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Exploring Advanced GARCH Models for Analyzing Asymmetric Volatility Dynamics for the Emerging Stock Market in Hungary: An Empirical Case Study
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The research study voyage commences with the foundational objective of fitting a suitable Generalized Autoregressive Conditional Heteroscedastic (GARCH) model to assess market volatility, a fundamental pillar of financial analysis. This research embarks on an ambitious quest to predict and understand stock market volatility within the realms of the DJIA and S&P 500 of USA and ATX index of Austria using different sophisticated GARCH models. The dataset used in this study comprises daily stock market data for two key indices: the S&P 500 Index, representing the USA stock market, and the ATX Index, representing the Austria stock market. Additionally, the DJIA Index, another representative of the USA stock market, was included. The dataset consists of 5967 daily observations over the specified time period from January 3, 2000, to September 21, 2023. The observation of results, analysis and discussion depicts that PARCH model shows most promising results and found suitable to model the volatility patterns of the selected indices. The findings and methodologies presented in this paper can be seen as a solid foundation upon which to build future investigations, refining our ability to anticipate market movements and make informed decisions in an uncertain financial landscape. In closing, this research not only contributes to the body of knowledge in financial econometrics but also underscores the importance of modeling long-term stock market behavior with precision and diligence.
IN THE STOCK MARKET, VOLATILITY IS A TERM USED TO DESCRIBE THE
DEGREE TO WHICH THE PRICES OF ASSETS FLUCTUATE AND DETERMINES
THE DEGREE OF RISK OR UNCERTAINTY. THE MAIN AIM OF THE PRESENT
STUDY IS TO MODELING THE BEHAVIOR OF THE SWITZERLAND STOCK
MARKET USING DATA FROM 4TH JANUARY, 2000 TO 9TH NOVEMBER, 2023.
THROUGH THE APPLICATION OF GARCH FAMILY MODELS WHICH, INCLUDE
GARCH/TARCH, EGARCH, COMPONENT ARCH (1,1), AND PARCH. THE STUDY
USED A SAMPLE NUMBER OF 5994 DAILY OBSERVATIONS FOR SWISS STOCK
INDEX REPRESENTING THE SWITZERLAND STOCK MARKET. WE USED SOME
STATISTICAL TECHNIQUES SUCH AS PHILLIPS-PERRON AND AUGMENTED
DICKEY FULLER TESTS STATISTIC. THE ARCH LAGRANGE MULTIPLIER (LM)
TEST, PARCH MODEL. WE UTILIZED THE EVIEWS 12 ECONOMETRICS PACKAGE. THIS STUDY HIGHLIGHTS THE SIGNIFICANCE OF ACCURATELY AND METICULOUSLY SIMULATING STOCK MARKET BEHAVIOR IN ADDITION TO ADDING TO THE CORPUS OF KNOWLEDGE IN FINANCIAL ECONOMETRICS. THE CONCLUSIONS AND METHODS DISCUSSED IN THIS STUDY PROVIDE A STRONG BASIS FOR FURTHER RESEARCH, ENHANCING OUR CAPACITY TO PREDICT MARKET MOVEMENTS AND MAKE WISE CHOICES IN A VOLATILE FINANCIAL ENVIRONMENT.
The domestic textiles and apparel industry stood at 225 billion by 2025. The textiles and apparel industry in India has strengths across the entire value chain from fibre, yarn, and fabric to apparel. On the other hand, many FinTech companies gained enough importance and attention during the Demonetization and COVID-19 pandemic situation where most people are dependent and prefer cashless payments and receipts over hard cash payments and receipts. Due to the growth of FinTech companies in India, consumer lending FinTech companies in India make up 17% of total FinTech enterprises. Many angel investors are coming forward to invest in such FinTech companies as this industry has much potential to grow in future. As there is enough scope for the expansion of FinTech companies in India, retail investors come forward to invest in the stocks of listed FinTech companies. As retail investors always look forward to returns either in the form of dividends or appreciation of stock prices, it is also necessary to analyse and model the stock price volatility of FinTech companies in India before investing. Hence, this research study is an attempt to use high-frequency data i.e. 1-minute closing prices, to formulate suitable GARCH (Generalised Autoregressive Conditional Heteroscedasticity) models for stock price volatility of listed textiles and FinTech companies that could also capture the asymmetric volatility if it exists due to third phase of COVID-19 pandemic and Russia-Ukraine war. The results concluded that there is a presence of positive shocks which might be due to the third wave of the COVID-19 pandemic that might have again shot the demand for financial products and services of these FinTech companies namely Paytm and PolicyBazaar and there is no negative shock of Russia-Ukraine war.
The purpose of the empirical research study is to analyze the volatility of OMX Tallinn Index in Estonia from 2002 to 2022 in three major period phases i.. Moreover, it attempted to formulate PARCH Model for each phases of OMX Tallinn Index in Estonia from 2002 to 2022 that could grasp not only the volatility but also asymmetric volatility caused by various important events for each particular period. The total sample size is 6,032 i.e. 3 phases of 1826 observations each. The selected period covers a series of extreme events such as the global financial crisis, the COVID-19 pandemic, the war between Russia and Ukraine that began in 2021 and others. The empirical results are relevant and contribute to the existing literature.
The impact of COVID-19, due to the widespread demand and supply destruction and downward movement of crude oil prices is of concern for all those connected with the oil and gas industry. In this study, an attempt has been made to estimate the price volatility of crude oil and natural gas listed on multi commodity exchange of India (MCX). We measured the leverage effect of COVID-19 on price volatility of crude oil and natural gas by using the daily prices of crude oil and natural gas from May 01, 2017 to April 30, 2020. The findings of the study reveal that there is a presence of leverage effect of COVID-19 on the price volatility of crude oil. However, this leverage effect is not present on the price volatility of natural gas. The findings of the study will help investors to develop investment strategies and to the policymakers to formulate appropriate policies to overcome or minimise the impact of COVID-19. The forecasting graphs of crude oil prices indicate that there is a possibility that price volatility will be higher in the future. However, it is difficult to forecast the expected price volatility of natural gas for the future because the price volatility graph is extremely fluctuating.
This paper investigates the volatility of daily returns in the Romanian stock market over the period January 2005 to December 2017. Volatility is analysed using four stock market indices (BET, BETC, BETPlus and ROTX).We aimed in this article to identify an econometric model to shape volatility of our selected stock market indices. The analysis was carried out using GARCH models, very useful tools applied in financial economics. In the survey, the best model was identified for analysing the volatility of the stock market indices. The conditional volatility for the daily return series shows a clear evidence of volatility shifting over the period. In the course of the examination, we discovered that there is a great influence of international stock markets on the capital market operations in Romania.
This article examines the accuracy and forecasting performance of volatility models for the Leones/USA dollars exchange rate return, including the ARMA, Generalized Autoregressive Conditional Heteroscedasticity (GARCH), and Asymmetric GARCH models with normal and non-normal (student's t and skewed Student t) distributions. In fitting these models to the monthly exchange rate returns data over the period January 2004 to December 2013, we found that, the Asymmetric (GARCH) and GARCH model better fits under the non-normal distribution than the normal distribution and improve the overall estimation for measuring conditional variance. The GJR-GARCH model using the skewed Student t-distribution is most successful and better forecast the Sierra Leone exchange rate volatility. Finally, the study suggests that the given models are suitable for modeling the exchange rate volatility of Sierra Leone and the Asymmetric GARCH models shows asymmetric in exchange rate returns, resulting to the presence of leverage effect. Given the implication of exchange rate volatility, the study would be of great value to policy makers, investors and researchers at home and abroad in promoting development of the capital market and foreign exchange market stability in emerging economies.
The effects of the global financial crisis on developing countries will be as high as on developed countries. IMF forecasts for sub-Saharan African growth for 2008 and 2009 have recently been downgraded by between 1-2 percentage points to 5.5% in 2008 and 5.1% in 2009. The revision represents a reduction of up to USD 20 per head in sub-Saharan Africa due to the financial crisis. The channels through which the global financial turmoil affects developing countries include financial channels and real channels. Financial channels include effects through: stock markets, banking sector (borrowing from advanced economies, foreign ownership of banks, exposure to sub-prime market), and foreign direct investment. Real channels include effects through remittances, exports, imports, terms of trade, and aid. Our previous research based on current updates and forecasts as well as on evidence on what happened in previous slowdowns and in the absence of policy responses, suggested that net financial flows to developing countries may fall by as much as $300 billion in two years, equivalent to a 25% drop. This paper examines whether Ghana, Kenya, Mali, Mozambique, Rwanda, Senegal, Tanzania, and Uganda, which represent successful countries in Africa recently, are at risk of back sliding on their recent successes. It does this by first painting the picture on how Africa might be affected using the channels mentioned and then by discussing each country in detail, as much as possible taking into account of what has already happened. It is clear that some countries are seriously at risk of being affected by the current global financial crisis either through real contagion or financial contagion. Ghana, Mali, Mozambique and Tanzania are more at risk than the other countries considered since they have a significant share of foreign owned banks and their economies strongly rely on foreign direct investment. Uganda has a high remittances dependency that makes it exposed to the current crisis; the turnover at its nascent stockmarket has already been more than halved. Kenya's indicators (remittances down by 40%, tourism by 30%, stock prices down by 40%) suggest it has already been affected in a major way. The policy space for responding to the impact of the financial turmoil varies across SSA countries. In particular, the effects might be stronger in Ghana as it has both a large current account and a large fiscal deficit, and the level of its reserves was below 3 months of imports of goods and services. In other countries this crisis is yet another set back. Kenya was already under political pressures and the current bad news will make the situation only more precarious. We suggest that while some evidence has emerged, individual developing countries need urgent access to updated research on country-specific economic, social and political consequences of the financial crisis. Each developing country needs to set up a crisis task force to consider the best possible policy responses – short term and long term economic and social policy responses.
Let n observations Y 1, Y 2, ···, Y n be generated by the model Y t = pY t−1 + e t , where Y 0 is a fixed constant and {e t } t-1 n is a sequence of independent normal random variables with mean 0 and variance σ2. Properties of the regression estimator of p are obtained under the assumption that p = ±1. Representations for the limit distributions of the estimator of p and of the regression t test are derived. The estimator of p and the regression t test furnish methods of testing the hypothesis that p = 1.
We identify and estimate the mean and variance components of the daily closing share prices using
ARIMA-GARCH type models by explaining the volatility structure of the residuals obtained under the best
suited mean models for the said series. The parameters of ARIMA type simple specifications are routinely
anticipated by applying the OLS methodology but it has two disadvantages when the volatility or ARCH effect
is present. The first problem may be the autocorrelation in error terms. To handle this unwanted situation the
lagged dependent variables can be incorporated as independent variables in the mean equation. The other
problem may be the presence of ARCH effect. This problem can be resolved by employing the ARCH or
GARCH specifications so we have taken advantage of such type of models in our study.
This study adds evidence from the four emerging markets of Central Europe relevant to the econometric modelling of financial time series by modelling volatility in these markets. The sample has all the previously documented characteristics of the unconditional distribution of stock returns normally used to justify the use of the GARCH class of models of conditional volatility. Both univariate and multivariate models are considered. Strong GARCH effects are apparent in all series examined. The estimates of asymmetric models of conditional volatility show rather weak evidence of asymmetries in the markets. The results of the multivariate specifications of volatility have implication for understanding the pattern of information flow between the markets. The constant correlation specification indicates significant conditional correlations between two pairs of countries: Hungary and Poland, and Hungary and Czech Republic. The BEKK model of multivariate volatility shows evidence of return volatility spillovers from Hungary to Poland, but no volatility spillover effects are found in the opposite direction.
In this study, we propose the application of the GARCH‐EVT‐Copula model in estimating liquidity‐adjusted value‐at‐risk (L‐VaR) of energy stocks while modeling nonlinear dependence between return and bid‐ask spread. Using the L‐VaR framework of Bangia et al. (1998), we present a more parsimonious model that effectively captures non‐zero skewness, excess kurtosis, and volatility clustering of both return and spread distributions of energy stocks. Moreover, to measure the nonlinear dependence between return and spread series, we use multiple copulas: Clayton, Gumbel, Frank, Normal, and Student‐ t . Based on the statistical backtesting and economic loss functions, our results suggest that the GARCH‐EVT‐Clayton copula is superior and most consistent in forecasting L‐VaR compared with other competing models. This finding has several implications for investors, market makers, and daily traders who appreciate the importance of liquidity in market risk computation.
In this study, we propose a new GO-GARCH-EVT-copula combined approach to estimate minimum quantile risk hedge ratios. We examine the hedging effectiveness of the proposed model in comparison with three other competing models. In doing so, we consider thirty-five pairs of daily spot and futures price series data from various stock, currency, and commodity markets across the world. The evidence suggests that the proposed combined approach performs best in estimating minimum quantile risk hedge ratios. The superior performance is likely due to the combined approach’s ability to appropriately capture the statistical features of the data.
The volatility of financial markets has been a relevant topic for transition economies, as the countries of Central and Eastern Europe and the former Soviet Union have seemingly endured high levels of volatility in their financial sectors during the transition process. But what have been the determinants of this financial volatility? This paper posits that institutional changes, and in particular the volatility of various crucial institutions, have been the major causes of financial volatility in transition. Examining 20 transition economies over various time-frames within the period 1993-2012, this paper applies the GARCH family of models to examine financial volatility as a function of institutional volatility. The results from the EGARCH and TGARCH modelling supports the thesis that more advanced and more stable institutions help to dampen financial sector volatility at their levels, while institutional volatility feeds through directly to financial sector volatility in transition.
Using realized volatility to estimate conditional variance of financial returns, we compare forecasts of volatility from linear GARCH models with asymmetric ones. We consider horizons extending to 30 days. Forecasts are compared using three different evaluation tests. With data from an equity index and two foreign exchange returns, we show that asymmetric models provide statistically significant forecast improvements upon the GARCH model for two of the datasets and improve forecasts for all datasets by means of forecasts combinations. These results extend to about 10 days in the future, beyond which the forecasts are statistically inseparable from each other.
A volatility model must be able to forecast volatility; this is the central requirement in almost all financial applications. In this paper we outline some stylized facts about volatility that should be incorporated in a model: pronounced persistence and mean-reversion, asymmetry such that the sign of an innovation also affects volatility and the possibility of exogenous or pre-determined variables influencing volatility. We use data on the Dow Jones Industrial Index to illustrate these stylized facts, and the ability of GARCH-type models to capture these features. We conclude with some challenges for future research in this area.
We compare 330 ARCH-type models in terms of their ability to describe the conditional variance. The models are compared out-of-sample using DM- exchange rate data and IBM return data, where the latter is based on a new data set of realized variance. We find no evidence that a GARCH(1,1) is outperformed by more sophisticated models in our analysis of exchange rates, whereas the GARCH(1,1) is clearly inferior to models that can accommodate a leverage effect in our analysis of IBM returns. The models are compared with the test for superior predictive ability (SPA) and the reality check for data snooping (RC). Our empirical results show that the RC lacks power to an extent that makes it unable to distinguish "good" and "bad" models in our analysis.
A ‘long memory’ property of stock market returns is investigated in this paper. It is found that not only there is substantially more correlation between absolute returns than returns themselves, but the power transformation of the absolute return ¦rt¦d also has quite high autocorrelation for long lags. It is possible to characterize ¦rt¦d to be ‘long memory’ and this property is strongest when d is around 1. This result appears to argue against ARCH type specifications based upon squared returns. But our Monte-Carlo study shows that both ARCH type models based on squared returns and those based on absolute return can produce this property. A new general class of models is proposed which allows the power δ of the heteroskedasticity equation to be estimated from the data.
Modeling of Market Volatility with APARCH Model
- D Ding
Ding, D. (n.d.). Modeling of Market Volatility with APARCH Model.
Modeling of Market Volatility with APARCH Model
- M N P Karana
- I W Sumarjaya
- K Sari
KARANA, M. N. P., SUMARJAYA, I. W., & SARI, K. (2024). Modeling of Market Volatility with APARCH Model. E-JurnalMatematika, 13(1),
82. https://doi.org/10.24843/MTK.2024.v13.i01.p445
Modeling Volatility in the Stock Markets using GARCH Models: European Emerging Economies and Turkey. Financial Innovation
- J C Mba
Mba, J. C. (2024). Modeling Volatility in the Stock Markets using GARCH Models: European Emerging Economies and Turkey. Financial
Innovation, 10(1), 20. https://doi.org/10.1186/s40854-023-00559-2