جستجوی مقالات مرتبط با کلیدواژه "garch" در نشریات گروه "حسابداری"

This study empirically investigated the existence of Calendar effects by using closing daily data for the Vietnam index (VN-index) before and during the Covid-19 pandemic. Daily returns of the VN-Index from 2 January 2018 to 12 August 2022 are used in this study to ascertain calendar anomalies in Ho Chi Minh Stock Exchange (HOSE). To test these effects, the entire study period is divided into two sub-periods: during and before the Covid-19 crisis. Then, the ordinary least square (OLS) method and the Generalized Autoregressive Conditional Heteroskedasticity [GARCH (1,1)] regression model were employed. The empirical results from the OLS model support the occurrence of calendar anomalies for the HOSE both before and during the Covid-19 pandemic while the results of GARCH(1,1) only confirmed the positively significant effect on Friday during the Covid-19 periods. Regarding stock returns, positive returns were found only on Friday, during the Covid-19 pandemic. It implies that Covid-19 has changed the nature of the stock market from efficient to inefficient. The study's findings suggest that the Covid-19 crisis significantly impacted the daily returns anomaly in Vietnam’s HOSE.

Predictions are extremely important for a better decision-making. Uncertainty in decision making makes investors always seek to assess and estimate risk to minimize potential losses. Conditional risk value (CvaR) is considered as a comprehensive measure of risk that has been considered a useful tool in recent years. Due to the characteristics of capital market data, not all models will be able to make accurate predictions, and among the multitude of models, only the models which make predictions can correctly explain this market.In this study, according to the existing theoretical foundations and using Delphi model and analysis and review of experts, first the accounting variables in the financial statements are effective in predicting the conditional risk value, then the data of accepted companies are used. In Tehran Stock Exchange during 2012-2018, we evaluated the capability of GARCH and Markov index models in predicting conditional risk value as a criterion for predicting coherent risk. The results showed that the estimates made with the GARCH model (1, 1) are closer to reality with the distribution of T-Student.

Price volatility presents the investor possibilities and opportunities to buy securities at cheap prices and then sell it when they are overpriced, resulting in a profit at the end of the day. Recently, the volatility has become more valuable aspect for investors. Investment risk and return is important for investors. Investors have risk averse nature, they concerned about the information flow of stock price volatility. This study aims to review the literature on stock price volatility significance and its measurements by different methods. This study provides the detail review of stock price volatility different types including historical, implied, intraday, and indices volatility. This study discusses various measurements of stock price volatility forecasting with the empirical findings. Efficient market hypothesis supports the changes in stock prices in prior literature. Some studies shows that volatility can be measured by standard deviation of investor’s stock return. The price volatility mostly determined by high, low and closing prices. It is found that forecasting volatility can be measured by different methods. The literature review suggests that GARCH and Parkinson formula is considered most reliable method to measure volatility. Parkinson is more reliable measurement because it has daily high and low stock prices.

This study examines the influence of exchange rate volatility on foreign direct investment flows to the Nigeria economy. The study employs the ARCH, GARCH and EC models to analyze time series data for the period 1970 to 2016. The study established the stationarity of the data series and carried out the cointegration tests. The result of the study reveals that exchange rate volatility tends to persist throughout the study period. The findings of the study established empirical evidence to support the views that exchange rate volatility has a negative and significant influence on foreign direct investment inflows to Nigeria. The study demonstrates that increase in inflation exerts a negative effect on foreign direct investment inflows to Nigeria. The results of the analysis revealed that trade openness and interest rate have a positive influence on FDI in Nigeria. Thus, it is important for the government to muster the political will with efforts to create a stable environment to boost domestic production of export commodities and investment inflows. In addition, it is imperative for the government through its regulatory agencies to pursue a sound exchange rate regime with good policies and programs that would encourage investments in the economy.

Stochastic behavior of stock returns is very important for investors and policy makers in the stock market. In this paper, the stochastic behavior of the return index of Tehran Stock Exchange (TEDPIX) is examined using unobserved component Markov switching model (UC-MS) for the 3/27/2010 until 8/3/2015 period. In this model, stock returns are decomposed into two components; a permanent component and a transitory component. This approach allows analyzing the impact of shocks of permanent and transitory components. The transitory component has a three-state Markov switching heteroscedasticity (low, medium, and high variances). Results show that the unobserved component Markov switching model is appropriate for this data. Low value of RCM criteria implies that the model can successfully distinguish among regimes. The aggregate autoregressive coefficients in the temporary component are about 0.4. The duration of high-variance regime for the transitory component is short-lived and reverts to normal levels quickly. The implied result of the research is that the presidential election may have a significant effect on being in the third regime.

This study has been conducted to find out the effect of exchange rate uncertainty on the export volume of Pakistan to its major trading partner countries. Volatility in the nominal exchange rate of Pakistan has been estimated through Generalized Autoregressive Conditional Hetroscedastic (GARCH) process. The findings showed that exchange rate uncertainty has negative significant impact on the volume of exports of Pakistan with all considered trading partner countries. In case of Hong Kong, Kuwait and Malaysia relative prices have negative significant effect on the volume of exports, while for the rest, trading partner countries the relationship is found positive and significant. It is further documented that political instability has significant negative impact on the volume of exports in almost all the trading partner countries. Exchange rate volatility curtails the volume of exports, so appropriate policies are required to be adopted, which will stabilize the exchange rate.

This paper investigates the relation between political news and market returns. To do so we applied a Garch filter to a sample of the main Brazilian stock market index returns (Ibovespa Index) and of short-term interest rates (Selic Over and DI) which ranged from 01/02/2014 to 04/29/2016. Then we looked for periods of abnormal volatility which might be associated with political events using a parametric and a nonparametric method. Notwithstanding there were news like important politician been arrested and even speculation about the beginning of an impeachment process, we found relation between abnormal volatilities and political news only in Ibovespa returns during Presidential Elections.