Kajian yang dijalankan ini bertujuan untuk melihat impak pengenalan instrumen derivatif ke atas kemeruapan pulangan saham di pasaran semerta. Kajian ini juga mengambil kira kesemua instrumen derivatif yang telah diperkenalkan di pasaran tempatan. Data harian bagi indeks komposit dan sebilangan indeks-indeks untuk setiap sektor akan digunakan di dalam kajian ini bagi mengkaji kesan secara keseluruhan dan mengikut sektor. Dengan menggunakan model GARCH terubahsuai iaitu dengan mengambil kira kesan perubahan struktur. Kajian ini mendapati wujudnya kesan yang bercampur-campur. Secara umumnya, pengenalan instrumen derivative mampu untuk mengurangkan tahap kemeruapan pulangan dan secara langsung menstabilkan pasaran saham. Tambahan pula, ia turut mempertingkatkan lagi kadar dan kualiti aliran maklumat ke dalam pasaran dan dengan demikian menjadikan pasaran saham bertambah efisien.
This research investigated the unit-root tests using nonparametric sequences-reversals (S-R), Phillip-Perron (PP) tests and parametric Augmented Dickey-Fuller (ADF) test for the Malaysian equity indices. Under the considerations of drift and structural break, it was found that during the restructuring period after the Asian financial crisis, most of the indices provided evidences against the unit-root tests. These results are somewhat contrasted with the conventional unit-root tests that ignored the impact of structural changes. In addition, the S-R tests were found to have little power to identify the deviations from the unit-root even after the inclusion of structural break.
The behaviour of many financial time series cannot be modeled solely by linear time series model. Phenomena such as mean reversion, volatility of stock markets and structural breaks cannot be modelled implicitly using simple linear time series model. Thus, to overcome this problem, nonlinear time series models are typically designed to accommodate these nonlinear features in the data. In this paper, we use portmanteau test and structural change test to detect nonlinear feature in three ASEAN countries exchange rates (Malaysia, Singapore and Thailand). It is found that the null hypothesis of linearity is rejected and there is evidence of structural breaks in the exchange rates series. Therefore, the decision of using regime switching model in this study is justified. Using model selection criteria (AIC, SBC, HQC), we compare the in-sample fitting between two types of regime switching model. The two regime switching models we considered were the Self-Exciting Threshold Autoregressive (SETAR) model and the Markov switching Autoregressive (MS-AR) model where these models can explain the abrupt changes in a time series but differ as how they model the movement between regimes. From the AIC, SBC and HQC values, it is found that the MS -AR model is the best fitted model for all the return series. In addition, the regime switching model also found to perform better than simple autoregressive model in in-sample fitting. This result justified that nonlinear model give better in-sample fitting than linear model.
This paper compared the composition and performance of portfolios constructed by employing different risk measures utilizing the Malaysian share market data in three diverse economic scenarios. The risk measures considered were the mean-variance (MV) and their alternatives; the semi-variance (SV), mean absolute deviation (MAD) and conditional value at risk (CVAR). The data were divided into three sub-periods representing the growth period in the economy, financial crisis and the recovery period. The results of this study showed different optimal portfolios’ performances and compositions for the three economic periods. Nevertheless, among the risk models tested, CVAR(0.99) model gave the highest portfolio skewness. High skewness means that the probability of getting large negative returns is decreased. As a conclusion, for the Malaysian stock market, the CVAR(0.99) model is the most appropriate portfolio optimization model for downside risk aversion investors in all three economic scenarios.
This study proposes a simple methodology to estimate the power-law tail index of the Malaysian stock exchange by using the maximum likelihood Hill’s estimator. Recursive procedures base on empirical distribution tests are use to determine the threshold number of observations in the tail estimation. The threshold extreme values can be selected bases on the desired level of p-value in the goodness-of-fit tests. Finally, these procedures are apply to three indices in the Malaysian stock exchange.
In this research we introduce an analyzing procedure using the Kullback-Leibler information criteria (KLIC) as a statistical tool to evaluate and compare the predictive abilities of possibly misspecified density forecast models. The main advantage of this statistical tool is that we use the censored likelihood functions to compute the tail minimum of the KLIC, to compare the performance of a density forecast models in the tails. Use of KLIC is practically attractive as well as convenient, given its equivalent of the widely used LR test. We include an illustrative simulation to compare a set of distributions, including symmetric and asymmetric distribution, and a family of GARCH volatility models. Our results on simulated data show that the choice of the conditional distribution appears to be a more dominant factor in determining the adequacy and accuracy (quality) of density forecasts than the choice of volatility model.
The accuracy of financial time series forecasts often rely on the model precision and the availability of actual observations for forecast evaluations. This study aimed to tackle these issues in order to obtain a suitable asymmetric time-varying volatility model that outperformed in the forecast evaluations based on interday and intraday data. The model precision was examined based on the most appropriate time-varying volatility representation under the autoregressive conditional heteroscedascity framework. For forecast precision, the evaluations were conducted under three loss functions using the volatility proxies and realized volatility. The empirical studies were implemented on two major financial markets and the estimated results are applied in quantifying their market risks. Empirical results indicated that Zakoian model provided the best in-sample forecasts whereas DGE on the other hand indicated better out-of-sample forecasts. For the type of volatility proxy selection, the implementation of intraday data in the latent volatility indicated significant improvement in all the time horizon forecasts.
This study investigates the value-at-risk (VaR) using nonlinear time-varying volatility (ARCH model) and extreme-value-theory (EVT) methodologies. Similar VaR estimation and prediction are observes under the EVT and heavy-tailed long-memory ARCH approaches. The empirical results evidence the EVT-based VaR are more accurate but only at higher quantiles. It is also found that EVT approach is able to provide a convenient framework for asymmetric properties in both the lower and upper tails which implies that the risk and reward are not equally likely for the short- and long-trading positions in Malaysian stock market.
This article study the influences of structural break to the fractionally integrated time-varying volatility model in Malaysian stock markets from year 1996 to 2006. A fractionally integrated autoregressive conditional heteroscedastic (FIGARCH) model combines with sudden changes of volatility is develops to study the possibility of structural change in Asian financial crisis and currency crisis. Our empirical results evidence substantially reduction in long memory clustering volatility after the inclusion of sudden changes in the volatility. Finally, the estimation, diagnostic and model selection evaluations indicate that the fractionally integrated model with structural change is out-performed compared to the standard model.