Estimating and forecasting volatility of stock indices using asymmetric GARCH models and Student-t densities: Evidence from Chittagong Stock Exchange
|
|
- Nigel Price
- 5 years ago
- Views:
Transcription
1 IJBFMR 3 (215) ISSN Estimating and forecasting volatility of stock indices using asymmetric GARCH models and Student-t densities: Evidence from Chittagong Stock Exchange Md. Qamruzzaman A. C. M. A. School of Business Studies, Southeast University, Bangladesh. qzamanfindu@gmail.com. Article History Received 7 January, 215 Received in revised form 1 February, 215 Accepted 6 February, 215 Keywords: Unit root test, Random Walk model, Stock indices, Chittagong Stock Exchange. Article Type: Full Length Research Article ABSTRACT This paper examined a wide variety of popular volatility models for stock index return, including Unit Root Test, Random Walk model, Autoregressive model, Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model, and extensive GARCH model, with Normal, and Student t-distribution assumption. I fit these models to Chittagong stock return index from 4 January 24 to 14September 214. There has been empirical evidence of volatility clustering, alike to findings in previous studies. Each market contains different GARCH models, which fit well. From the estimation, we find that the volatility of the return were significantly higher after 29. The model introducing GARCH effect with normal and Student t-distribution assumption can better fit the volatility characteristics. We find that GARCH-z, EGARCH-z, IGARCH-z, GJR-GARCH-z and EGARCH-t. It is suggested that these five models can capture the main characteristics of Chittagong stock exchange (CSE). 215 BluePen Journals Ltd. All rights reserved INTRODUCTION The ability to forecast financial market volatility is important for portfolio selection, valuation of stocks, asset management, predictability of risk premiums and designing optimal dynamic hedging strategies for options and Futures. While most researchers agree that volatility is predictable in many asset markets. Up and down movement in the daily prices of the securities can be considered as one of the consequences of the stochastic nature of the financial markets. In the face of usual updown price movements, investors invest their funds in the financial markets particularly in the stocks or stock indices with the expectation of being compensated by risk-premium. The variation in the returns provided by the stocks due to changes in the daily price is generally termed as volatility which is measured by the standard deviation or the variance. In such a volatile market it is difficult for companies to raise funds in the capital markets. Uncertainty causes loss of investor confidence which is important in stock trading particularly in making investment and leverage. This uncertainty can aggravate volatility further. Excess volatility may even lead to crashes or crisis in financial markets. Volatility can either be historical volatility which is a measure based on past data, or implied volatility which is derived from the market price of a market traded derivative particularly an option. The historical volatility can be calculated in three ways namely; (1) simple volatility, (2) Exponentially Weighted Moving Average (EWMA) and (3) generalized autoregressive conditional heteroscedasticity (GARCH). Among the financial time series non-linearity research literature, (Engle, 1982) proposed an autoregressive conditional Heteroscedasticity (ARCH) model and (Bollerslev, 1986) presented the GARCH model. These two models can determine the financial properties when the conditional variance is not a fixed parameter (Nelson, 199). Looked at stock price changes and discovered that they have both positive and negative relationships with future stock price volatility. The GARCH model supposes a settled time conditional variance for the preceding issue of conditional variance and an error term square function. Therefore, the error term s positive and
2 Int. J. Bus. Financ. Manage. Res. 2 negative values do not respond to its influence on the conditional variance equation. The conditional variance only changes along with the error term s value change, and cannot go along with the error term s positive and negative changes. To improve this flaw (Nelson, 1991) presented an exponential GARCH model and (Glosten, 1993) developed a GJR-GARCH model. These are socalled models of asymmetric GARCH. A large number of researcher s used ARCH and GARCH in capturing the dynamic characteristics of stock market return across the countries, such as Islam (213a), Elsheikh (211) Islam (213b), Engle (1987), Bae (27), Wann-Jyi (29), Tse (21), Koutmos (1995), Bucevska (212), Dima Alberga (28), Ajab Al Freedi (212) and many more. Few of empirical research findings are as follows. Md. Ariful Islam (214) has studied Stock market volatility: comparison between Dhaka stock exchange and Chittagong stock exchange considering Standard deviation, coefficient of Variation, F-test. Study results revealed that stock price at CSE is more volatile than DSE. Even the stock price of leading companies (top 2 and 3 companies of DSE and CSE) also varies from DSE to CSE and the volatility is much high than CSE3 of DSE2. D.D.Tewari (213) have studied existence and the nature of the volatility clustering phenomenon in the Johannesburg Stock Exchange (JSE) considering GARCH-type models. Study results revealed that an asymmetric effect of positive and negative shocks on conditional volatility could not be identified. Suliman Zakaria (212) have studied Stock market volatility in two African exchanges, Khartoum Stock Exchange, KSE (from Sudan) and Cairo and Alexandria Stock Exchange by employing different univariate specifications of the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model. Study results revealed that the asymmetric GARCH models find a significant evidence for asymmetry in stock returns in the two markets, confirming the presence of leverage effect in the returns series. Mehdi (212) has studied Tehran Stock Market with GARCH Models in Forecasting Volatility. Study result revealed that the estimation and test results for models suggest that the leverage effect term, is significant in EGARCH model (even with a one-sided test). So there does appear to be an asymmetric effect in Tehran stock market. In addition Evaluation forecasting with MSE criteria indicate that GARCH models in this paper have a same forecasting power, but when Log- Likelihood is evaluation criteria, CGARCH has the best forecasting power. Alberg (28) estimate stock market volatility of Tel Aviv Stock Exchange indices, for the period They report that the EGARCH model is the most successful in forecasting the TASE indices. Various time series methods are employed by Tudor (28), including the simple GARCH model, the GARCH-in-Mean model and the exponential GARCH to investigate the Risk- Return Trade-off on the Romanian stock market. Results of the study confirm that E-GARCH is the best fitting model for the Bucharest Stock Exchange composite index volatility in terms of sample-fit. Balaban (25) used both symmetric and asymmetric ARCH-type models to derive volatility expectations. The outcome showed that there has a positive effect of expected volatility on weekly and monthly stock returns of both Philippines and Thailand markets according to ARCH model. The result is not clear if using other models such as GARCH, GJR-GARCH and EGARCH. For emerging African markets, Ogum (25) investigate the market volatility using Nigeria and Kenya stock return series. Results of the exponential GARCH model indicate that asymmetric volatility found in the U.S. and other developed markets is also present in Nigerian stock exchange (NSE), but Kenya shows evidence of significant and positive asymmetric volatility. Also, they show that while the Nairobi Stock Exchange return series indicate negative and insignificant risk-premium parameters, the NSE return series exhibit a significant and positive time-varying risk premium. By using asymmetric GARCH models, Chowdhury (24) and Chowdhury and Rahman (24) have studied the relationship between the predicted volatility of DSE returns and that of selected macroeconomic variables of Bangladesh economy. They have calculated volatility from errors after using an autoregressive and seasonality adjusted forecasting model. The volatility series derived from such process has some limitations, which have been corrected in Generalized Conditional Auto Regressive Heteroscedasticity (GCARH) models developed by (Bollerslev, 1986). Peters (21) examined the forecasting performance of four GARCH (1, 1) models (GARCH, EGARCH, GJR and APARCH) used with three distributions (Normal, Studentt and Skewed Student-t). Study result revealed that overall estimation are achieved when asymmetric GARCH are used and when fat-tailed densities are taken into account in the conditional variance. Moreover, it is found that GJR and APARCH give better forecasts than symmetric GARCH. Finally increased performance of the forecasts is not clearly observed when using non-normal distributions. But to the best of my knowledge, there are no such empirical studies for the Chittagong Stock Exchange (CSE). This study is useful for number of reasons. Firstly, to the best of our knowledge, this is the first study of this kind of modeling volatility of the Bangladesh stock exchange especially Chittagong stock exchange. Secondly, the results of this study will be of great interest to academician, policy maker and investor both domestically and internationally. The main objective of
3 Qamruzzaman 21 this study is to study volatility pattern of Chittagong Stock Markets and get some insight on volatility modeling using ARCH and GARCH kind of models. Thus, one of the contributions of this paper is to provide empirical evidence on the fit of conditional volatility models for the Chittagong stock exchange (CSE). The main objective of this paper is to model stock returns volatility in Chittagong Stock Exchange by employing different univariate specifications of GARCH type models for daily observations on the index returns series of each market over the period of January 24 to June 214, as well as describing special features of the markets in terms of trading activity and index components and calculations. The volatility models employed in this paper include both symmetric and asymmetric GARCH models. METHODOLOGY The main focus of this study is to conduct a comprehensive analysis of the volatility characteristics of the Dhaka Stock Exchange DSE. A number of widely recognized volatility models are used in this regard, namely 1. Random walk model 2. Unit root test.3. ARMA model 4. GARCH model and 5. Expanded GARCH Model. Daily returns as asset time series Daily returns are identified as the difference in the natural logarithm of the closing index value for the two consecutive trading days, i.e.. Where, r t is the logarithmic daily return at time t and and are daily price of an asset at two successive days t-1 and t respectively. In order to do time series analysis, transformation of original series is required depending upon the type of series when the data is in the level form. I have transformed the series of return by taking natural logarithm of the series. Some scholars have pointed out two advantages of this kind of transformation of the series. First, it eliminates the possible dependence of changes in stock price index on the price level of the index. Second, the change in the log of the stock price index yields continuously compounded series. Random walk model: If is a random series with mean µ and constant variance and is serially uncorrelated that the series [ ] is said to be random walk if: Where return series of stock price is [ ], random walk model as, where u is the model parameter, E(, Var ( =, and to follow a normal distribution ARMA model The ARMA (the autoregressive Moving Average) proposed by the Box and Jenkins (197). If the series [ ] stratifies ARMA (p, q), then [ ] can be described as follows; Where,,,i=1,.. Ƥ are parameters, E =, Var ( =, and a particular distribution The ARCH Models Let us consider univariate time series y t. If Ψ t-1 is the information set (i.e. all the information available) at time t- 1, we can define its functional form as: y t = E[y t Ψ t-t ] + ε t (1) Where, E [y t Ψ t-t ] denotes the conditional expectation operator and is the disturbance term (or unpredictable part), with E[εt] = and E[εtεs] = ; 8t 6= s. The ε t term in Equation 1 is the innovation of the process. The conditional expectation is the expectation conditional to all past information available at time t-1. The Autoregressive Conditional Heteroscedastic (ARCH) process of Engle (1982) is any {εt} of the form Where, is an independently and identically distributed (i.i.d.) process, E ( ) =, Var ( ) =1 and where is a time-varying, positive and measurable function of the information set at time t-1. By definition, is serially uncorrelated with mean zero, but its conditional variance equals, therefore, may change over time, contrary to what is assumed in ordinary least squares (OLS) estimations. Specifically, the ARCH (q) model is given by: (1) (2) (3) (4)
4 Int. J. Bus. Financ. Manage. Res. 22 GARCH model The conditional variance of is assumed constant for random walk and AR model, but in financial time series the data are usually volatility clustering, such that the conditional volatility changes over time. The stock price s return series is [r t ], sample GARCH (p, q) model can be describe as follows: ) =, )= Using the lag or backshift operator L, the GARCH (p, q) model is (5 (6) of (. That is why he suggest to express the function g ( as, Where,, (8) and known as. Another advantage of this specification is that it does not require any stationary constraint. Thus, E depends on the assumption made on the unconditional density. GJR-GARCH This popular model is proposed by Gloston, Jagannathan and Runkle (1993). It is generalized version is given by; ) (9) and Where is a dummy variable. Based on Equation 5, it is straight forward to show that the GARCH model is based on an infinite ARCH specification. If all the roots of the polynomial 1- = of Equation 5 lie outside the unit circle, we have or equivalently In this model, it is assumed that the impact of on the conditional variance is difference when is positive or negative. That is why the dummy variable takes the value (respectively 1 ) when is positive (negative). Note that model of Zakoian (1994) is very similar to the GJR but models the conditional standard deviation instead of the conditional variance. APARCH Which may be seen as an ARCH ( process since the conditional variance linearly depends on all previous squared residuals. EGARCH Our first asymmetric GARCH model is the exponential GARCH model of Nelson (1991); 7 Ding, Granger, and Engle (1993) introduce the asymmetric Power ARCH (APARCH) model. The APARCH (p,q) model can be expressed as; Where, (j=1 p), (1) This model is quite interesting since it couples the flexibility of a varying exponent with the asymmetry coefficient to take the leverage effect into account. Where, is the normalized series. RESULTS AND DISCUSSION The value of g ( depends on several elements, Nelson (1991) note that to accommodate the asymmetric relation between stock return and volatility changes, the value of g ( must be a function of both magnitude and the sign Descriptive statistics This research considers daily closing prices index for Chittagong stock exchange namely CSCX index in
5 Figure 1: graph of CSCX price series Qamruzzaman 23 CSCX price series 5 2, 4 16, 3 12, 2 8, 1 4, Price Figure 1. Graph of CSCX price series. Bangladesh from 4th January 24 to 15th September 214. The analysis is undertaken using the econometric package Eviews-8 for volatility modeling. The return indices are obtained from CSE market for different indices. Daily returns are calculated by using the following formula; rt =1*dlog(pt) Where, rt represents daily return, pt represents daily closing stock market index. Here, we also try to examine the behavior of the return series using all three price index of Chittagong stock exchange. The graphs of Price Series, BSE SENSEX Series and Descriptive Statistics, Conditional Volatility and Trajectory of Volatility Returns are displayed in Figures 1 to 9. It is observed from the graphs that there is volatility clustering with non-normal distribution for each case. In case of the trajectory of volatility of returns indicates less sensitive for return from CSCZ index while rest two indexes show greater sensitivity toward return volatility. Some of the descriptive statistics of three different index are displayed in Table 1. It is observed that average return of CSE3 is.78, CASPI is.83 and CSCX is.78 respectively which is very much closure to each other. Volatility (measured as a standard deviation) shows maximum value for CSCX index is whereas rest two index shows almost same level of volatility (1.5). Return of CSE show Leptokurtic distribution in all cases which is indicating that distribution of return is sharper than a normal distribution, with values concentrated around the mean and thicker tails. This means high probability for extreme values. According to Jarque Bera statistics normality is rejected for the return series by showing high non-normality in all three indices. However, it is obvious from Table 2 that Strong positive correlation exists among all index in Chittagong Stock exchange. Unit root test It is found that the variables RTCSE3, RTCSCX AND RTCASPI have trends in their level.augmented Dickey- Fuller (ADF) t-tests and (PP) Phillips and Perron (1988) tests are used for each of the three time series-cse3,
6 Int. J. Bus. Financ. Manage. Res. 24 Figure 2: Conditional Volatility of CSCX Conditional variance Figure 2. Conditional Volatility of CSCX Price. RTCASPI Figure 3. The trajectory of volatility of returns.
7 Figure 4: Graph of CSE3 price series Figure 5: C Qamruzzaman 25 CSE3 24, 2, 16, 12, 2,8, 2,4, 2,, 1,6, 1,2, 8, 8, es Figure 4, 5: Conditional Volatility of CSE3 Price , 24 2 Figure 4. Graph of CSE3 price series. 2,8, 2,4, 2,, 1,6, 1,2, 8, 4, Conditional variance Figure 5. Conditional volatility of CSE3 price.
8 Int. J. Bus. Financ. Manage. Res. 26 RTCSE Figure 6. The trajectory of volatility of returns. Figure 7: graph of CSE3 price series Figure 8: 2, CSCX 12 16, 12, , 4 4, Figure 7. Graph of CSE3 price series. Price 24 2
9 Qamruzzaman Figure 27 8: Conditional Volatility of CSE Price Conditional variance Figure 8. Conditional volatility of CSE3. Figure 9: The Trajectory of Volatility of Returns RTCSCZ Figure 9. The trajectory of volatility of returns.
10 Int. J. Bus. Financ. Manage. Res. 28 Table 1. Descriptive statistics for logarithm differences rt=1*dlog (pt). Index Average Min Max SD Kurtosis Skewness Jarque Bera stat. CSCX CSE CASPI Table 2. Correlation matrix among different index in Chittagong stock exchange. CSCX CSE3 CASPI CSCX 1 CSE CASPI Table 3. Unit root test. Variables Augmented Dickey Fuller (ADF) Test-(t stat) No. of observations Phillips and Perron (PP) test-(t stat) No. of observations RTCSCX Level 2568 (25.2)* (25.2) * (26.67) * 2569 (264.24) * (283.54) * (24.84) * 1 st difference 255. (19.92) * (19.92) * (19.93) * (493.71) * (4929.7) * ( ) * RTCSE3 Level 268. (51.67) * (51.74) * (51.55) * 268. (51.68) * (51.74) * (51.61) * 1 st difference (19.84) * (19.84) * (19.84) * 267. (675.46) * (675.3) * (675.75) * RTCSCPI Level 268. (49.29) * (49.37) * (49.14) * 268. (49.6) * (49.61) * (49.65) * 1 st difference (19.11) * (19.11) * (19.11) * 267. (732.51) * (732.23) * (732.84) * Note: Superscripts * indicate rejection of null hypothesis at 5% level of significance. 1, No trend, no intercept; 2, only intercept; 3, trend and intercept. CSCX and CASPI to test for the presence of a unit root. To ensure that the residuals were white noise the lag length for the ADF tests was selected. The outcomes of the Augmented Dickey Fuller (ADF) test by Engle (1987) with and without trend and the Phillips and Perron (1988) test again with and without trend are reported in Table 3. It is obvious from Table 3 estimation that at level the variables are non-stationary in both ADF and PP tests which is indicate there is no unit root existing among tested variables. Autocorrelation test We test for autocorrelation in the raw returns and their squares using Ljung-Box (L-B) Q-statistics. For detecting autocorrelation look at Q-Statistics in Tables 1, 4 and 5 and its associated probability values. If the probability value is greater than.5, we accept the null hypothesis (it suggests absence of autocorrelation). In a situation where probability value is less than.5, we reject the null hypothesis (it suggests presence of autocorrelation). The statistics given in Tables 4, 5 and 6 of Sample Autocorrelation-CSCX, CASPI and CSE3 respectively suggest the presence of autocorrelation in all lags of the series. I computed Q-statistics up to 36 lags but reported the Q-statistics up to 15 lags for both raw returns and their square to test for ARCH effect. All the lags are statistically significant, and the squares of the lag values are larger, suggesting that ARCH type modeling is more appropriate. Random walk test Table 7 exhibits the results of Random walk model with their z statistics and associated probabilities for each
11 Qamruzzaman 29 Table 4. Sample autocorrelation CSCE. Raw return series Return square series Lags AC Q-Stat Probability Lags AC Q-Stat Probability Table 5. Sample autocorrelation -CSE3. Raw return series Return square series Lags AC Q-Stat Probability Lags AC Q-Stat Probability case. It is manifested that that z-statistics of all three index are lower than associated probability, which is explain the existence of autocorrelation. Table 8 lists the estimated results of AR models and their t-statistics as well as log likelihood for three index of Chittagong stock exchange. It is observed that estimated mean of each model is insignificant and dummy variable is insignificant as well, but volatility is significant. AR model shows negative likelihood values in all three index for each model which indicates that there is log likelihood exist among each model from AR (1) to AR (5). GARCH model Estimation results of the GARCH models including the t- statistic as well as likelihood value are listed in Table 9. The comparison of all log likelihood values of AR models
12 Int. J. Bus. Financ. Manage. Res. 3 Table 6. Sample autocorrelation-caspi. Raw return series Return square series Lags AC Q-Stat Probability Lags AC Q-Stat Probability Table 7. Estimation of RW model with normal distribution of market index. index Joint Tests Value d.f Probability CSCX CSE3 Max z (at period 2)* CASPI *Probability approximation using studentized maximum modulus with parameter value 4 and infinite degrees of freedom Individual Tests Period Var. Ratio Std. Error z-statistic Probability CSCX INDEX CSE3 INDEX CASPI INDEX CSCX INDEX CSE3 INDEX CASPI INDEX CSCX INDEX CSE3 INDEX CASPI INDEX CSCX INDEX CSE3 INDEX CASPI INDEX shows that adding the GARCH effect significantly improve the in sample fit of the models. The log likelihood values increases from (992) to (989) for CASPI, from (4512) to (455) for CSE3 and values decrease from (9983) to (17) for CSCX respectively. GARCH effect is significant for CASPI, CSE3 but insignificant for CXCZ. In order to further diagnosis whether there is any residual effect remain in GARCH model. Table 1 exhibits estimate results of standardized squared residual indicating that values of Q-stat are significantly lower in every lags with having maximum probability such ensure
13 Qamruzzaman 31 Table 8. Parameter estimation for AR models with normal distribution. AR (1) AR (2) AR (3) AR (4) AR (5) CAXPI CSCZ CAXPI CSCZ CAXPI CSCZ CAXPI CSCZ CAXPI CSCZ µ Α α 1 β Log likelihood ( ) (.121).196 (.6186).5362 ( ) (.18).196 (.938).9253 ( ) (.24).196 (1.424).2973 ( ) ( ) ( ) ( ) (.3).197 (.175).986 ( ) ( ) ( ) (166.26) ( ) ( ) (.3).197 (.158).9874 ( ) that there is no residual effects in GARCH models. Expanded GARCH models with normal distribution and non-normal distribution: The estimated parameters in expanded GARCH models with z-distribution and t-distribution are listed in Table 11 including z-statistics and t-statistics with log likelihood values. The results shows that the means are insignificant exception may occur in AR (1) GJR-GARCH. The sum of GARCH estimates α 1 + β is less than 1which shows that the volatility is limited and the data is nonstationary, explaining why model fit well. RESEARCH FINDINGS This paper compared the forecasting performance of several GARCH-type models. The comparison was focused on two different aspects: the difference between symmetric and asymmetric GARCH (i.e., GARCH versus EGARCH, GJR and APARCH) and the difference between normal tailed symmetric, fat-tailed symmetric and fat-tailed asymmetric distributions (i.e. Normal versus Student-t and Skewed Student-t). Study results revealed that return of CSE leptokurtic, significant skewness, and deviation from normality and the return series are volatility clustering. Conclusion about CSE returns are as follows: (1) AR model, which is added in lag, cannot improve performance and error of the model in contrast to random walk model. There is no significant different between two models. (2) Adding the GARCH effect based on random walk model can improve performance and error of the model to some extent. GARCH model which have leverage effect do a little help to improve the model performance. Moreover, it can
14 Int. J. Bus. Financ. Manage. Res. 32 Table 9. Parameter estimates for GARCH (1, 1) models with normal distribution. AR(1) GARCH (1,1) AR(2) GARCH (1,1) AR(3) GARCH (1,1) AR(4) GARCH(1,1) AR(5) GARCH(1,1) CASPI CSCX CASPI CSCX CASPI CSCX CASPI CSCX CASPI CSCX µ Α α 1 β Log likelihood (.967) (.414).967 (.373).489 (.912).9273 ( ) ( ) (.1259) (.582).9536 (.236).259 (.9113).3621 ( ) (.134) (.416) ( ) (.432).232 (1.8647).622 ( ) (.1696) (.449).9642 (.628).9155 (.685).9454 ( ) (.14) (.428).9658 (.32) (.3).9976 ( ) ( ) (.1616) (.511).9592 (.536).4256 (.1258).8999 (113.16) (.173) (.439) ( ) ( ) (.1515) (.482).9615 (.56).4311 (.13).9897 (123.35) (.114) (.49) ( ) ( ) (.158) (.45).9641 (.153).3495 (.439).965 (169.24) Table 1. Diagnostic test for standardized squared residuals. Lags AC PAC Q-Stat Probability
15 Qamruzzaman 33 Table 1. Diagnostic test for standardized squared residuals. Lags AC PAC Q-Stat Probability Table 11. Parameter estimates for expanded GARCH models with normal distribution. AR(1) GJR- GARCH CAXPI CSCZ µ α α 1 β Log likelihood AIC SIC (.9).1 (.857).392 ( ) (451.15) (.9).1 (.857).392 ( ) AR(1) EGARCH(1,1) CAXPI CSCZ (.397).271 (1.465).143 (.2).2 (1.8) (.397).271 (1.465).143 (.2).2 (1.8).313 (963.81) ( ) (963.81) CAXPI AR(1) EGARCH(t)** CSCZ **Estimated values of AR (1) EGARCH consider student s t-distribution. (444.81) ( ) ( ) increase the specification error of the model such as EGARCH, IGARCH, and GJR-GARCH model. Therefore, it can be said that all four models may be best suited for capturing CSE return volatility. Scope of further study The study examined stock return volatility focusing on three indexes available at CSE with application of ARCH,
16 Int. J. Bus. Financ. Manage. Res. 34 GARC and Expanded GARCH Models. Thus, the study paves the avenue for following further research agenda: 1. Factors responsible for stock return clustering volatility in both DSE and CSE. REFERENCES Ajab Al Freedi A. S. (212). A study on the behavior of volatility in saudi arabia stock market using symmetric and asymmetric GARCH Models. Journal of Mathematics and Statistics. Pp Alberg D. S. (28). Estimating stock market volatility using asymmetric GARCH models. Appl. Financ. Econ. Pp Bae J. C. (27). Why are stock returns and volatility negatively correlated. J. Appl. Financ. Pp Balaban E. B. A. (25). Forecasting stock market volatility: Evidence from 14 countries. 1th Global Finance Conference, Frankfurt. Pp Bollerslev T. (1986). Generalized autoregressive conditional hetroscedasticity. Journal of Econometrics. Pp Bucevska V. (212). An empirical evaluation of GARCH Models in value-at-risk estimation: evidence from the macedonian stock exchange. Bus. Syst. Res. Pp Chowdhury S. S. (24). On the Empirical relation between macroeconomic volatility and stock market volatility of Bangladesh. The Global Journal of Finance and Economics. Pp D.D.Tewari O. N. (213). Volatility clustering at the Johannesburg stock exchange: Investigation and analysis. Mediterranean Journal of Social Sciences. Pp Dima Alberga H. S. (28). Estimating stock market volatility using asymmetric GARCH models. Appl. Financ. Econ. Pp Elsheikh A. A. (211). Modeling stock market volatility using GARCH models evidence from Sudan. Int. J. Bus. Soc. Sci. Pp Engle R. D. (1987). Estimating time varying risk premia in the term structure. The ARCH-M model. Econometrica. Pp Engle R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation. Econometrica. Pp Glosten L. J. (1993). On the relation between the expected value and the volatility on the nominal excess returns on stocks. J. Financ. Pp Islam M. (213a). Modeling univariate volatility of modeling univariate volatility of evidence from 4-Asian markets. Int. Res. J. Financ. Econ. Pp Islam M. A. (213b). Estimating volatility of stock index returns by using symmetric garch models. Middle-East J. Sci. Res. Pp Koutmos G. (1995). Asymmetric volatility transmission in international stock markets. J. Int. Money Financ. Pp Md. Ariful Islam M. R. (214). Stock market volatility: Comparison between Dhaka stock exchange and Chittagong stock exchange. Int. J. Econ. Financ. Manage. Sci. Pp Mehdi Parvaresh M. B. (212). Forecasting volatility in Tehran stock market with GARCH models. J. Basic Appl. Sci. Res. Pp Nelson (1991). Conditional heteroscedasticity in asset returns: A new Approach. Econometrica. Pp Nelson D. B. (199). Stationarity and persistence in the GARCH (1, 1) model. Econometric Theory, Pp Ogum G. B. (25). Emerging equity market volatility: An empirical investigation of markets in Kenya and Nigeria. J. Afr. Bus. Pp Peters J. P. (21). Estimating and forecasting volatility of stock indices using asymmetric GARCH models and (skewed) Student-t densities. Int. J. Bus. Manage. Pp Suliman Zakaria S. A. (212). Modelling stock market volatility using univariate GARCH models evidence from Sudan and Egypt. Int. J. Econ. Financ. Pp Tse Y. K. (21). A multivariate GARCH model with time-varying correlations. J. Bus. Econ. Stat. Pp Wann-Jyi Horng T.-C. H.-L. (29). Dynamic relatedness analysis of two stock market returns volatility: An empirical study on the South Korean and Japanese stock markets. Asian J. Manage. Human. Sci. Pp
Modelling Stock Market Return Volatility: Evidence from India
Modelling Stock Market Return Volatility: Evidence from India Saurabh Singh Assistant Professor, Graduate School of Business,Devi Ahilya Vishwavidyalaya, Indore 452001 (M.P.) India Dr. L.K Tripathi Dean,
More informationVolatility Analysis of Nepalese Stock Market
The Journal of Nepalese Business Studies Vol. V No. 1 Dec. 008 Volatility Analysis of Nepalese Stock Market Surya Bahadur G.C. Abstract Modeling and forecasting volatility of capital markets has been important
More informationResearch Article The Volatility of the Index of Shanghai Stock Market Research Based on ARCH and Its Extended Forms
Discrete Dynamics in Nature and Society Volume 2009, Article ID 743685, 9 pages doi:10.1155/2009/743685 Research Article The Volatility of the Index of Shanghai Stock Market Research Based on ARCH and
More informationMODELING EXCHANGE RATE VOLATILITY OF UZBEK SUM BY USING ARCH FAMILY MODELS
International Journal of Economics, Commerce and Management United Kingdom Vol. VI, Issue 11, November 2018 http://ijecm.co.uk/ ISSN 2348 0386 MODELING EXCHANGE RATE VOLATILITY OF UZBEK SUM BY USING ARCH
More informationVolatility Clustering of Fine Wine Prices assuming Different Distributions
Volatility Clustering of Fine Wine Prices assuming Different Distributions Cynthia Royal Tori, PhD Valdosta State University Langdale College of Business 1500 N. Patterson Street, Valdosta, GA USA 31698
More informationAn Empirical Research on Chinese Stock Market Volatility Based. on Garch
Volume 04 - Issue 07 July 2018 PP. 15-23 An Empirical Research on Chinese Stock Market Volatility Based on Garch Ya Qian Zhu 1, Wen huili* 1 (Department of Mathematics and Finance, Hunan University of
More informationModeling Exchange Rate Volatility using APARCH Models
96 TUTA/IOE/PCU Journal of the Institute of Engineering, 2018, 14(1): 96-106 TUTA/IOE/PCU Printed in Nepal Carolyn Ogutu 1, Betuel Canhanga 2, Pitos Biganda 3 1 School of Mathematics, University of Nairobi,
More informationPrerequisites for modeling price and return data series for the Bucharest Stock Exchange
Theoretical and Applied Economics Volume XX (2013), No. 11(588), pp. 117-126 Prerequisites for modeling price and return data series for the Bucharest Stock Exchange Andrei TINCA The Bucharest University
More informationThe Great Moderation Flattens Fat Tails: Disappearing Leptokurtosis
The Great Moderation Flattens Fat Tails: Disappearing Leptokurtosis WenShwo Fang Department of Economics Feng Chia University 100 WenHwa Road, Taichung, TAIWAN Stephen M. Miller* College of Business University
More informationModelling Stock Returns Volatility on Uganda Securities Exchange
Applied Mathematical Sciences, Vol. 8, 2014, no. 104, 5173-5184 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.12988/ams.2014.46394 Modelling Stock Returns Volatility on Uganda Securities Exchange Jalira
More informationIndian Institute of Management Calcutta. Working Paper Series. WPS No. 797 March Implied Volatility and Predictability of GARCH Models
Indian Institute of Management Calcutta Working Paper Series WPS No. 797 March 2017 Implied Volatility and Predictability of GARCH Models Vivek Rajvanshi Assistant Professor, Indian Institute of Management
More informationInternational Journal of Business and Administration Research Review. Vol.3, Issue.22, April-June Page 1
A STUDY ON ANALYZING VOLATILITY OF GOLD PRICE IN INDIA Mr. Arun Kumar D C* Dr. P.V.Raveendra** *Research scholar,bharathiar University, Coimbatore. **Professor and Head Department of Management Studies,
More informationChapter 4 Level of Volatility in the Indian Stock Market
Chapter 4 Level of Volatility in the Indian Stock Market Measurement of volatility is an important issue in financial econometrics. The main reason for the prominent role that volatility plays in financial
More informationSt. Theresa Journal of Humanities and Social Sciences
Volatility Modeling for SENSEX using ARCH Family G. Arivalagan* Research scholar, Alagappa Institute of Management Alagappa University, Karaikudi-630003, India. E-mail: arivu760@gmail.com *Corresponding
More informationMODELING VOLATILITY OF BSE SECTORAL INDICES
MODELING VOLATILITY OF BSE SECTORAL INDICES DR.S.MOHANDASS *; MRS.P.RENUKADEVI ** * DIRECTOR, DEPARTMENT OF MANAGEMENT SCIENCES, SVS INSTITUTE OF MANAGEMENT SCIENCES, MYLERIPALAYAM POST, ARASAMPALAYAM,COIMBATORE
More informationA Study on the Performance of Symmetric and Asymmetric GARCH Models in Estimating Stock Returns Volatility
Vol., No. 4, 014, 18-19 A Study on the Performance of Symmetric and Asymmetric GARCH Models in Estimating Stock Returns Volatility Mohd Aminul Islam 1 Abstract In this paper we aim to test the usefulness
More informationFinancial Econometrics
Financial Econometrics Volatility Gerald P. Dwyer Trinity College, Dublin January 2013 GPD (TCD) Volatility 01/13 1 / 37 Squared log returns for CRSP daily GPD (TCD) Volatility 01/13 2 / 37 Absolute value
More informationINFORMATION EFFICIENCY HYPOTHESIS THE FINANCIAL VOLATILITY IN THE CZECH REPUBLIC CASE
INFORMATION EFFICIENCY HYPOTHESIS THE FINANCIAL VOLATILITY IN THE CZECH REPUBLIC CASE Abstract Petr Makovský If there is any market which is said to be effective, this is the the FOREX market. Here we
More informationConditional Heteroscedasticity
1 Conditional Heteroscedasticity May 30, 2010 Junhui Qian 1 Introduction ARMA(p,q) models dictate that the conditional mean of a time series depends on past observations of the time series and the past
More informationOil Price Effects on Exchange Rate and Price Level: The Case of South Korea
Oil Price Effects on Exchange Rate and Price Level: The Case of South Korea Mirzosaid SULTONOV 東北公益文科大学総合研究論集第 34 号抜刷 2018 年 7 月 30 日発行 研究論文 Oil Price Effects on Exchange Rate and Price Level: The Case
More informationThe University of Chicago, Booth School of Business Business 41202, Spring Quarter 2009, Mr. Ruey S. Tsay. Solutions to Final Exam
The University of Chicago, Booth School of Business Business 41202, Spring Quarter 2009, Mr. Ruey S. Tsay Solutions to Final Exam Problem A: (42 pts) Answer briefly the following questions. 1. Questions
More informationBooth School of Business, University of Chicago Business 41202, Spring Quarter 2010, Mr. Ruey S. Tsay. Solutions to Midterm
Booth School of Business, University of Chicago Business 41202, Spring Quarter 2010, Mr. Ruey S. Tsay Solutions to Midterm Problem A: (30 pts) Answer briefly the following questions. Each question has
More informationInvestment Opportunity in BSE-SENSEX: A study based on asymmetric GARCH model
Investment Opportunity in BSE-SENSEX: A study based on asymmetric GARCH model Jatin Trivedi Associate Professor, Ph.D AMITY UNIVERSITY, Mumbai contact.tjatin@gmail.com Abstract This article aims to focus
More informationA Study of Stock Return Distributions of Leading Indian Bank s
Global Journal of Management and Business Studies. ISSN 2248-9878 Volume 3, Number 3 (2013), pp. 271-276 Research India Publications http://www.ripublication.com/gjmbs.htm A Study of Stock Return Distributions
More informationAmath 546/Econ 589 Univariate GARCH Models: Advanced Topics
Amath 546/Econ 589 Univariate GARCH Models: Advanced Topics Eric Zivot April 29, 2013 Lecture Outline The Leverage Effect Asymmetric GARCH Models Forecasts from Asymmetric GARCH Models GARCH Models with
More informationBooth School of Business, University of Chicago Business 41202, Spring Quarter 2012, Mr. Ruey S. Tsay. Solutions to Midterm
Booth School of Business, University of Chicago Business 41202, Spring Quarter 2012, Mr. Ruey S. Tsay Solutions to Midterm Problem A: (34 pts) Answer briefly the following questions. Each question has
More informationModeling Volatility of Price of Some Selected Agricultural Products in Ethiopia: ARIMA-GARCH Applications
Modeling Volatility of Price of Some Selected Agricultural Products in Ethiopia: ARIMA-GARCH Applications Background: Agricultural products market policies in Ethiopia have undergone dramatic changes over
More informationModelling Inflation Uncertainty Using EGARCH: An Application to Turkey
Modelling Inflation Uncertainty Using EGARCH: An Application to Turkey By Hakan Berument, Kivilcim Metin-Ozcan and Bilin Neyapti * Bilkent University, Department of Economics 06533 Bilkent Ankara, Turkey
More informationA market risk model for asymmetric distributed series of return
University of Wollongong Research Online University of Wollongong in Dubai - Papers University of Wollongong in Dubai 2012 A market risk model for asymmetric distributed series of return Kostas Giannopoulos
More informationStudy on Dynamic Risk Measurement Based on ARMA-GJR-AL Model
Applied and Computational Mathematics 5; 4(3): 6- Published online April 3, 5 (http://www.sciencepublishinggroup.com/j/acm) doi:.648/j.acm.543.3 ISSN: 38-565 (Print); ISSN: 38-563 (Online) Study on Dynamic
More informationModelling and Forecasting Volatility of Returns on the Ghana Stock Exchange Using GARCH Models
MPRA Munich Personal RePEc Archive Modelling and Forecasting Volatility of Returns on the Ghana Stock Exchange Using GARCH Models Joseph Magnus Frimpong and Eric Fosu Oteng-Abayie 7. October 2006 Online
More informationLecture 5a: ARCH Models
Lecture 5a: ARCH Models 1 2 Big Picture 1. We use ARMA model for the conditional mean 2. We use ARCH model for the conditional variance 3. ARMA and ARCH model can be used together to describe both conditional
More informationForecasting Volatility of USD/MUR Exchange Rate using a GARCH (1,1) model with GED and Student s-t errors
UNIVERSITY OF MAURITIUS RESEARCH JOURNAL Volume 17 2011 University of Mauritius, Réduit, Mauritius Research Week 2009/2010 Forecasting Volatility of USD/MUR Exchange Rate using a GARCH (1,1) model with
More informationApplying asymmetric GARCH models on developed capital markets :An empirical case study on French stock exchange
Applying asymmetric GARCH models on developed capital markets :An empirical case study on French stock exchange Jatin Trivedi, PhD Associate Professor at International School of Business & Media, Pune,
More informationForecasting Volatility in the Chinese Stock Market under Model Uncertainty 1
Forecasting Volatility in the Chinese Stock Market under Model Uncertainty 1 Yong Li 1, Wei-Ping Huang, Jie Zhang 3 (1,. Sun Yat-Sen University Business, Sun Yat-Sen University, Guangzhou, 51075,China)
More informationFinancial Time Series Analysis (FTSA)
Financial Time Series Analysis (FTSA) Lecture 6: Conditional Heteroscedastic Models Few models are capable of generating the type of ARCH one sees in the data.... Most of these studies are best summarized
More informationForecasting the Volatility in Financial Assets using Conditional Variance Models
LUND UNIVERSITY MASTER S THESIS Forecasting the Volatility in Financial Assets using Conditional Variance Models Authors: Hugo Hultman Jesper Swanson Supervisor: Dag Rydorff DEPARTMENT OF ECONOMICS SEMINAR
More informationRecent analysis of the leverage effect for the main index on the Warsaw Stock Exchange
Recent analysis of the leverage effect for the main index on the Warsaw Stock Exchange Krzysztof Drachal Abstract In this paper we examine four asymmetric GARCH type models and one (basic) symmetric GARCH
More informationModelling Stock Indexes Volatility of Emerging Markets
Modelling Stock Indexes Volatility of Emerging Markets Farhan Ahmed 1 Samia Muhammed Umer 2 Raza Ali 3 ABSTRACT This study aims to investigate the use of ARCH (autoregressive conditional heteroscedasticity)
More informationESTABLISHING WHICH ARCH FAMILY MODEL COULD BEST EXPLAIN VOLATILITY OF SHORT TERM INTEREST RATES IN KENYA.
ESTABLISHING WHICH ARCH FAMILY MODEL COULD BEST EXPLAIN VOLATILITY OF SHORT TERM INTEREST RATES IN KENYA. Kweyu Suleiman Department of Economics and Banking, Dokuz Eylul University, Turkey ABSTRACT The
More informationForecasting Stock Index Futures Price Volatility: Linear vs. Nonlinear Models
The Financial Review 37 (2002) 93--104 Forecasting Stock Index Futures Price Volatility: Linear vs. Nonlinear Models Mohammad Najand Old Dominion University Abstract The study examines the relative ability
More informationModel Construction & Forecast Based Portfolio Allocation:
QBUS6830 Financial Time Series and Forecasting Model Construction & Forecast Based Portfolio Allocation: Is Quantitative Method Worth It? Members: Bowei Li (303083) Wenjian Xu (308077237) Xiaoyun Lu (3295347)
More informationVOLATILITY OF SELECT SECTORAL INDICES OF INDIAN STOCK MARKET: A STUDY
Indian Journal of Accounting (IJA) 1 ISSN : 0972-1479 (Print) 2395-6127 (Online) Vol. 50 (2), December, 2018, pp. 01-16 VOLATILITY OF SELECT SECTORAL INDICES OF INDIAN STOCK MARKET: A STUDY Prof. A. Sudhakar
More informationAmath 546/Econ 589 Univariate GARCH Models
Amath 546/Econ 589 Univariate GARCH Models Eric Zivot April 24, 2013 Lecture Outline Conditional vs. Unconditional Risk Measures Empirical regularities of asset returns Engle s ARCH model Testing for ARCH
More informationThe Analysis of ICBC Stock Based on ARMA-GARCH Model
Volume 04 - Issue 08 August 2018 PP. 11-16 The Analysis of ICBC Stock Based on ARMA-GARCH Model Si-qin LIU 1 Hong-guo SUN 1* 1 (Department of Mathematics and Finance Hunan University of Humanities Science
More informationStock Price Volatility in European & Indian Capital Market: Post-Finance Crisis
International Review of Business and Finance ISSN 0976-5891 Volume 9, Number 1 (2017), pp. 45-55 Research India Publications http://www.ripublication.com Stock Price Volatility in European & Indian Capital
More informationTrading Volume, Volatility and ADR Returns
Trading Volume, Volatility and ADR Returns Priti Verma, College of Business Administration, Texas A&M University, Kingsville, USA ABSTRACT Based on the mixture of distributions hypothesis (MDH), this paper
More informationANALYSIS OF THE RETURNS AND VOLATILITY OF THE ENVIRONMENTAL STOCK LEADERS
ANALYSIS OF THE RETURNS AND VOLATILITY OF THE ENVIRONMENTAL STOCK LEADERS Viorica Chirila * Abstract: The last years have been faced with a blasting development of the Socially Responsible Investments
More informationEconometrics II. Seppo Pynnönen. Spring Department of Mathematics and Statistics, University of Vaasa, Finland
Department of Mathematics and Statistics, University of Vaasa, Finland Spring 2018 Part IV Financial Time Series As of Feb 5, 2018 1 Financial Time Series Asset Returns Simple returns Log-returns Portfolio
More informationDeterminants of Stock Prices in Ghana
Current Research Journal of Economic Theory 5(4): 66-7, 213 ISSN: 242-4841, e-issn: 242-485X Maxwell Scientific Organization, 213 Submitted: November 8, 212 Accepted: December 21, 212 Published: December
More informationANALYSIS OF THE RELATIONSHIP OF STOCK MARKET WITH EXCHANGE RATE AND SPOT GOLD PRICE OF SRI LANKA
ANALYSIS OF THE RELATIONSHIP OF STOCK MARKET WITH EXCHANGE RATE AND SPOT GOLD PRICE OF SRI LANKA W T N Wickramasinghe (128916 V) Degree of Master of Science Department of Mathematics University of Moratuwa
More informationModeling the volatility of FTSE All Share Index Returns
MPRA Munich Personal RePEc Archive Modeling the volatility of FTSE All Share Index Returns Bayraci, Selcuk University of Exeter, Yeditepe University 27. April 2007 Online at http://mpra.ub.uni-muenchen.de/28095/
More informationEconometric Models for the Analysis of Financial Portfolios
Econometric Models for the Analysis of Financial Portfolios Professor Gabriela Victoria ANGHELACHE, Ph.D. Academy of Economic Studies Bucharest Professor Constantin ANGHELACHE, Ph.D. Artifex University
More informationTHE INFLATION - INFLATION UNCERTAINTY NEXUS IN ROMANIA
THE INFLATION - INFLATION UNCERTAINTY NEXUS IN ROMANIA Daniela ZAPODEANU University of Oradea, Faculty of Economic Science Oradea, Romania Mihail Ioan COCIUBA University of Oradea, Faculty of Economic
More informationFinancial Econometrics Jeffrey R. Russell. Midterm 2014 Suggested Solutions. TA: B. B. Deng
Financial Econometrics Jeffrey R. Russell Midterm 2014 Suggested Solutions TA: B. B. Deng Unless otherwise stated, e t is iid N(0,s 2 ) 1. (12 points) Consider the three series y1, y2, y3, and y4. Match
More informationBooth School of Business, University of Chicago Business 41202, Spring Quarter 2014, Mr. Ruey S. Tsay. Solutions to Midterm
Booth School of Business, University of Chicago Business 41202, Spring Quarter 2014, Mr. Ruey S. Tsay Solutions to Midterm Problem A: (30 pts) Answer briefly the following questions. Each question has
More informationLecture 6: Non Normal Distributions
Lecture 6: Non Normal Distributions and their Uses in GARCH Modelling Prof. Massimo Guidolin 20192 Financial Econometrics Spring 2015 Overview Non-normalities in (standardized) residuals from asset return
More informationThe Impact of Falling Crude Oil Price on Financial Markets of Advanced East Asian Countries
10 Journal of Reviews on Global Economics, 2018, 7, 10-20 The Impact of Falling Crude Oil Price on Financial Markets of Advanced East Asian Countries Mirzosaid Sultonov * Tohoku University of Community
More informationEconomics 413: Economic Forecast and Analysis Department of Economics, Finance and Legal Studies University of Alabama
Problem Set #1 (Linear Regression) 1. The file entitled MONEYDEM.XLS contains quarterly values of seasonally adjusted U.S.3-month ( 3 ) and 1-year ( 1 ) treasury bill rates. Each series is measured over
More information1 Volatility Definition and Estimation
1 Volatility Definition and Estimation 1.1 WHAT IS VOLATILITY? It is useful to start with an explanation of what volatility is, at least for the purpose of clarifying the scope of this book. Volatility
More informationDemand For Life Insurance Products In The Upper East Region Of Ghana
Demand For Products In The Upper East Region Of Ghana Abonongo John Department of Mathematics, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana Luguterah Albert Department of Statistics,
More informationGARCH Models for Inflation Volatility in Oman
Rev. Integr. Bus. Econ. Res. Vol 2(2) 1 GARCH Models for Inflation Volatility in Oman Muhammad Idrees Ahmad Department of Mathematics and Statistics, College of Science, Sultan Qaboos Universty, Alkhod,
More informationModelling Stock Returns Volatility In Nigeria Using GARCH Models
MPRA Munich Personal RePEc Archive Modelling Stock Returns Volatility In Nigeria Using GARCH Models Kalu O. Emenike Dept. of Banking and Finance, University of Nigeria Enugu Campus,Enugu State Nigeria
More informationMAGNT Research Report (ISSN ) Vol.6(1). PP , 2019
Does the Overconfidence Bias Explain the Return Volatility in the Saudi Arabia Stock Market? Majid Ibrahim AlSaggaf Department of Finance and Insurance, College of Business, University of Jeddah, Saudi
More informationVOLATILITY COMPONENT OF DERIVATIVE MARKET: EVIDENCE FROM FBMKLCI BASED ON CGARCH
VOLATILITY COMPONENT OF DERIVATIVE MARKET: EVIDENCE FROM BASED ON CGARCH Razali Haron 1 Salami Monsurat Ayojimi 2 Abstract This study examines the volatility component of Malaysian stock index. Despite
More informationGARCH Models. Instructor: G. William Schwert
APS 425 Fall 2015 GARCH Models Instructor: G. William Schwert 585-275-2470 schwert@schwert.ssb.rochester.edu Autocorrelated Heteroskedasticity Suppose you have regression residuals Mean = 0, not autocorrelated
More informationINTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING AND TECHNOLOGY (IJARET)
INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING AND TECHNOLOGY (IJARET) ISSN 0976-6480 (Print) ISSN 0976-6499 (Online) Volume 5, Issue 3, March (204), pp. 73-82 IAEME: www.iaeme.com/ijaret.asp
More informationImplied Volatility v/s Realized Volatility: A Forecasting Dimension
4 Implied Volatility v/s Realized Volatility: A Forecasting Dimension 4.1 Introduction Modelling and predicting financial market volatility has played an important role for market participants as it enables
More informationDATABASE AND RESEARCH METHODOLOGY
CHAPTER III DATABASE AND RESEARCH METHODOLOGY The nature of the present study Direct Tax Reforms in India: A Comparative Study of Pre and Post-liberalization periods is such that it requires secondary
More informationTrends in currency s return
IOP Conference Series: Materials Science and Engineering PAPER OPEN ACCESS Trends in currency s return To cite this article: A Tan et al 2018 IOP Conf. Ser.: Mater. Sci. Eng. 332 012001 View the article
More informationBESSH-16. FULL PAPER PROCEEDING Multidisciplinary Studies Available online at
FULL PAPER PROEEDING Multidisciplinary Studies Available online at www.academicfora.com Full Paper Proceeding BESSH-2016, Vol. 76- Issue.3, 15-23 ISBN 978-969-670-180-4 BESSH-16 A STUDY ON THE OMPARATIVE
More informationModelling Rates of Inflation in Ghana: An Application of Arch Models
Current Research Journal of Economic Theory 6(2): 16-21, 214 ISSN: 242-4841, e-issn: 242-485X Maxwell Scientific Organization, 214 Submitted: February 28, 214 Accepted: April 8, 214 Published: June 2,
More informationARCH and GARCH models
ARCH and GARCH models Fulvio Corsi SNS Pisa 5 Dic 2011 Fulvio Corsi ARCH and () GARCH models SNS Pisa 5 Dic 2011 1 / 21 Asset prices S&P 500 index from 1982 to 2009 1600 1400 1200 1000 800 600 400 200
More informationForecasting the Philippine Stock Exchange Index using Time Series Analysis Box-Jenkins
EUROPEAN ACADEMIC RESEARCH Vol. III, Issue 3/ June 2015 ISSN 2286-4822 www.euacademic.org Impact Factor: 3.4546 (UIF) DRJI Value: 5.9 (B+) Forecasting the Philippine Stock Exchange Index using Time HERO
More informationThe Effect of 9/11 on the Stock Market Volatility Dynamics: Empirical Evidence from a Front Line State
Aalborg University From the SelectedWorks of Omar Farooq 2008 The Effect of 9/11 on the Stock Market Volatility Dynamics: Empirical Evidence from a Front Line State Omar Farooq Sheraz Ahmed Available at:
More informationAsian Economic and Financial Review A REGRESSION BASED APPROACH TO CAPTURING THE LEVEL DEPENDENCE IN THE VOLATILITY OF STOCK RETURNS
Asian Economic and Financial Review ISSN(e): 2222-6737/ISSN(p): 2305-2147 URL: www.aessweb.com A REGRESSION BASED APPROACH TO CAPTURING THE LEVEL DEPENDENCE IN THE VOLATILITY OF STOCK RETURNS Lakshmi Padmakumari
More informationThe Impact of Macroeconomic Volatility on the Indonesian Stock Market Volatility
International Journal of Business and Technopreneurship Volume 4, No. 3, Oct 2014 [467-476] The Impact of Macroeconomic Volatility on the Indonesian Stock Market Volatility Bakri Abdul Karim 1, Loke Phui
More informationIntaz Ali & Alfina Khatun Talukdar Department of Economics, Assam University
Available online at http://sijournals.com/ijae/ ISSN: 2345-5721 Stock Market Volatility and Returns: A Study of National Stock Exchange in India Intaz Ali & Alfina Khatun Talukdar Department of Economics,
More informationVolatility in the Indian Financial Market Before, During and After the Global Financial Crisis
Volatility in the Indian Financial Market Before, During and After the Global Financial Crisis Praveen Kulshreshtha Indian Institute of Technology Kanpur, India Aakriti Mittal Indian Institute of Technology
More informationThe Efficient Market Hypothesis Testing on the Prague Stock Exchange
The Efficient Market ypothesis Testing on the Prague Stock Exchange Miloslav Vošvrda, Jan Filacek, Marek Kapicka * Abstract: This article attempts to answer the question, to what extent can the Czech Capital
More informationARCH modeling of the returns of first bank of Nigeria
AMERICAN JOURNAL OF SCIENTIFIC AND INDUSTRIAL RESEARCH 015,Science Huβ, http://www.scihub.org/ajsir ISSN: 153-649X, doi:10.551/ajsir.015.6.6.131.140 ARCH modeling of the returns of first bank of Nigeria
More informationAsymmetry and Persistence of Stock Returns: A Case of the Ghana Stock Exchange
International Journal of Business and Economics Research 2016; 5(6): 183-190 http://www.sciencepublishinggroup.com/j/ijber doi: 10.11648/j.ijber.20160506.11 ISSN: 2328-7543 (Print); ISSN: 2328-756X (Online)
More informationModelling Kenyan Foreign Exchange Risk Using Asymmetry Garch Models and Extreme Value Theory Approaches
International Journal of Data Science and Analysis 2018; 4(3): 38-45 http://www.sciencepublishinggroup.com/j/ijdsa doi: 10.11648/j.ijdsa.20180403.11 ISSN: 2575-1883 (Print); ISSN: 2575-1891 (Online) Modelling
More informationManagement Science Letters
Management Science Letters 4 (2014) 941 950 Contents lists available at GrowingScience Management Science Letters homepage: www.growingscience.com/msl An application of unit rate estimation on shareholders
More informationVolatility spillovers among the Gulf Arab emerging markets
University of Wollongong Research Online University of Wollongong in Dubai - Papers University of Wollongong in Dubai 2010 Volatility spillovers among the Gulf Arab emerging markets Ramzi Nekhili University
More informationRisk- Return and Volatility analysis of Sustainability Indices of S&P BSE
Available online at : http://euroasiapub.org/current.php?title=ijrfm, pp. 65~72 Risk- Return and Volatility analysis of Sustainability Indices of S&P BSE Mr. Arjun B. S 1, Research Scholar, Bharathiar
More informationModelling the Stock Price Volatility Using Asymmetry Garch and Ann-Asymmetry Garch Models
International Journal of Data Science and Analysis 218; 4(4): 46-52 http://www.sciencepublishinggroup.com/j/ijdsa doi: 1.11648/j.ijdsa.21844.11 ISSN: 2575-1883 (Print); ISSN: 2575-1891 (Online) Modelling
More informationModelling Volatility of the Market Returns of Jordanian Banks: Empirical Evidence Using GARCH framework
(GJEB) 1 (1) (2016) 1-14 Science Reflection (GJEB) Website: http:// Modelling Volatility of the Market Returns of Jordanian Banks: Empirical Evidence Using GARCH framework 1 Hamed Ahmad Almahadin, 2 Gulcay
More informationProperties of financail time series GARCH(p,q) models Risk premium and ARCH-M models Leverage effects and asymmetric GARCH models.
5 III Properties of financail time series GARCH(p,q) models Risk premium and ARCH-M models Leverage effects and asymmetric GARCH models 1 ARCH: Autoregressive Conditional Heteroscedasticity Conditional
More informationCOINTEGRATION AND MARKET EFFICIENCY: AN APPLICATION TO THE CANADIAN TREASURY BILL MARKET. Soo-Bin Park* Carleton University, Ottawa, Canada K1S 5B6
1 COINTEGRATION AND MARKET EFFICIENCY: AN APPLICATION TO THE CANADIAN TREASURY BILL MARKET Soo-Bin Park* Carleton University, Ottawa, Canada K1S 5B6 Abstract: In this study we examine if the spot and forward
More informationThe January Effect: Evidence from Four Arabic Market Indices
Vol. 7, No.1, January 2017, pp. 144 150 E-ISSN: 2225-8329, P-ISSN: 2308-0337 2017 HRS www.hrmars.com The January Effect: Evidence from Four Arabic Market Indices Omar GHARAIBEH Department of Finance and
More informationAn Empirical Research on Chinese Stock Market and International Stock Market Volatility
ISSN: 454-53 Volume 4 - Issue 7 July 8 PP. 6-4 An Empirical Research on Chinese Stock Market and International Stock Market Volatility Dan Qian, Wen-huiLi* (Department of Mathematics and Finance, Hunan
More informationMarket Integration, Price Discovery, and Volatility in Agricultural Commodity Futures P.Ramasundaram* and Sendhil R**
Market Integration, Price Discovery, and Volatility in Agricultural Commodity Futures P.Ramasundaram* and Sendhil R** *National Coordinator (M&E), National Agricultural Innovation Project (NAIP), Krishi
More informationVOLATILITY. Time Varying Volatility
VOLATILITY Time Varying Volatility CONDITIONAL VOLATILITY IS THE STANDARD DEVIATION OF the unpredictable part of the series. We define the conditional variance as: 2 2 2 t E yt E yt Ft Ft E t Ft surprise
More informationEquity Price Dynamics Before and After the Introduction of the Euro: A Note*
Equity Price Dynamics Before and After the Introduction of the Euro: A Note* Yin-Wong Cheung University of California, U.S.A. Frank Westermann University of Munich, Germany Daily data from the German and
More informationRISK SPILLOVER EFFECTS IN THE CZECH FINANCIAL MARKET
RISK SPILLOVER EFFECTS IN THE CZECH FINANCIAL MARKET Vít Pošta Abstract The paper focuses on the assessment of the evolution of risk in three segments of the Czech financial market: capital market, money/debt
More informationThe Relationship between Inflation, Inflation Uncertainty and Output Growth in India
Economic Affairs 2014, 59(3) : 465-477 9 New Delhi Publishers WORKING PAPER 59(3): 2014: DOI 10.5958/0976-4666.2014.00014.X The Relationship between Inflation, Inflation Uncertainty and Output Growth in
More informationVolume 35, Issue 1. Thai-Ha Le RMIT University (Vietnam Campus)
Volume 35, Issue 1 Exchange rate determination in Vietnam Thai-Ha Le RMIT University (Vietnam Campus) Abstract This study investigates the determinants of the exchange rate in Vietnam and suggests policy
More information12. Conditional heteroscedastic models (ARCH) MA6622, Ernesto Mordecki, CityU, HK, 2006.
12. Conditional heteroscedastic models (ARCH) MA6622, Ernesto Mordecki, CityU, HK, 2006. References for this Lecture: Robert F. Engle. Autoregressive Conditional Heteroscedasticity with Estimates of Variance
More informationIS GOLD PRICE VOLATILITY IN INDIA LEVERAGED?
IS GOLD PRICE VOLATILITY IN INDIA LEVERAGED? Natchimuthu N, Christ University Ram Raj G, Christ University Hemanth S Angadi, Christ University ABSTRACT This paper examined the presence of leverage effect
More information