Trading Activities and the Volatility of Return on Malaysian Crude Palm Oil Futures

Trading activities represent the flow of market information to the investors. This paper examines the effect of trading activities, i.e., trading volume and open interest, on the volatility of return for Malaysian Crude Palm Oil Futures. The GARCH model is applied by adding the expected and unexpected elements of trading activities (trading volume and open interest) as the independent variables. The results show that there is a negative contemporaneous relationship between the expected volume and volatility, but that a positive relationship exists between unexpected volume and volatility. On the contrary, the expected and unexpected open interest mitigate the volatility. Therefore, both trading volume and open interest should be considered together when information flows into the market.

Comparison of GARCH & ARMA Models to Forecasting Exchange Rate

Financial time series are defined by their fluctuations, which are characterized by instability or uncertainty, implying that there are periods of volatility followed by periods of relative calm. Therefore, time series analysis requires homogeneity of variance. In this paper, some models used in time series analysis have been studied and applied. Comparison between Autoregressive Moving Average (ARMA) and Generalized Autoregressive Conditionally Heteroscedastic (GARCH) models to identify the efficient model through (MAE, MASE) measures to determine the best forecasting model is studied. The findings show that the models of Generalised Autoregressive Conditional Heteroscedastic are more efficient in forecasting time series of financial. In addition, the GARCH model (1,1) is the best to forecasting exchange rate.

Predicting standardized absolute returns using rolling-sample textual modelling

Understanding how textual information impacts financial market volatility has been one of the growing topics in financial econometric research. In this paper, we aim to examine the relationship between the volatility measure that is extracted from GARCH modelling and textual news information both publicly available and from subscription, and the performances of the two datasets are compared. We utilize a latent Dirichlet allocation method to capture the dynamic features of the textual data overtime by summarizing their statistical outputs, such as topic distributions in documents and word distributions in topics. In addition, we transform various measures representing the popularity and diversity of topics to form predictors for a rolling regression model to assess the usefulness of textual information. The proposed method captures the statistical properties of textual information over different time periods and its performance is evaluated in an out-of-sample analysis. Our results show that the topic measures are more useful for predicting our volatility proxy, the unexplained variance from the GARCH model than the simple moving average. The finding indicates that our method is helpful in extracting significant textual information to improve the prediction of stock market volatility.

Liquidity effects on oil volatility forecasting: From fintech perspective

Fin-tech is an emerging field, inspiring revolutionary innovations in the financial field. It may initiate the evolutionary episode of the financial research, where volatility forecasting is a crucial topic in finance. For forecasting volatility, GARCH model is a prevailing model, however, further improvement of the GARCH model is still challenging. In this paper, we demonstrate how Fintech can play a part in volatility forecasting by employing a metaheuristic procedure called Genetic Programming. On the basis, we are able to develop a new volatility forecasting model, which can beat GARCH family models (including GARCH, IGARCH and TGARCH models) in a significant way. Since genetic programming is an evolutionary algorithm based on the principles of natural selection, this innovative work will be a breakthrough point in the financial area. The innovation of this paper demonstrates how GP technology can be applied in the financial field, attempting to explore the volatility forecasting area from the combination of new technology and finance, known as fintech. More importantly, when the formula of volatility forecasting is unknown as we introduce a new factor, namely, the liquidity factor, we unveil that how GP method can be helpful in determining the specific volatility forecasting model format. We thereby exhibit the liquidity effects on volatility forecasting filed from the fintech perspective.

Modelling the Efficiency of TGARCH Model in Nigeria Inflation Rate

In general, volatility is known and referred to as variance and it is a degree of spread of a random variable from its mean value. Two volatility models were considered in this paperwork. Nigeria's inflation rate was modeled by applying the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and Threshold GARCH models. Symmetric and asymmetric models captured the most commonly stylized facts about the rate of inflation in Nigeria like leverage effects and irregularities in clustering and were studied. These models are GARCH (1,1) and TGARCH (1,1). This work estimated the comparison of volatility models in term of best fit and forecasting. The result showed that TGARCH (1,1) model outperformed GARCH (1,1) models in term of best fit, because it has the least AIC of 2.590438. We forecasted to see the level of volatility using Theils Inequality Coefficient and the result shows that TGARCH has the highest Theils Inequality Coefficient of 0.065075 which makes the TGARCH model better than the GARCH model in this research. From the initial and modified sample static forecast, it was discovered that the return on inflation is stable and shows that volatility slows towards the end of the month, we can see a downward spiral, which means price reaction to economic crisis led to lower production, lower wages, decreased demand, and still lower prices.

Stock price volatility during the COVID-19 pandemic: The GARCH model

This study examined the response of stock prices on the Indonesia Stock Exchange (IDX) to COVID-19 using an event study approach and the GARCH model. The research sample is the closing price of the Composite Stock Price Index (JCI) and companies that are members of LQ-45 in the 40-day period before the COVID-19 incident, 1 day during the COVID-19 incident (March 2, 2020) and 10 days after, January 6, 2020 – March 16, 2020. Empirical findings prove that abnormal returns react negatively to COVID-19, JCI volatility fluctuates widely during the COVID-19 event, and the GARCH(1,2) model can be used to assess volatility and predict stock abnormal returns in IDX in market conditions infected with COVID-19. The practical implication of the study’s findings for investors is that the COVID-19 event caused stock price volatility, which affects abnormal returns. Therefore, to face the conditions of uncertainty and increased volatility in the future, several lines of risk management are needed in managing a stock portfolio. In addition, it also opens up opportunities for speculators to profit in an inefficient market environment. This study is based on the empirical literature currently being developed to investigate the phenomenon of stock price volatility behavior during COVID-19 on the IDX. The GARCH model used proves that during the COVID-19 pandemic, stock price volatility increases and leads to a decrease in abnormal returns. The empirical findings also validate the efficient market hypothesis theory related to the study of events and the theory of financial behavior related to uncertainty.

Volatility of Technology and Healthcare Sectors Before and During Covid-19 Pandemic

The study investigates the effect of the Covid-19 on the volatility of the technology and healthcare sector stock index in Malaysia. The two sectors pose considerable attention during the pandemic due to the increase in demand for healthcare products and digital services. The volatilities are estimated using the GARCH model for the period before and after the implementation of the nationwide movement order control using daily data from September 2019 to September 2020. The finding shows that the Covid-19 pandemic caused a volatility jump for the technology sector index in March 2020 but subsided afterward with estimated conditional volatility revert to normal in the middle of April 2020. However, during the high uncertainty period, the healthcare sector shows a steady increase in volatility beginning in March 2020 till the end of September 2020. The study confirms that there is a significant difference in the volatility of healthcare and technology sectors before and during the Covid-19 outbreak. The outbreak has a significant impact on increasing the volatilities for both sectors but is impacted in different magnitude.

GJR-GARCH Volatility Modeling under NIG and ANN for Predicting Top Cryptocurrencies

Cryptocurrencies are currently traded worldwide, with hundreds of different currencies in existence and even more on the way. This study implements some statistical and machine learning approaches for cryptocurrency investments. First, we implement GJR-GARCH over the GARCH model to estimate the volatility of ten popular cryptocurrencies based on market capitalization: Bitcoin, Bitcoin Cash, Bitcoin SV, Chainlink, EOS, Ethereum, Litecoin, TETHER, Tezos, and XRP. Then, we use Monte Carlo simulations to generate the conditional variance of the cryptocurrencies using the GJR-GARCH model, and calculate the value at risk (VaR) of the simulations. We also estimate the tail-risk using VaR backtesting. Finally, we use an artificial neural network (ANN) for predicting the prices of the ten cryptocurrencies. The graphical analysis and mean square errors (MSEs) from the ANN models confirmed that the predicted prices are close to the market prices. For some cryptocurrencies, the ANN models perform better than traditional ARIMA models.

Cryptocurrency Safe Haven Property against Indonesian Stock Market During COVID-19

Safe-haven assets conserve their value or grow against another asset or portfolioduring market turmoil. Indonesian stock market, represented by the Jakarta composite index (JKSE), plunged in price because of COVID-19, pushing investors to look for safe-havens. The cryptocurrency began to be perceived as a store of value as indicated by the transaction volume increase; hence it was expected to be a safe haven asset. However, cryptocurrency’s high price volatility cast doubts on its store of value effectiveness, prompting inspection for its safe haven property as well. This research aimed to predict the assets' risk and return plus investigate whether cryptocurrency is safe haven assets against the Indonesian stock market during COVID- 19. Daily closing prices of JKSE, Bitcoin, Ethereum, Litecoin, and Ripple were used, then the GARCH model was implemented in the forecasting. DCC-GARCH model, followed by dummy variable regression, will be applied to the return data to evaluate the safe haven property. The prediction projected Bitcoin as the most profitable asset andRipple as the riskiest. The analysis and robustness test suggested that none of these cryptocurrencies were safe haven assets during the whole observation. This indicates that investors who intend to seek safe haven investments were advised against investing in these cryptocurrencies.