risk premium and market volatility are essential in investment decisions.
Volatility is a vital variable in derivative securities that measures changes in stock returns.
Aggregate stock market volatility has been used as an economic indicator by financial
economics concerned with government policy. Fund managers usually pay attention to
risk premiums and volatility when determining asset allocation through efficient limit
estimation (Manurung, 1997). Annin & Falaschetti (1998) define the equity risk premium
Risk Premium and Volatility Analysis on the Indonesia Stock Exchange
Indonesian Journal of Social Technology, Vol. 5, No. 4, April, 2024 1799
as the extra return an investor gets in return for his willingness to bear the risk of a stock
investment above the average risk.
Estimating volatility and analysing the relationship with equity risk premiums has
become an area of financial research. The observations of Manurung (1997) show that
market volatility has a positive relationship with the market risk premium, but there is no
significant difference with zero.
Research on volatility has been conducted by previous researchers conducted by
Banumathy & Azhagaiah (2015) on the stock market in India (Lin, 2018) tested stock
volatility in China using the GARCH model (Nghi & Kieu, 2021) on Japanese and
Vietnamese stocks, and (Yahaya et al., 2023) delves into stock volatility in Nigeria
(NGX). Meanwhile, research on risk premiums was, among others, conducted by
(Morawakage et al., 2019) and (Yue et al.
While many studies have focused on the volatility of stock returns, research on the
risk premium in emerging markets has been limited. As such, this research is new and
vital in examining emerging markets volatility and risk premiums, which can aid better
decision-making for investors. Overall, the study opens up new lines of research to
investigate emerging market volatility and risk premiums. This article examines the
relationship between market equity premium and volatility using GARCH (1.1) on the
Indonesia Stock Exchange (IDX).
Volatility is a statistical measurement that measures price fluctuations of a security
or commodity in a certain period. Since volatility can be represented by standard
deviation, the public also considers volatility as a form of risk. The higher the level of
volatility, the greater the uncertainty associated with the return that can be obtained from
stocks. Stocks included in the price index face a dynamic market, considering market
participants can quickly enter or leave the market (Mukmin & Firmansyah, 2015).
Thus, we can conclude that volatility is a variation in stock movements that can be
measured by standard deviation, reflecting an unstable nature and difficult to predict.
Equity Risk Premium (ERP) has been the focus of attention in asset pricing
literature over a long period due to its significant role in determining the expected rate of
return on investment. Annin & Falaschetti (1998) focused on ERP by introducing the
"ERP puzzle." Their research proved that historical ERP was substantially larger than
could be rationalised using United States data from 1889 to 1978. However, studies using
more recent data from the United States market show different results with such ERP.
Further, the relationship between expected returns and volatility has been shown to have
a negative or insignificant correlation. However, the observations of Manurung's (1997)
research show that market volatility has a positive relationship with the market risk
premium but does not show a significant difference. Meanwhile, (Morawakage et al. show
that there is no direct relationship between volatility and risk premium.
Research Methods
This research uses daily closing price data of the Indonesia Stock Exchange
Composite Index (JCI). This index has a weighted value and includes all stocks listed on
Syanindita Prameswari, Adler Haymans Manurung
Indonesian Journal of Social Technology, Vol. 5, No. 4, April, 2024 1800
the IDX. The daily closing price is used to calculate the composite index. Meanwhile, the
data sample taken in this study is only the Composite Stock Price Index (JCI) based on
time series data per day from January 2, 2010, to September 30, 2023. The effective day
of regular market stock trading is five business days in one week. Data collection is done
by downloading daily composite stock price index data on the Yahoo Finance website.
ARCH-GARCH
The ARCH model was initially developed by Engle (1982). ARCH expresses that
under the random variable y, taken from the conditional density
oneuoneconeiononenononone󰇛
󰇜, eestimatedvalueoof
todayedependsonpaseperformanceoreeileaan s oallows
……………. (1) Where,
thethe E(
󰇜 n oneoneri dari
=





……………. (2)
The above equation is called ARCH(q). This reveals that conditional variance
changes over time due to past errors, leaving a constant unconditional variance (W).
Ow: 






……………. (3)
This context is the conditional variance that changes over time as a function of past
error and past conditional variance. et al. (1986) Note that alpha sub one and beta sub, i.,
end subscript must be positive in the GARCH process (1,1) (to produce that all sigma sub
t squared is positive.
The risk premium is the difference between market returns and risk-free interest
rates and led:



……………. (4)
Where,
R
mt
= Stock returns for the i period
R
it
= Risk-free interest rate (3-month term deposit rate at the beginning of the month t)
= Monthly volatility
Interest rate data is based on the current 3-month term deposits from Bank
Indonesia. Return is the total gain or loss from an investment over time. Monthly returns
are chosen to calculate volatility based on daily data. Marke returns are calculated as
follows: 
󰇛

󰇜 ……………. (5)
Where,
I
t
= Market indices at the end of the month t
I
t-1
= Pasae index at the end of the month t-1
Natural logs are used to determine continuously coupled returns.
Monthly volatility is calculated from daily stock returns. The standard deviation of
the sample I used to measure historical volatility. The formula for standard deviation
volatility measured from sampl n observations of the variable R (return) is calculated as
follows (Manurung, 1997)
󰇛
󰎞󰇜


……………. (6)
Where,
R
i
= Stock returns for the i period
R = Average stock returns over a period
n = Number of Observations
Risk Premium and Volatility Analysis on the Indonesia Stock Exchange
Indonesian Journal of Social Technology, Vol. 5, No. 4, April, 2024 1801
Results and Discussion
Table 1 shows the lowest and highest daily returns during the sample period.
September saw the lowest daily return, and March showed the highest return. A
significant correction in JCI had occurred in 2011. The potential default on debt of
European countries such as Greece, Portugal, and Spain is said to cause investor anxiety.
In addition, the highest daily returns occurred twice in March 2020. The most
crucial factor is the policy stimulus implemented by the government and central bank
during the Covid-19 pandemic. In the context of monetary policy, the central bank
conducts quantitative easing by buying securities issued by the government. This easing
can affect the performance of a country's capital market. The government's fiscal stimulus
policy on the economy is in the form of business assistance. These two stimuli can
ultimately support capital market performance.
Table 1
Twenty Daily Return Lows and Highs
From January 2010 to September 2023
Lowest Daily Return (%)
Highest Daily Return (%)
1
22 September 2011
-
8,88
10,19
2
09 March 2020
-
6,58
7,27
3
03 October 2011
-
5,64
4,76
4
August 19, 2013
-
5,58
4,76
5
March 19, 2020
-
5,20
4,65
6
March 12, 2020
-
5,01
4,55
7
10 September 2020
-
5,01
4,55
8
March 17, 2020
-
4,99
4,07
9
March 23, 2020
-
4,90
4,06
10
05 August 2011
-
4,86
3,98
11
August 19th, 2011
-
4,43
3,90
12
March 16, 2020
-
4,42
3,82
13
09 May 2022
-
4,42
3,53
14
10 January 2011
-
4,21
3,50
15
- 11 November 2016
-
4,01
3,44
Syanindita Prameswari, Adler Haymans Manurung
Indonesian Journal of Social Technology, Vol. 5, No. 4, April, 2024 1802
Lowest Daily Return (%)
Highest Daily Return (%)
16
24 August 2015
-
3,97
3,44
17
04 June 2012
-
3,82
3,35
18
05 From 2010
-
3,81
3,32
19
05 September 2018
-
3,76
3,32
20
27 August 2013
-
3,71
3,26
Source: Processed Data, 2024
Figure 1
Composite Stock Price Index 2010 2023 (September)
Figure 2
Indonesia Stock Exchange Monthly Volatility 2010 2023 (September)
-
1.000
2.000
3.000
4.000
5.000
6.000
7.000
8.000
20102011201220132014201520162017201820192020202120222023
INDEKS HARGA SAHAM GABUNGAN
TAHUN
Indeks Harga Saham Gabungan
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
20%
Jan 2010
Jul 2010
Jan 2011
Jul 2011
Jan 2012
Jul 2012
Jan 2013
Jul 2013
Jan 2014
Jul 2014
Jan 2015
Jul 2015
Jan 2016
Jul 2016
Jan 2017
Jul 2017
Jan 2018
Jul 2018
Jan 2019
Jul 2019
Jan 2020
Jul 2020
Jan 2021
Jul 2021
Jan 2022
Jul 2022
Jan 2023
Jul 2023
Volatilitas
Risk Premium and Volatility Analysis on the Indonesia Stock Exchange
Indonesian Journal of Social Technology, Vol. 5, No. 4, April, 2024 1803
Figure 2 presents the volatility pattern of daily market monthly returns from 2010
to September 2023. The figure shows a high peak in March 2020, reflecting that the
market index fell dramatically and sharply doubled in the month. After March 2020,
volatility tended to decline, relatively small from April 2021 until the end of the study
period.
Descriptive Statistics
Table 2 illustrates the descriptive statistics of the calculated risk premium for the
Indonesian market. Results corresponding to the Jarque-Bera test for normality are also
presented in Table 2.
Table 2
Descriptive Statistics
Mean
0.11%
Median
-0.34%
Maximum
24.01%
Minimum
-13,54%
Hours Deviasi
5.67%
Skewness
0.663
Kurtosis
4.86
Jarque-Bera
35.77
Probability
0.000
Statistics Uji ADF
-18.675***
Uji ARCH LM F-Stat
55.67***
The average daily risk premium is positive in Indonesia at 0.11%. Indonesia's risk
premium indicates relatively greater unconditional volatility based on standard deviation.
The Indonesian market (IDX) indicated positive skewness and leptokurtosis during the
sample period, which indicated deviations from normal distribution. The Jarque-Bera test
also justifies abnormal distribution in the risk premium. A significant kurtosis coefficient
indicates a leptokurtic risk premium in the market. Each risk premium indicates
stationarity based on the ADF test results in Table 2. In addition, the ARCH LM test
indicates a significant heteroscedasticity in the risk premium; thus, conditional volatility
varies with time.
model GARCH (1,1):




……………. (7a)

neere,

= Cumulative average monthly returnoneagin t-




……………. (7b)
Equation (7b) reveals that the coefficient at the lag of the squared error is positive.
The lag of the conditional variance of this term is very significantly different from zero,
but the lag of the squared error is not significantly different from zero.
Risk is significant in the Capital Asset Pricing Model (CAPM). These results can
help decision-makers for investors predict market risk using last month's market risk. If
the market becomes more volatile this month, risk-averse investors can enter it. However,
Syanindita Prameswari, Adler Haymans Manurung
Indonesian Journal of Social Technology, Vol. 5, No. 4, April, 2024 1804
more risk-averse investors will not enter the market and will wait until the market has
performed well. Risk can also help securities companies, especially finance or investment
banking companies, create new products.
Volatility and Risk Premium
The estimated risk premium of Indonesia Stock Exchange (IDX) aggregate sha
ow Cap



……………. (8)
Equation (8) shows that the volatility coefficient is a significant positive value or
differs significantly from zero. These results will help decision-makers estimate the risk
premium. These results indicate that market volatility is essential in determining a stock's
risk premium. Equation (8) can be used to estimate the risk-free rate, which is often
difficult to find, especially in emerging markets such as the IDX. With quantifiable
market returns, investors can estimate the cost of equity and further assist in making
decisions.
Conclusion
This study's conclusion is to test whether there is a relationship between the
volatility of return and risk premium in the Indonesian stock market. Using the daily trend
of the Indonesian stock market (IDX) from January 2010 to September 2023. We use the
GARCH model (1,1). The methodology used here differs from previous studies that
mainly used the usual least squares time series (OLS) method to estimate the GARCH
coefficient and risk premium. It was found that there is a relationship between risk
premium and volatility in the Indonesian stock market.
Risk Premium and Volatility Analysis on the Indonesia Stock Exchange
Indonesian Journal of Social Technology, Vol. 5, No. 4, April, 2024 1805
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