Overestimation in the Traditional GARCH Model During Jump Periods

The traditional continuous and smooth models, like the GARCH model, may fail to capture extreme returns volatility. Therefore, this study applies the bivariate poisson (CBP)-GARCH model to study jump dynamics in price volatility of crude oil and heating oil during the past 20 years. The empirical results indicate that the variance and covariance of the GARCH and CBP-GARCH models were found to be similar in low jump intensity periods and to diverge during jump events. Significant overestimations occur during high jump time periods in the GARCH model because of assumptions of continuity, and easily leading to excessive hedging and overly measuring risk. Nevertheless, in the CBP-GARCH model, the specific shocks are assumed to be independent of normal volatility and to reduce the persistence of abnormal volatility. Therefore, the CBP-GARCH model is appropriate and necessary in high volatility markets.Jumps Overestimation Volatility CBP-GARCH model

MODELLING AND FORECASTING STOCK MARKET VOLATILITY OF NASDAQ COMPOSITE INDEX

On the NASDAQ Composite Index from March 1971 to April 2019 it appears that the data is not stationary. For this reason, differentiation is needed by finding the value of stock returns from the NASDAQ Composite Index data from March 1971 to April 2019. After differentiating by looking for return values, the next analysis can be done, namely looking for the ARIMA model. Finding an ARIMA model using conventional analysis will require a long analysis time. So to shorten the analysis process using the EViews 10 statistical program. The results obtained after using the EViews program are getting the ARIMA model (8.0,6). The ARIMA model (8,0,6) was chosen because it has the smallest AIC value of 12,664073. This can be used as a reference later that the ARIMA model (8.0,6) is the best model in conducting forecasting. After that, the GARCH model is continued which aims to determine the ARIMA-GARCH model combination model. From the results of the analysis, it is known that the best model for forecasting the return value of the NASDAQ Composite Index is a combination of ARIMA (8.0,6)-EGARCH (1,1) models, which from the results of this analysis are known for fluctuating return values and index values for NASDAQ for one year in the future it is stagnant and does not show a trend

Forecasting Stock Market Volitility- Evidence From Muscat Security Market Using Garch Models

Engle (1982) introduced the autoregressive conditionally heteroskedastic model for quantifying the conditional volatility and by Boollerslev (1986), Engle, Lilien and Robins (1987) and Glosten, Jaganathan and Runkle (1993) extended the class asymmetric model. Amongst many others, Bollerslev, Chou and Kroner (1992) or (1994) are considered to be the précis of ARCH family models. In this direction the paper forecasts the stock market volatility of four actively trading indices from Muscat security market by using daily observations of indices over the period of January 2001 to November 2015 using GARCH(1,1), EGARCH(1,1) and TGARCH (1,1) models. The study reveals the positive relationship between risk and return. The analysis exhibits that the volatility shocks are quite persistent. Further the asymmetric GARCH models find a significance evidence of asymmetry in stock returns. The study discloses that the volatility is highly persistent and there is asymmetrical relationship between return shocks and volatility adjustments and the leverage effect is found across all flour indices. Hence the investors are advised to formulate investment strategies by analyzing recent and historical news and forecast the future market movement while selecting portfolio for efficient management of financial risks to reap benefit in the stock market

Impact of crude oil volatility on stock returns: Evidence from Southeast Asian markets

The study investigates the connection between international oil indices and Southeast Asian stock markets. The outcomes of both employed models, namely EGARCH and GARCH-jump, confirm the significant oil-stock linkage in Southeast Asian region. While the oil price fluctuations have positive effect on stock returns, the impacts of the implied crude oil volatility index (OVX) are negative, implying that the increase in level of future oil prices uncertainty leads to downward movement on stock markets. This association is relatively stronger in crisis period and symmetric in most markets, except for Malaysia and Philippines. The research also finds a relatively weak volatility transmission from oil market to the stock returns after controlling for the impact of the implied volatility index (VIX). Additionally, the study further reports the existence of GARCH effects in Southeast Asian stock markets. Besides, the results from EGARCH models illustrate that the previously negative shocks seem to have greater effects on the current volatility of stock returns in analyzed countries than the positive shocks. Furthermore, the jump effects are found in most markets, as evidenced by the estimates for GARCH-jump models. Generally, the volatility driven by abnormal information positively affects the volatility of return while the jump behavior has negative impact on return in Southeast Asian markets. Providing greater understandings about new markets in Southeast Asian area, the research could be utilized in improving investment decisions and gaining the advantages of international portfolio diversification.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Modeling and Forecasting the Volatility of the Export Price of Sesame in Ethiopia

The aim of this study is to model the export price of sesame as well as its volatility in Ethiopia using ARIMA and GARCH family models. The data used are monthly observations of the export price of sesame, food price index, fuel oil price and exchange rate from January 1998 to June 2013.Unit root tests of the series under study reveal that all the series are non-stationary at level and stationary after first difference. ARIMA and GARCH models were employed to analyze the monthly export price of sesame data. It was found that ARIMA(0,1,1) and ARMA(2,2)-GARCH(2,1) with normal distributional assumption for the residuals were adequate models for the data considered in this study. Among the exogenous variable that are considered in this study, food price index had an impact on the volatility of the export price of sesame in Ethiopia.Finally, various forecast accuracy statistics indicate that the estimated ARIMA model is good enough to describe the export price of sesame. Moreover, the out-of-sample forecasts indicate that the export price of sesame has an increasing trend. The in-sample forecast using the best-fit GARCH model indicates that the export price volatility of sesame steadily increased at the beginning of the study period, remained at almost a constant level till 2007 and then exhibited a downward trend around the end of the study period. Keywords: Sesame, ARIMA, GARCH, Forecasting, Ethiopi

A comparison of Spillover Effects before, during and after the 2008 Financial Crisis

This paper applies graphical modelling to the S&P 500, Nikkei 225 and FTSE 100 stock market indices to trace the spillover of returns and volatility between these three major world stock market indices before, during and after the 2008 financial crisis. We find that the depth of market integration changed significantly between the pre-crisis period and the crisis and post- crisis period. Graphical models of both return and volatility spillovers are presented for each period. We conclude that graphical models are a useful tool in the analysis of multivariate time series where tracing the flow of causality is important.Volatility spillover; graphical modelling; financial crisis; causality

Structural breaks and financial volatility: Lessons from BRIC countries

Despite the fact that there is a substantial literature on the analysis of volatility spillovers between stock returns and domestic exchange rates, surprisingly, little empirical research has examined volatility spillovers between oil prices and emerging economies, where a clear gap of research have been found regarding to the BRIC financial markets and the effects of the 2007-2009 World economy crisis. This lack of research might appear as surprising given that energy markets are of particular interest as they are considered a fundamental reference for economic recovery and growth. Therefore, this work aims to address this gap on the literature by looking at the BRIC financial markets and their co-movements with regard to some energy markets (oil, natural gas and electricity) and also to the international pressures that may arise from fluctuations originated in the US stock markets. This research major findings show compelling evidence highlighting the weak integration levels that exist among the Chinese financial markets, energy markets and the US stock market. On the other hand, the Brazilian, Indian and Russian markets are found to be more sensitive to international shocks arisen from US markets and also to energy markets instability, especially with regard to oil market uncertainty. --BRIC,Energy Markets,GARCH,T-GARCH modeling,Volatility