p-ISSN: 2723-660 e-ISSN: 2745-5254
Vol. 5, No. 11, November 2024 http://jist.publikasiindonesia.id/
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 11, November 2024 4815
Comparative Analysis of Risk Premium and Stock Market
Returns in Indonesia: A Case Study of Normal and COVID-
19 Pandemic Periods
Peni Rahmadani
1*
, Adler Haymans Manurung
2
Universitas Trisakti, Jakarta, Indonesia
1
Universitas Bhayangkara Jaya, Jakarta, Indonesia
2
Email: [email protected]
1*
2
*Correspondence
ABSTRACT
Keywords: Risk Premium;
Return Pasar; COVID-19;
GARCH (1,1)
This research examines the effect of market risk premium (risk
premium) on market returns (JCI). In addition, this study aims to
see the effect of premium risk on market returns during the
COVID-19 Pandemic. The data used in this study is secondary
(JCI data and Deposit Interest Rates) for the period January 2010
– September 2023. The method used in this study is the GARCH
method (1.1). The results of this study show a positive and
significant influence of risk premium on market returns (JCI).
During the COVID-19 Pandemic, risk premiums negatively and
significantly affected JCI returns or the Indonesian stock market.
Introduction
Volatility is a measure that describes the degree of variation or fluctuation in a
value, usually in the context of the price of a financial asset such as stocks, bonds, or
currencies. Volatility also reflects changes in the value of stocks, often used to measure
the uncertainty associated with returns. In the financial world, a high level of volatility
indicates excellent risk but also opens up opportunities for high returns (Firmansyah,
2006; Mukmin & Firmansyah, 2020). Understanding asset volatility is critical for
investors aiming to manage portfolios effectively and achieve optimal investment returns.
In addition to volatility, the concept of risk premium is central to financial
decision-making. Risk premium represents the additional compensation investors expect
for taking on higher risks compared to risk-free assets. This metric becomes even more
vital during periods of economic uncertainty, such as the COVID-19 pandemic. In
Indonesia, the pandemic caused unprecedented market volatility, which highlighted the
critical importance of risk premium in guiding investment strategies.
In the financial economy, aggregate stock market volatility is used as an economic
indicator of government policy. Fund managers often consider risk premiums and
volatility when determining asset allocation, using efficient limit estimation (Manurung,
1997). Annin and Dominic (1998) explained that the equity risk premium is an additional
return investors receive as compensation for their willingness to take on higher stock
investments than the average risk.
Peni Rahmadani, Adler Haymans Manurung
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 11, November 2024 4816
Estimating volatility and analysing its relationship to equity risk premiums has
become the focus of research in the financial sector. The Capital Asset Pricing Model
(CAPM), as expressed by Sharpe (1964), Lintner (1965), and Black (1976), highlights
the positive relationship between risk or volatility and the expected return of a security.
Manurung's (1997) research found a positive relationship between market volatility and
market risk premiums, although the difference was insignificant from zero.
Research on volatility has been widely conducted by previous researchers, such
as Banumathy & Azhagaiah (2015), who examined the stock market in India; Lin (2018),
who examined stock volatility in China using the GARCH model; Nghi & Kieu (2021)
who analysed stocks in Japan and Vietnam, and Yahaya et al. (2023) who investigated
stock volatility in Nigeria (NGX). Meanwhile, research on risk premiums has been
conducted by Morawakage et al. (2019) and Yue et al. (2023)
However, research specifically addressing risk premiums in emerging markets
remains limited. The current study fills this gap by examining the relationship between
market equity premiums and volatility in Indonesia, particularly during the pandemic
period. This focus provides valuable insights into how emerging markets respond to
global crises and informs investment strategies in similar contexts.
Moreover, the urgency to understand the role of risk premiums in Indonesia is
underscored by the need to stabilize financial markets amidst heightened uncertainty.
Risk premiums offer a lens through which investors can evaluate the trade-offs between
potential returns and associated risks. The insights from this study aim to contribute to
more resilient market strategies, enabling stakeholders to navigate both current and future
economic challenges effectively.
In this article, we employ the GARCH (1,1) model to analyze the relationship
between market risk premiums and volatility in the Indonesia Stock Exchange (IDX). By
doing so, the study seeks to provide a comprehensive understanding of how these
variables interact under normal and crisis conditions, with implications for broader
economic resilience and investor confidence.
Methods
This research uses daily closing price data from the Jakarta Composite Stock Price
Index (JCI) listed on the Indonesia Stock Exchange. The index is weighted and includes
all stocks listed on the IDX, with the daily closing price used to calculate the composite
index. The data sample used in this study includes JCI with daily time series data from
January 1, 2010, to December 31, 2023. Stock trading on the regular market is done five
working days a week. Data is collected by downloading daily composite stock price index
price index information from the Yahoo Finance website.
Data
Composite Stock Price Index (JCI)
The Jakarta Composite Stock Price Index (JCI) is an indicator that describes the
movement of stock prices (Gumanti, 2011). The index also functions as an indicator of
market trends, meaning that the movement of the index describes the condition of the
market at a particular time, whether the market is active or sluggish (Mukmin, 2015). JCI
can be calculated by the following formula (Tobing and Manurung 2008)
Comparative Analysis of Risk Premium and Stock Market Returns in Indonesia: A Case Study
of Normal and COVID-19 Pandemic Periods
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 11, November 2024 4817
In this case:
IHSG
t
= Composite Stock Price Index in the period t.
Q
0, i
= Number of Shares Listed for the ith share in the basis period (0)
P
t, i
= Stock price to 1 in the period t.
P
t-1, i
= Stock price to 1 in the period t-1.
Return of the Composite Stock Price Index (JCI)
Return is the total profit or loss from an investment over time. Monthly returns
are chosen to calculate volatility based on daily data. Market returns are calculated as
follows (Manurung, 1997) :
(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
The natural log is used to determine the continuously combined returns.
Risk Premium
The risk premium is the difference between the market yield and the risk-free
interest rate and is calculated as follows (Manurung, 1997)
(4)
Where,
R
mt
= Stock yield for the period i
R
it
= Risk-free interest rate (3-month term deposit rate at the beginning of the month
t)
= Monthly volatility
The interest rate data is based on the current 3-month deposits from Bank
Indonesia.
Volatility
The calculation of monthly volatility is based on the daily stock returns. The
standard deviation of the sample is employed to quantify historical volatility. The formula
for the standard deviation or volatility, derived from the sample of n observations of the
R (return) variable, is as follows: (Manurung, 1997)
Peni Rahmadani, Adler Haymans Manurung
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 11, November 2024 4818
(6)
Where
Ri
= Stock yield for period i
R = Average stock yield over a period
n = Number of Observations
COVID-19 Pandemic
The COVID-19 pandemic in Indonesia officially began on March 2, 2020, and
ended on June 21, 2023. So, in this study, the COVID-19 pandemic data starts from March
2022 and ends in June 2023 by providing the number 1 for the months of the COVID-19
Pandemic and the number 0 for the months that are not the COVID-19 Pandemic.
ARCH-GARCH
Heteroscedasticity describes the change in volatility over a time horizon. One of
the models to overcome heteroscedasticity is the Generalized Autoregressive Conditional
Heteroscedasticity (GARCH) proposed by Bollerslev (1986)
The ARCH model was initially developed by (Engle, 1982). ARCH Declare under
the random variable Y, taken from the conditional density function
, Then
the estimated value of today depends on past performance, for example, as follows:
(1)
Where,
E(
= 0
and variants of
=
(2)
The above equation is called ARCH (q). This reveals that the conditional variance
changes the time siring as a function of past errors, leaving a constant unconditional
variance (W).
Bollerslev (1986) extended the ARCH(q) model to what is called GARCH(p,q).
GARCH (p,q) is formulated as follows:
(3)
This context is a conditional variance that changes over time as a function of past
errors and past conditional variances. (Bollerslev, 1986) notes that
and
must be
positive in the GARCH (1,1) process (to produce that all
Is positive.
Comparative Analysis of Risk Premium and Stock Market Returns in Indonesia: A Case Study
of Normal and COVID-19 Pandemic Periods
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 11, November 2024 4819
Results and Discussion
Table 1 illustrates the twenty lowest and highest daily rates of return over the
specified sample period. The lowest daily rate of return was observed in September, while
the highest was observed in March. In 2011, JCI experienced a significant correction due
to investor concerns about the potential for debt defaults in European countries, including
Greece, Portugal, and Spain.
Table 1. Twenty Lowest and Highest Daily Returns
From January 2010 to September 2023
Lowest Daily Return (%)
Highest Daily Return (%)
1
22 September 2011
-8,88
26 March 2020
10,19
2
09 March 2020
-6,58
26 May 2010
7,27
3
03 October 2011
-5,64
27 March 2020
4,76
4
19 August 2013
-5,58
27 September 2011
4,76
5
19 March 2020
-5,20
19 September 2013
4,65
6
12 March 2020
-5,01
27 August 2015
4,55
7
10 September 2020
-5,01
06 October 2011
4,55
8
17 March 2020
-4,99
06 April 2020
4,07
9
23 March 2020
-4,90
10 May 2010
4,06
10
05 August 2011
-4,86
10 September 2013
3,98
11
19 August 2011
-4,43
15 September 2010
3,90
12
16 March 2020
-4,42
26 June 2013
3,82
13
09 May 2022
-4,42
16 June 2020
3,53
14
10 January 2011
-4,21
01 February 2021
3,50
15
11 November 2016
-4,01
17 April 2020
3,44
16
24 August 2015
-3,97
10 August 2011
3,44
17
04 June 2012
-3,82
16 September 2013
3,35
18
05 May 2010
-3,81
14 June 2013
3,32
19
05 September 2018
-3,76
06 June 2012
3,32
20
27 August 2013
-3,71
30 April 2020
3,26
Source: Yahoo Finance, Data processed, 2024
In March 2020, the highest daily returns were recorded on two occasions. This
can be attributed to the policy stimulus implemented by the government and central banks
in response to the global health crisis caused by the COVID-19 pandemic. In monetary
policy, the central bank engages in quantitative easing by purchasing government
securities, which exerts an influence on capital market performance. Furthermore,
government fiscal stimulus in the form of assistance to businesses also contributes to
supporting capital market performance.