ARCH-LM Test Research Articles

Statistical Modelling and Projection of Future Rainfall using SARIMA and Hybrid SARIMA-GARCH Models in Various Zones of Kerala

Water is an important natural resource considered as basic need for all living things around the world. The volume of pure water present in the Earth is regulated by the amount of rainfall received over the years. Sudden climatic changes are observed in throughout the world which led to flood, drought and uneven rainfall over the years. In this study, SARIMA and SARIMA-GARCH models are applied for forecasting rainfall in different zones of Kerala. The presence of heteroscedasticity in residuals obtained from SARIMA model was identified using ARCH-LM test and it was eliminated by applying SARIMA-GARCH model to the same. The ARCH-LM test results confirmed the presence of heteroscedasticity in residuals. The comparison of models used for predicting rainfall revealed that hybrid SARIMA-GARCH model is more efficient in projecting future values of rainfall in the northern and southern zones of Kerala whereas SARIMA model is showing more accuracy in the central zone of Kerala even in the presence of heteroscedasticity of residuals. The comparison of rainfall forecasted in different zones of Kerala clearly indicated that rainfall is higher in the northern zone whereas lower in the southern zone. In the northern and central zones, the rainfall showed a peak from June to September and almost negligible rainfall from December to February. The outperformed model in each zones of Kerala was applied for projection of future rainfall for next 5 years (2021-2025). Compare to previous years, the rainfall in the northern and central zones is expected to decrease whereas in southern zone of Kerala, rainfall will be almost same.

Forecasting the Stock Market Returns Using nonlinear hybrid GARCH-SETAR model

Forecasting stock market returns is a valuable tool for investors seeking to enhance their gains in stock trading. Predicting stock prices proves to be a formidable endeavor due to its substantial volatility, non-linear characteristics trends, and responsiveness to multifaceted variables, including economic conditions, market trends, seasonality, and sentiment. Despite these complexities, non-linear methodologies like threshold time series and conditional heteroscedasticity models are underutilized. This study aims to assess the predictive capabilities of a hybrid GARCH-SETAR model in the context of stock market returns, encompassing both Islamic and conventional stocks listed on the Pakistan Stock Exchange. The Islamic and conventional stock markets in Pakistan represented by KMI-30, KSE-30 and KSE-100 selected contain daily data from January, 2012 till June, 2023. After the confirmation of stationarity, ARCH effect and non-linearity by ADF, PP unit root, ARCH-LM and BDS test respectively the best estimated linear traditional Box-Jenkins ARIMA, non-linear threshold SETAR and ARIMA-GARCH models are selected based on AIC and BIC information criteria’s. The best proposed hybrid GARCH-SETAR model is also selected based on AIC and BIC information criteria’s. It is revealed that the one-step-ahead recursive forecast performance proposed hybrid GARCH-SETAR model outperforms all other selected linear and non-linear models for both Islamic and conventional stock markets based on RMSE, MAE, MAPE and SMAPE forecast evaluation criteria’s.

The Spill-over Effects between Oil and Gas Marketing and Exploration Firms of Pakistan Stock Exchange (PSX) KSE-100 index: During the Times of COVID-19 and Russian-Ukraine War

This study aims to trace the mean and volatility spillover effects between oil and gas marketing and exploration firms registered in KSE-100 index in the Pakistan Stock Exchange (PSX). Daily data of 8 firms (MGAS, PPL, PKOL, APL, HITE, HASC, PSO, and SHEL) for the period of 30th November 2019 to 30th November 2022. The whole data set is divided into two subsets, one is after COVID-19 (after 26th February2020) and the other is after the start of Russian-Ukraine war (after 24th February 2022). After that Hamao et.al (1990) uni-variate ARCH-GARCH type modeling is employed to the data in order to explore the dynamic linkages between the Marketing and Exploration firms (oil and gas) registered in KSE-100 index (PSX). The results from the data sets are indicating that there is mix movements between the oil and gas marketing and exploration firms in the KSE-100 index (PSX). The results are providing evidence there is Mean spill over from MGAS to APL, HASC, HITE, PSO, and SHEL. From PKOL to APL, HASC, HITE, PSO, and SHEL. From PPL to APL, HITE, PSO. And there is Volatility spill over from MGAS to APL, HITE, PSO and SHEL. From PKOL to APL, HITE, PSO, and SHEL. From PPL to APL, HASC, HITE, PSO, and SHEL. This linkage is developed between MGAS, PPL, and PKOL to APL, HASC, HITE, PSO, and SHEL due to the co-movements among the mentioned firms. The Augmented Dickey-Fuller Test is run on the return series and the test is insignificant in all series which is indicating that our return series are stationary at level, ARCH-LM test is run on return series and ARCH-LM test is also insignificant in all the return series means that there is no heteroskedasticity present in the return series.

Predicting volatility of bitcoin returns with ARCH, GARCH and EGARCH models

The ınvestment decisions of institutional and individual investors in financial markets are largely influenced by market uncertainty and volatility of the investment instruments. Thus, the prediction of the uncertainty and volatilities of the prices and returns of the investment instruments becomes imperative for successful investment. In this study we seek to identify the best fit model that can predict the volatility of return of Bitcoin, which is in high demand as an investment tool in recent times. Using the opening data of weekly Bitcoin prices for the period of 11.24.2013–03.22.2020, their logarithmic returns were calculated. The stationarity properties of the Bitcoin return series was tested by applying the ADF unit root test and the series were found to be stationary. After reaching the average equation model as ARMA (2.2), it was tested whether there was an ARCH effect in the ARMA (2,2) model. As a result of the applied ARCH-LM test, it is reached that the residuals of the average equation model selected have ARCH effect. Volatility of Bitcoin return series after detection of ARCH effect has been tried to predict with conditional variance models such as ARCH (1), ARCH (2), ARCH (3), GARCH (1,1), GARCH (1,2), GARCH (1,3), GARCH (2,1), GARCH (2,2), EGARCH (1,1) and EGARCH (1,2). While the obtained findings indicate that the best model is in the direction of GARCH (1,1) according to Akaike info criterion, it was found that GARCH (1,1) model does not have ARCH effect as a result of the applied ARCH-LM test. Thus, our empirical findings highlight an ample guide on appropriate modeling of price information in the Bitcoin market.

Exploring Asymmetric Linkages and Volatility Transmission between Stock Market and Foreign Exchange (FOREX)

This study examines the direction and nature of volatility transmission between the stock market and the foreign exchange market (FOREX) of Pakistan. For this purpose, daily data is gathered to conduct the analysis for both markets. Stock market returns are measured by using the KSE-100 index and the daily Pakistan Rupee-PKR to the United States Dollar-USD exchange rate is used for the foreign exchange market. Noticeably, both the variables are stationary at their first difference. Normality is examined through Skewness and Kurtosis, Autocorrelation through Correlogram Q-statistics, and the ARCH effect through the ARCH-LM test. Volatility transmission between the two markets is examined by using bivariate diagonal BEKK-GARCH and bivariate E-GARCH models. The results of the study indicate that bi-directional volatility transmission exists between the stock market and the foreign exchange market (FOREX) of Pakistan. Results from the E-GARCH model further indicate that the nature of volatility transmission between these two markets is asymmetric, which means bad news of one market has a greater impact on the volatility of another market than the good news. Hence, the results of this study also indicate that past variances of the stock market and foreign exchange market has a significant effect on their own current volatility.

Analysing Volatility Dynamics of India and US Stock Market

Abstract: Analysing the risk-return trade-off of financial assets, volatility is a key factor. Volatility promotes market liquidity and demonstrates the effectiveness of the financial system. To have an insights of India And US Market Volatility behaviour, this paper examines the volatility dynamics of India And US Stock market by taking Sensex And S & P 500 daily closing prices for the 2nd January 2003 to 31st December 2019. The authors have two questions to examine about volatility whether India and US stock market volatility shows volatility clustering and volatility persistence or not. ARCH LM test and Ljung Box Q2 statistics were used to check for volatility clustering. And for volatility persistence GARCH (1,1) model is used. The results of the study confirm the presence of volatility clustering in in both India And US stock market. Additionally, Indian stock return volatility shows lower volatility persistence than US. The results of the study are quite useful for the academicians, policy makers and investor community in general

Modelling volatility effects between stock, oil, gold and forex markets: Evidence from India

Although several studies on the integration of diverse stock markets have been conducted in the financial literature, most of them have focused on the integration and volatility spillovers across established stock markets. The present study explores the dynamics of integration and volatility spillover across gold, oil, forex, and stock markets during four significant events in India: the pre-changed government regime, the post-changed government regime, the post-Brexit referendum date, and the COVID era. Daily data from 2010 to 2022 is divided into four categories using the Chow test. This is done to examine if these events’ financial turmoil affects market interconnectivity. The unit root test determines data stationarity. The ARCH LM test examines series volatility clustering, and the BEKK GARCH test examines market volatility spillover. Results indicate that gold cannot be considered a hedge or safe haven. Secondly, market interconnectedness increased during the crisis period. Third, domestic political and geopolitical conditions globally do not increase the scale of spillover amongst financial assets, though they impact the spillover’s magnitude. The results of this study have several important implications for portfolio diversification and risk management.

АНАЛІЗ ТА ПРОГНОЗУВАННЯ ДОХІДНОСТІ АКЦІЙ MICROSOFT ТА PFIZER ЗА ДОПОМОГОЮ ARIMA-GARCH-МОДЕЛЕЙ

Shares are one of the most common objects for investment. Individual investors both invest directly in the securities of a certain company and invest in various funds created from the shares of public companies according to different structures. For a significant share of the population of highly developed countries with a developed financial infrastructure, income from investments is an important source of passive income that increases the financial security of households in case of temporary loss of work, illness, or other adverse circumstances. Therefore, the analysis of securities quotes to select assets for further investment is an extremely important task. When studying the dynamics of stock quotes, due to the significant role of risk, volatility is an essential component. To correctly respond to possible spikes in volatility caused by certain events, and forecasting their duration, it is important to use analysis. Econometric analysis with the help of time series research models is selected as the optimal option for the study of the dynamics of stock quotations. Due to the high quality, the most common is the simulation of securities quotations using a combination of ARIMA-GARCH models. Various modifications of this method were implemented in this work using the R programming language. Data on the daily returns of Microsoft and Pfizer shares were used for the analysis. At the first stage of the modeling process, a transition to log- returns was made, graphs of the initial time series, autocorrelation functions were constructed, time series were checked for stationarity according to the Dickey-Fuller test, and the optimal specification of the ARIMA model was obtained for both indices. At the same time, when checking the residuals of the models for autocorrelation and the ARCH effect, positive results were obtained, which indicates the inadequacy of using only the ARIMA model and the need for GARCH. As a result of sorting through various GARCH specifications, optimal ones were chosen for two stocks, both of which take into account the asymmetric impact of disturbances depending on their signs. The resulting models were tested by the Leung-Box test, the ARCH LM test, and the Pearson test for specification optimality. Based on the obtained models, a forecast was built using the sliding window method and compared with the actual time series data. The quality of the forecasts of the optimal models and other specifications was also correlated to check that the minimum forecast error was obtained using the selected models. All results confirmed the correctness of the built models, which allows them to be used for analysis and forecasting already for further periods.

Modeling and Forecasting Cryptocurrency Returns and Volatility: An Application of GARCH Models

The future of e-money is crypocurrencies, it is the decentralize digital and virtual currency that is secured by cryptography. It has become increasingly popular in recent years attracting the attention of the individual, investor, media, academia and governments worldwide. This study aims to model and forecast the volatilities and returns of three top cryptocurrencies, namely; Bitcoin, Ethereum and Binance Coin. The data utilized in the study was extracted from the higher market capitalization at 31st December, 2021 and the data for the period starting from 9th November, 2017 to 31st December 2021. The Generalised Autoregressive conditional heteroscedasticity (GARCH) type models with several distributions were fitted to the three cryptocurrencies dataset with their performances assessed using some model criteria. The result shows that the mean of all the returns are positive indicating the fact that the price of this three crptocurrencies increase throughout the period of study. The ARCH-LM test shows that there is no ARCH effect in volatility of Bitcoin and Ethereum but present in Binance Coin. The GARCH model was fitted on Binance Coin, the AIC and log L shows that the CGARCH is the best model for Binance Coin. Automatic forecasting was perform based on the selected ARIMA (2,0,1), ARIMA (0,1,2) and the random walk model which has the lowest AIC for ETH-USD, BNB-USD and BTC-USD respectively. This finding could aid investors in determining a cryptocurrency's unique risk-reward characteristics. The study contributes to a better deployment of investor’s resources and prediction of the future prices the three cryptocurrencies.

New R package arfurimaaparch for Estimation of ARFURIMA-APARCH Model and Big Data Analytics

This paper introduces the R package arfurimaaparch version 0.1.0 for time series computations, big data analytics and estimation of Autoregressive Fractional Unit Root Integral Moving Average-Asymmetric Power Autoregressive Conditional Heteroscedasticity (ARFURIMA-APARCH) model. The fdr, arfurimaaparch, arfurimaaparchforecast, arfurimaaparchdiagnostic and arfurimaaparch.sim are the main functions of the package. An improved version of the arfurima package version 1.1.0 of Jibrin and Rahman (2019) for implementing Monte Carlo simulation is also presented. Daily Nigeria all share index and West Texas Intermediate (WTI) crude oil prices for the period 26th January 2004 to 31st December 2018 were used to explained the usage of the packages. When the arfurimaaparch package is compared with other long memory packages, It would produce better stationary process after transformation, appropriate fractional differencing values in the interval of , minimum Akaike Information Criteria values, larger log-likelihood values, minimum p-values of the ARFURIMA-APARCH parameters estimates and large p-values of the Ljung-Box, ARCH-LM and Jarque-Bera test. Findings show that both R packages and their functions are robust, simple and user-friendly. As conclusion, the R packages are suitable, good and reliable for time series analysis computations, statistical analysis and big data analytics.

The Use of GARCH Autoregressive Models in Estimating and Forecasting the Crude Oil Volatility

Today, oil is one of the most popular commodities traded globally, due to its indispensable character and multiple properties offered to mankind. Increased attention is paid to the analysis of volatile and fluctuating trends in the overall price of this valuable energy source. Using the autoregressive conditional heteroskedasticity models such as GARCH(1,1), GARCH-M(1,1) and EGARCH(1,1), the present study has as a priority objective in estimating and predicting the volatility of the oil returns series (Brent Crude Oil return series) in the 1987-2022. The main results highlighted the preference in using the asymmetric model EGARCH (1,1) on the measurement of conditional variance, showing that Brent Crude Oil reacts over 90% to any existing market’s shock (i.e.: information, events, facts, news, etc.) in a negative manner/way. At the same time, various tests and evaluation conditions were used (ARCH-LM Test, Durbin-Waston Test, High Log likelihood, Lowest Schwarz Information Criteria) in investigating the level of performance in estimation the conditional crude oil volatility. Each GARCH (1,1) model is meeting brilliantly these conditions and acquiring the character of stability and validity in use. At the same time, performing forecast analysis on crude oil volatility in two different time periods: 1987-2022, respectively 2020-2022, it was shown that existence of the phenomenon of clustering-volatility over the time, with strong implications for the functioning mechanism of international financial markets. Fulfilling those restrictive conditions, the symmetric and parametric model GARCH-M (1,1) becomes, in our case, the most efficient model in forecasting the volatility of Brent Crude Oil return series in the analysed period.

Forcasting Stock Price PT. Indonesian Telecomunication with ARCH-GARCH Model

This research discusses the modeling of time series using R software, focusing on forecasting the stock price of PT. Indonesian telecommunications with ARCH-GARCH model. The data used daily closing data on stock prices from January 6, 2020, to January 6, 2021 was obtained from the website www.finance.yahoo.com. The goal is to find out the best model arch-garch on PT. Indonesian telecommunications to find out the results of stock price forecasting the next day using the ARCH-GARCH model. The best model was ARIMA (2,1,3). The results of the ARCH-LM test showed the data contained heteroskedasticity effects or ARCH elements. The research models proposed in this study are ARCH (1) and ARCH-GARCH (1,1). The smallest AIC and BIC values of these two models are ARCH-GARCH (1,1) which is the best model for forecasting the stock price of PT. Indonesian telecommunications for the next 10 days. The study attempts to conduct stock price forecasting with the ARCH-GARCH model. The result of the forecasting of the share price of PT. Indonesian telecommunications from January 07, 2021 to January 20, 2021 respectively except for holidays is IDR 3374.884, IDR 3379.617,IDR 3378.305, IDR 3376.610, IDR 3380.050, IDR 3376.372, IDR 3379.071, IDR 3377.964, IDR 3377.515, IDR 3379.002. Forecasting results are close to factual data for forecasting the next 10 days so that they can be taken into consideration in investing by investors.

Different GARCH model analysis on returns and volatility in Bitcoin

<abstract> <p>The aim of this study was to examine the returns and volatility of Bitcoin. The study uses the daily closing price of Bitcoin from October 1, 2013 to July 31, 2020 as the sample data, which include 2496 observations. About the methodology, the paper describes the utilisation of GARCH models to analyse Bitcoin's returns and volatility. First, the data were tested by using the augmented Dickey-Fuller test to verify the stability and diagram tests sequence. After that, the lag order and determination results of the mean value equation show that the Lag 4 period is the best. Additionally, the paper describes an autocorrelation test of the residual series, which revealed that there is no significant autocorrelation in the residual term for the Bitcoin returns, but that the residual squared has significant autocorrelation. In addition, a linear graph of squared residuals was formulated and the ARCH-LM test was used to find the data that are suitable for modelling with GARCH models since the data have a strong ARCH effect. As result, a GARCH (1, 1) model was used; the findings indicated that the returns and volatility of Bitcoin have clustering characteristics, and that the returns and volatility of Bitcoin constitute a persistent process although the effects gradually reduce over time. Because of the limitations of the GARCH (1, 1) model and researching asymmetry of the returns and volatility of Bitcoin, TARCH and EGARCH models were adopted; the findings indicated that the returns and volatility of Bitcoin are without a "leverage effect". To further explain this special phenomenon, safe-property is quoted in this research. In the end, this paper demonstrates that Bitcoin, as a safe-haven property, can hedge financial risks in times of economic depression. Besides, Bitcoin has a revised asymmetric effect between positive and negative shocks that makes it a viable asset to add to the portfolios of investors.</p> </abstract>

Modelling and Forecasting of Monthly Rainfall and Temperature Time Series Using SARIMA for Trend Detection- A Case Study of Umiam, Meghalaya (India)

Seasonal Auto Regressive Integrative Moving Average Models (SARIMA) were developed for monthly rainfall, mean monthly maximum and minimum temperature time series for Umiam (Barapani), Meghalaya (India). The best model was selected based on the minimum values of AIC and BIC criteria as well as based on observing the ACF and PACF plot of residuals. SARIMA (5,1,2) x (1,1,1)12, SARIMA (2,1,2) x (2,1,1)12, SARIMA (6,1,4) x (2,1,3)12 models were found to be the best fit model for the monthly rainfall, mean monthly maximum and minimum temperatures time series respectively. The adequacy of the SARIMA models was also verified using the Ljung-Box (Q) statistic test. McLeod-Li test and Engle’s ARCH LM test were carried out for residuals. The results indicated that there was no Arch effect in the established SARIMA models and models can be used for forecasting the future values for the year 2013 to 2028. The determination of trend in monthly rainfall, mean maximum and minimum temperatures in the forecasted series were done using different trend analysis techniques. For monthly rainfall and mean monthly minimum temperature time series, all the selected methods supported no significant trend. However, in the case of mean monthly maximum temperature time series, three selected methods supported falling trend.

The Dynamic Relationship Between Accidents, Drivers' Licensing and Automobile Registrations; A Vector Autoregression Perspective

Aims/ Objectives: The paper seeks to investigate the dynamic relationship between drivers licensing, vehicle registration, motorbike registration and accidents.Study Design: Cross-sectional study.Place and Duration of Study: The secondary data was collated on a monthly basis on Accidents, Driver license, Motor Registration and Vehicle Registration that spanned 9 years from January 2010 to December 2018 from the Upper East Regional Oce of the Drivers Vehicle and License Authority.
 Methodology: The data was analyzed using vector autoregression model to establish the dynamic relationship between the variables. The R and Eviews softwares were used in the analysis.Results: The ndings revealed that in the short-run and long-run neither Driver license, Vehicle Registration, Motor Registration none Accidents cannot in uence much on each other but experienced their own shock. Findings further ascertain that Accidents can granger cause vehicle registration to change but the remaining variable have no much in uence on accidents.Although, accidents can granger cause vehicle registration to change, the remaining variables had noin uence on accidents. The nding nally concluded that ARCH-LM test indicated that there was no ARCH eect present in the series implying that the Vector Autoregression model was appropriate to establish the dynamic relationship between the variables.

What Influences the Real Estate Price Volatility in Hong Kong: An ARMA-GARCH Analysis

In this paper, we apply the ARMA-GARCH model to Hong Kong real estate market. We analyzed the monthly data of housing, office retail and factories from February 1993 to February 2019. The result of ARCH LM test indicates that volatility clustering is shown in there four kinds of real estate. The price volatility of housing is influenced by foreign exchange rate, especially the USD exchange rate. The commercial real estate market shows different, they are all influenced by unemployment. All these real estate shows limited inflation hedging ability in a short period. The result of the EGARCH model shows there were no asymmetric effects in the real estate market.

TIME SERIES ANALYSIS OF RAINFALL USING HETEROSKEDASTICITY MODELS

Weather forecasting is predicting present state of weather by the help of analyzing collected data such as temperature, humidity, wind etc. to analyze atmospheric processes and determine how weather condition is going to change in future. Weather forecasting is important not only for prediction but also to prepare for future coming events if any such as cyclone, heavy rainfall, hails which can cause harm to the agricultural production of the country and hence affect the livelihood of the farmers. Continuous change in variance of time series over time; such a process is termed as Volatility. Heteroskecdascity refers to increasing variance in a way, such as increase in trend, this property of series is termed as Heteroskecdascity. Objective of the paper is to analyze, model and predict Rainfall time series of Delhi region from January 01, 2017 to December 31, 2018 using Heteroskecdascity model such as ARCH, GARCH, TARCH/ GJR-GARCH, EGARCH models to select most suited model on basis of probability value of the model hence calculated. Further, to analyze and choose model has full filled required conditions the model is checked for Serial Correlation, ARCH Effect, Normal distribution of Residuals, ARCH-LM test is applied, AIC, SIC values are calculated. GJR-GARCH model is most suited model among all models tested for modeling and analyzing rainfall. Model selection is done based on AIC value and SIC value calculated.

Econometric Modelling: Testing of Randomness,Volatility, Casualty and Cointegration of Emerging Stock Market Indices of India and MIST Countries

In general, stock market indices are widely interrelated to the other global markets to detect the impact of diversification opportunities. The present research paper empirically examines randomness and long term equilibrium affiliation amongst the emerging stock market of India and Mexico, Indonesia, South Korea and Turkey from the monthly time series data during February 2008 to October 2019. The researcher employs by the way, Run test, Pearson’s correlation test, Johnsen’s multivariate cointegration test, VECM and Granger causality test with reference to post-September 2008 Global financial crisis. The test results of the above finds that Nifty 50 and BSE Sensex is significantly cointegrated either among themselves or with MIST countries particularly during the post-September Global financial crisis. No random walk is found during the study period. The ADF (Augmented Dickey-Fuller) and PP (Phillips Pearson) tests evidenced stationarity at the level, but converted into non-stationarity in first difference. Symmetric and asymmetric volatility behaviors are studied using GARCH, EGARCH and TARCH models in order to test which model has the best forecasting ability. Leverage effect was apparent during the study period. So the influx of bad news has a bigger shock or blow on the conditional variance than the influx of good news. The residual diagnostic test (Correlogram-Squared residuals test, ARCH LM test and Jarque-Bera test) confirms GARCH (1,1) as the best suited model for estimating volatility andforecasting stock market index.

Analyzing Stock-Bond Correlation in Emerging Markets

This study investigates stock-bond correlation in 17 countries of emerging markets during 2011 to 2018 using monthly price data. Data was analyzed using ARCH-LM test, GJR GARCH and Multivariate GARCH type Asymmetric DCC model. Findings of this paper revealed that sequence of return series are stationary containing white noise error, past return volatilities do not have the ability to predict future volatilities and conditional volatility is higher and negative momentum of the market increase the correlation of stock and bond in a country or vice versa and hence increase the diversification benefit for asset allocation in a portfolio construction and provide hedging assets characteristics among countries and it is found that there is a co-movement between stock and bond in a country of emerging markets.

Analyzing Stock-Bond Correlation in Emerging Markets

This study investigated stock-bond correlation in 17 countries of emerging markets (i.e. Czech Republic, Egypt, Greece, Hungary, Poland, Russia, Turkey, Israel, China, India, Indonesia, South Korea, Malaysia, Pakistan, Philippines, Taiwan, Thailand) during 2011 to 2018 using monthly price data. Data were analyzed using the ARCH-LM test, GJR GARCH and Multivariate GARCH type Asymmetric DCC model. Finding of this paper revealed that sequence of return series are stationary contains white noise error, past return volatilities does not have the ability to predict future volatilities and conditional volatility is higher and negative momentum of the market increase the correlation of stock and bond in a country or vice versa and hence increase the diversification benefit for asset allocation in a portfolio construction and provide hedging assets characteristics among countries and it is verified that there is a co-movement between stock and bond in a country of emerging markets.