GARCH Method Research Articles

VOLATILITIY OF WORLD FOOD COMMODITY PRICES AND RENEWABLE FUEL STANDARD POLICY

Biofuel as an alternative fuel which in recent years has become the world's demand for renewable energy fuels and is a major factor underlying the increase in prices of agricultural commodities, especially corn as the main commodity for biofuels in the US. The purpose of this study is to examine the volatility of several world food commodities, namely rice, wheat, corn, and soybeans and to analyze the impact of biofuel development on the price volatility of some world food commodities. This research uses coefficient of variation and volatility analysis with the ARCH GARCH method. Based on the results, the price of food commodities is more volatile after the adoption of the Renewable Fuel Standard 2 (RFS) policy in 2007. The results of the study show that development of biofuels (from corn and soybeans) have a higher level of volatility than the other two commodities (rice and wheat) due to the variance of rice and wheat was lower than corn and soybeans. It means that the greater the relevance of a commodity to the development of biofuels, the higher the price volatility of the commodity. Meanwhile, the expansion of biofuels also made US reduce the amount corn and soybean exports, of course this is what makes the supply of US corn and soybeans decreased and makes the price will increase.

Volatility Spillover Between Stock Market Sectors

In this study, volatility spillover and volatility relationship of Istanbul Stock Exchange (BIST) basic markets were investigated by using daily data between 4 January 2010 and 28 August 2019. Volatility spillover between industry, trade, service and financial sectors were examined by causality test in the variance Hafner and Herwartz (2006), and inter-sector relations were examined by DCC GARCH method. In this study, dynamic conditional correlation between BIST core markets and volatility spillover from industry, trade and service sector to financial sector were observed. In addition, it has been determined that the relationship between the major sectors of BIST varies with time and that economic and political events partially determine these changes. It has been understood that the relationship between financial-industry, service-trade, financial-service and service-industry sectors is higher than the relationship between trade-financial and industry-trade sectors. According to these results, it is not possible to reduce risk through diversification based on BIST core markets. The results provide guidance for investors, risk managers and portfolio managers.

Cryptocurrencies value‐at‐risk and expected shortfall: Do regime‐switching volatility models improve forecasting?

AbstractThis paper evaluates the presence of regime changes in the log‐returns volatility dynamics of cryptocurrencies using Markov‐Switching GARCH (MS‐GARCH) models. The empirical study compares the prediction performance of MS‐GARCH against traditional single‐regime GARCH methods for one‐, five‐ and ten‐steps‐ahead volatility forecasting of six leading digital coins such as Bitcoin, Dashcoin, Ethereum, Litecoin, Monero and Ripple. Using a Bayesian approach, different MS‐GARCH structures are estimated considering specifications up to three regimes, three scedastic functions and six error distributions, resulting in a total of 54 models for each cryptocurrency. Forecasts are compared according to an economic criterion, that is, through the estimation of Value‐at‐Risk (VaR) and Expected Shortfall (ES) risk measures. The results support the evidence of regime changes in the volatility process of selected cryptocurrencies and show that MS‐GARCH models do provide more accurate VaR and ES forecasts than their single‐regime counterparts.

The correlation of gold, exchange rate, and stock market on Covid-19 pandemic period

This study aims to analyze the correlation of gold, exchange rate, and CSPI on COVID-19 pandemic periods by testing the effect of gold exchange prices and exchange rate on CSPI and stock volatility. Also, by considering the dynamic correlation of dynamic correlations between CSPI with gold and CSPI with exchange rates. The data was collected from secondary data in the form of JCI daily data, gold prices, and exchange rate during the COVID-19 pandemic period from January 2020 to June 2020. Further, the data was analyzed by using a GARCH method to examine the effect of changes in gold and USD prices for CSPI and stock volatility. Hence, DCC-GARCH method was used to see the dynamic correlation between CSPI with gold and IHSG with exchange rate. The result showed that changes of gold prices has significant effect of on stock price volatility, the presence of a positive dynamic correlation between CSPI and gold, and a negative dynamic correlation between CSPI and exchange rates. This research can be used as a reference for investors for their investments by looking at the relationship between the CSPI, gold, and the exchange rate. JEL Classification: G10, G11, G12 DOI : https://doi.org/10.26905/jkdp.v24i3.4621

Does the Energy-Food Nexus still exist: Empirical Evidence from the Indian Agriculture Sector post food crisis of 2006

The present study made an attempt to empirically investigate the impact of crude oil prices on agricultural prices for India during the calendar decade 2010-19. The bio-fuels which were considered in the present study included soy-oil, rapeseed oil, sunflower oil and palm-oil. The study covered a period of post food crisis of 2006 and after three years of stabilization of these prices to get an insight into the current scenario. The econometric tools used to examine the relationship included Johansen Co-integration, Gregory Hansen Co-integration with structural breaks, Toda and Yamamoto Causality and GARCH (1,1) Method. The results of our study did not give sufficient evidence to prove that crude oils impacted any of the bio-fuel prices in current scenario. The long run inter-linkages between crude oil and energy crops was not established which brought us to the conclusion that the energy-food nexus was a temporary phenomenon that existed during food crisis (2006) only and crude prices were no longer a guiding force to determine the price of bio-fuels

Analyzing Crude Oil Prices under the Impact of COVID-19 by Using LSTARGARCHLSTM

Under the influence of the COVID-19 pandemic and the concurrent oil conflict between Russia and Saudi Arabia, oil prices have exhibited unusual and sudden changes. For this reason, the volatilities of the West Texas Intermediate (WTI), Brent and Dubai crude daily oil price data between 29 May 2006 and 31 March 2020 are analysed. Firstly, the presence of chaotic and nonlinear behaviour in the oil prices during the pandemic and the concurrent conflict is investigated by using the Shanon Entropy and Lyapunov exponent tests. The tests show that the oil prices exhibit chaotic behavior. Additionally, the current paper proposes a new hybrid modelling technique derived from the LSTARGARCH (Logistic Smooth Transition Autoregressive Generalised Autoregressive Conditional Heteroskedasticity) model and LSTM (long-short term memory) method to analyse the volatility of oil prices. In the proposed LSTARGARCHLSTM method, GARCH modelling is applied to the crude oil prices in two regimes, where regime transitions are governed with an LSTAR-type smooth transition in both the conditional mean and the conditional variance. Separating the data into two regimes allows the efficient LSTM forecaster to adapt to and exploit the different statistical characteristics and ARCH and GARCH effects in each of the two regimes and yield better prediction performance over the case of its application to all the data. A comparison of our proposed method with the GARCH and LSTARGARCH methods for crude oil price data reveals that our proposed method achieves improved forecasting performance over the others in terms of RMSE (Root Mean Square Error) and MAE (Mean Absolute Error) in the face of the chaotic structure of oil prices.

The Impact of Exchange Rate volatility on Banking Performance (case of Iran)

Banks play an important role in the Iranian economy which has a bank-based financial system. We examined the impact of exchange rate volatility as a determinant of banks' performance. In recent years, the exchange rate has been volatile in the Iranian economy and have an adverse effect on banks' performance. This study, investigate the issue for the period 2007-2017 for 14 Iranian banks. Exchange rate fluctuations are derived by GARCH method and the effect of its fluctuations on bank performance examined using panel data method. In order to evaluate banks' performance, we used two criteria, namely liquidity and profitability. Estimation of econometric model using panel data by random effects indicated that exchange rate volatility has a negative and statistically significant effect on banks' capital return ratio. Exchange rate volatility is also a determinant in increasing the ratio of lending to total bank deposits, as it increases the financial gap and creates the credit risk that the gap entails. JEL: N10, G01, E44, C22.

Does the Energy-Food Nexus Still Exist: Empirical Evidence from the Indian Agriculture Sector Post Food Crisis of 2006

The present study made an attempt to empirically investigate the impact of crude oil prices on agricultural prices for India during the calendar decade 2010-19. The bio-fuels which were considered in the present study included soy-oil, rapeseed oil, sunflower oil and palm-oil. The study covered a period of post food crisis of 2006 and after three years of stabilization of these prices to get an insight into the current scenario. The econometric tools used to examine the relationship included Johansen Co-integration, Gregory Hansen Co-integration with structural breaks, Toda and Yamamoto Causality and GARCH (1,1) Method. The results of our study did not give sufficient evidence to prove that crude oils impacted any of the bio-fuel prices in current scenario. The long run inter-linkages between crude oil and energy crops was not established which brought us to the conclusion that the energy-food nexus was a temporary phenomenon that existed during food crisis (2006) only and crude prices were no longer a guiding force to determine the price of bio-fuels

The Dynamics of Exchange Rate and Stock Return Before and After the Fed Policy Normalization: Evidence from Fragile Five Countries

Abstract: This research aims to examine the correlation between exchange rate and stock price return in each fragile five countries; Indonesia, Brazil, India, Turkey and South Africa. Using daily data, we investigate and then divide it into two periods; before Fed funds rate normalization (2013-2015) and after normalization (2016-2018), to find out whether the Fed funds rate hike caused a difference in the correlation between the two variables in each fragile five country. The methods used for this analysis are granger causality test and Vector Autoregression (VAR) using Eviews 9 program. Further investigation by analyzing the Dynamic Conditional Correlation-Multivariate GARCH (DCC MGARCH) method using Stata 15 program, which aims to find out the dynamic correlation between stock markets and also between currencies in fragile five countries. Granger test results found a difference in the relationship between variable exchange rates and stock price returns in Indonesia, India, and Turkey after the Fed normalization. Additionally, we learn that exchange rate lead stock price return in these three countries. Furthermore, the results of the DCC MGARCH test show that there is a significant positive dynamic correlation on the stock price index returns between markets. Moreover, we found similar results in testing positive and significant dynamic correlations between the exchange rates of each country.
 Key words: fragile five, exchange rate, stock return, VAR, DCC MGARCH

HERDING BEHAVIOR IN THE INDONESIAN ISLAMIC STOCK MARKET

Indonesia Stock Exchange provides Islamic stocks for Muslim investors who want toinvest, with the first Islamic stock index in Indonesia being Jakarta Islamic Index or JIIthat consists of thirty of the most liquid Islamic stocks. The market capitalization of JIItends to increase every year. This paper examines the presence of herding behavior inemerging Islamic stock market of Indonesia using daily return of Indonesia CompositeIndex and JII from October 6, 2000 to October 5, 2018. Herding behavior could generallytrigger shifting market prices from equilibrium values. Herding behavior may beidentified from the relation between stock return dispersion and market return. Stockreturn dispersion is measured using Cross Sectional Absolute Deviation or CSAD.Generalized Auto Regressive Conditional Heteroskedasticity or GARCH method isused to detect herding behavior. GARCH does not see heteroskedasticity as a problem,instead uses it to make a model. The result indicates that herding behavior exist inIslamic stock market of Indonesia. Asymmetric herding occurs in Indonesia Islamicstock market where herding behavior exists during falling market condition only.

Trader differences in Shanghai’s A-share and B-share markets: Effects on interaction with the Shanghai housing market

This study analyzes the relationship between the second-hand house price index of Shanghai and Shanghai stock market indices distinguishing between the A-share market, in which Chinese investors predominate, and the B-share market, in which foreign investors predominate. Monthly data for the period April 2003 to June 2018 are analyzed using VAR and GARCH methods. The A-share market shows little covariance with the housing market, whereas the B-share market generally shows a negative covariance, indicating the potential for B-shares to provide risk diversification for Shanghai house owners. Using VAR estimation, a positive shock to the housing market is found to have a negative effect on the A-share market contrary to a positive such credit-price effect between stocks and house prices established for developed countries, perhaps due to a crowding-out of rising house prices on share purchase. Using GARCH estimation, however, the negative credit-price effect is discerned to apply only for negative house price shocks, suggesting falling housing prices allow more funds to be diverted to the share market. While the A-share market shows causality in only one direction from house prices to share prices, causality between the B-share and housing markets is two-way. This is consistent with the less restricted B-share market affording better information transmission.

Volatility and Market Integration of Spot-Forward Corn Price in Indonesia

This study aims to determine the volatility and market integration between the price of corn in the Indonesian spot market and futures market in the international market. The data used in this research is secondary data consisting of Indonesian corn spot price and corn forward price referring commodity exchange, Chicago. Data in the form of monthly time series in 2007 until 2016. ARCH / GARCH method is used to measure the volatility at spot and forward price, whereas the market integration of spot and forward corn is used Johansen Cointegration and Engel-Granger Causality method. The results show that spot and forward prices of corn occur high volatility. The best ARCH/GARCH model for spot price is GARCH (2,0) with the volatility value of 0,91 and for forward price is GARCH (2,0) with the volatility value of 1.12. It means that volatility of spot and forward influenced by the increase and fluctuations of spot and forward price two previous periods. Between the spot and forward market, there is market integration and a one-way causal relationship. The market integration indicates there is long-run relationship, while one way indicates the spot price effect on the forward price, not vice versa.

FINANCIAL VULNERABILITY AND INCOME INEQUALITY: NEW EVIDENCE FROM OECD COUNTRIES

This study explores, for the first time, how financial vulnerability affects incomeinequality across OECD countries, from 1990 to 2015. The empirics use a new financialvulnerability index constructed by Adrian and Duarte (2016). Through the methodologyof their modeling approach, panel GARCH and GMM methods, the findings indicatethat financial vulnerability exerts a negative impact on income equality conditions.The results survive certain definitions of income inequality and corruption, whilethey highlight the importance of financial stability conditions, with potential furtherrepercussions to the real economy.

DESIGN, CONSTRUCTION AND STUDY OF THE EFFICIENCY OF GREEN PANELS WITH A MONITORING SYSTEM FOR PHYSICAL VARIABLES

The present work had some challenges such as to design and then construct an electronic system for monitoring the values of a select group of physical variables in a prototype of a green panel, the selected variables were temperature, relative humidity, soil humidity, mechanic and telluric vibrations and quantity of light, then study their behaviors of these variables mentioned before. To get the system ready for data acquisition and saving the data, it became necessary the construction of an electronically arrangement that understands; Open Source code, diverse types of electronic sensors and a free hardware device named “Arduino Uno board”. The prototype of a green panel in which we installed the electronic system with sensors that recorded the changes in an automatic way, then they were sent to a computer connected to the system with an USB wire for capturing data in Microsoft Excel, with an interval defined by Open Source Code, constructing a database, after 44 days of monitoring, for processing the data we occupied the software “R studio” of Open Source for continuing the study with a statistical analysis giving the central tendency measures, graphs, histograms, density curves and proving the correlation between variables with correlation coefficient (R) obtained by “Pearson” methodology. The study of time series and for developing linear and non-linear equations were done with the least squares’ methodology and GARCH method, running a simple regression, that included: logarithmic terms, quadratic terms, first differences, “dummies” and constants, in this case the statistical suites of choice were primarily E views, secondarily Minitab, SPSS, SAS and STATA. At the end of the regression, the results were in favor for the implementation of green panels in buildings, because they can bring several benefits to the environment. The conclusion was that the experiment generated atmospheric conditions that reduced in 8 °C of atmospheric temperature and the relative humidity in air is augmented in 15% and reduces carbon dioxide and carbon monoxide and the diminution of the “Urban Heat Island” effect in weather for a determined green area. KEYWORDS: Open Source Code, Arduino, Green Panels, Monitoring. MSC: 68U99

Determining the Change in Retail Prices of Veal in Turkey by GARCH Method Between 2014-2017

Determining the Change in Retail Prices of Veal in Turkey by GARCH Method Between 2014-2017

Risk Exposition of Prices in Agricultural Commodities Using Options and Futures

Changes and fluctuations in commodity prices exert different effects on value chain participants, depending on the position they have in the chain. Agricultural commodities are exposed to a set of different factors influencing the prices of the commodities. They are influenced by the season, weather shocks, demand and supply forces, household income, tastes and preferences of the consumers. Observing the most recent history, high price fluctuations have been observed during the financial crisis in 2008. One out of many approaches for hedging the price risk is the usage of financial derivatives. This study will be concerned with the volatility modelling methods with the help of futures and options for corn and soya. Methods used for modelling the volatility are GARCH and Black Scholes Implied Volatility. The simplest method in ARCH family, namely the GARCH (1,1) method will be used for modelling volatility based on the historical futures data dating back to 2005. The implied volatility is derived solving back the Black – Scholes Model, only this time looking for sigma. The sole purpose of the thesis is to examine which of the two methods has a better predictive power. Model comparison is done with the help of forecast regression models. The regression models have shown the difficulty in assessing which model has more accurate predictive power. The Adjusted R2 for both models in both cases is relatively low. However, the GARCH (1,1) model has slightly higher values for this indicator. Even the GARCH (1,1) model has shown a better performance, due to the relatively low adjusted R2 values, no stable conclusion regarding the model performance can be derived.

Volatility Spillover In Stock And Commodity Futures Market: Empirical Analysis In Indonesia’s Financial Market

Volatility spillover between stock markets causes insignificancy of diversification. Therefore, other investment alternatives is required to build an optimal portfolio, one of them being commodity futures. The low correlation between commodity futures and stocks indicates the advantage of diversification in investment portfolio containing both assets. In order to prove the advantage of diversification, author tested the existence of volatility spillover during September 16, 2010 - September 30, 2015. Estimation result using GARCH method indicates the presence of significant volatility spillover from stock exchange to commodity futures exchange. An important implication of this finding is that if the sectoral stock index and commodity futures are incorporated into an investment portfolio, the investor will not have optimal diversification advantage. This is because there is a correlation between performance of both markets as a result of both markets having the same characteristics in response to the shock that is coming.

Price Change, Volatility, and Accurate VaR: Evidence from the NSW and QLD Power Markets

This article investigates the relationship among price changes, volatility forecasts, and accurate Value-at-Risk (VaR) estimates in power markets. The authors use various GARCH(p,q) methods to model daily volatility and estimate VaR in New South Wales (NSW) and Queensland (QLD) regions. Then, they explore the relationship among price changes, and volatility forecasts and accurate VaRs for both generator and retailer sides, at 99 percent, 95 percent and 90 percent VaR confidence levels in six different out-of-sample periods. They find that forecasted volatility and accurate VaR variables tend to be statistically significant in explaining variations in power price changes. They also find that the coefficients of forecasted volatility tend to be negative. This indicates that higher forecasted volatility predicts negative price changes. Therefore, market participants in power markets, either generator or retailer, should expect a negative price change when their volatility forecast is high. Furthermore, the authors find that accurate VaR tends to lead to a decrease in price changes. This implies that well-functioning market risk management explains an unfavorable price change for the generator, but a favorable price change for the retailer in the Australian power markets.

Volatility Spillover Analysis Post Implementation of AEC 2015 Agreement: Empirical Study on ASEAN-5 Stock Market

Efforts to improve financial integration that continue to be implemented after the implementation of the Asean Economic Community 2015 agreement, can encourage increased integration of capital markets in countries within the region. This study was conducted to investigate the spillover of volatility between stock markets that accompanied the ongoing efforts of financial integration carried out by ASEAN member countries. Investigation of volatility spillover is done by applying Exponential GARCH method on time series daily data of stock return of ASEAN-5 countries period September 2016 - December 20, 2017. If previous studies found significant spillover of volatility from Singapore, Malaysia, Thailand and Philippines, the results of this study show that only Singapore's stock exchanges consistently have a significant impact on the Indonesian stock market. The turmoil in the Singapore stock market will be consistently transmitted to the Indonesian stock market. However, efforts to improve the financial integration carried out by ASEAN member countries have not consistently caused the turmoil in Malaysia, Thailand and the Philippines stock exchange to be transmitted to the Indonesian stock market.

Measuring and Comparing the Value-at-Risk Using GARCH and CARsR Models for CSI 300 Index

This article is the first to provide a detailed method for range-based CARR model to estimate the VaR and its out-of-sample prediction. In this paper, we use GARCH and CARR volatility models to compare the VaR’s out-of-sample forecasting performance. Using the historical simulation method as benchmark for VaR estimation, we found that the historical simulation approach for VaR measurement is more conservative than GARCH and CARR methods. The mean violation rate for the CARR VaRs is lower than that of the GARCH VaRs. Meanwhile, the CARR VaR is able to deliver lower required capital levels without producing bigger violations. This paper argued that the CARR VaR valuation approach is suitable as an internal model method for financial institution in VaR forecasting.