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.
The study of stock market volatility has been the focus of market participants primarily because most
of the applications in financial economics are concerned with volatility. The economic structure in
Malaysia is divided into three sectors: primary, secondary and tertiary. As the stability of the stock
market is important for businesses, this paper carefully reviews the concept of volatility and analyses
how different business sectors in Malaysia are affected by stock market volatility.
Studies have indicated that up to 47% of total N fertilizer applied in flooded rice fields may be lost to the atmosphere through NH3 volatilization. The volatilized NH3 represents monetary loss and contributes to increase in formation of PM2.5 in the atmosphere, eutrophication in surface water, and degrades water and soil quality. The NH3 is also a precursor to N2O formation. Thus, it is important to monitor NH3 volatilization from fertilized and flooded rice fields. Commercially available samplers offer ease of transportation and installation, and thus, may be considered as NH3 absorbents for the static chamber method. Hence, the objective of this study is to investigate the use of a commercially available NH3 sampler/absorbent (i.e., Ogawa® passive sampler) for implementation in a static chamber. In this study, forty closed static chambers were used to study two factors (i.e., trapping methods, exposure duration) arranged in a Randomized Complete Block Design. The three trapping methods are standard boric acid solution, Ogawa® passive sampler with acid-coated pads and exposed coated pads without casing. The exposure durations are 1 and 4 h. Results suggest that different levels of absorbed NH3 was obtained for each of the trapping methods. Highest level of NH3 was trapped by the standard boric acid solution, followed by the exposed acid-coated pads without casing, and finally acid-coated pads with protective casing, given the same exposure duration. The differences in absorbed NH3 under same conditions does not warrant direct comparison across the different trapping methods. Any three trapping methods can be used for conducting studies to compare multi-treatments using the static chamber method, provided the same trapping method is applied for all chambers.
Gold is a valuable asset to a country because of itsliquidity.Gold reserve can stabilize the currency in a country.The objectiveof this paper is to identify the factors contributingto the volatility of gold prices, such as Real Malaysia GDP, inflation rates, crude oil pricesand exchange rates. The data was analysed using Autoregressive Distributed Lag (ARDL) approachwith time series data, with 30-year coverage from 1987 to 2016. Findings showed that only Real Malaysia GDP and crudeoil priceswere significantly relatedto gold prices. As a conclusion, this study can beusedas reference byother investors.The author suggests toother researchers to further improve upon this study by adding more variables or diversifying the variables that relate to volatility of gold prices
The novel Coronavirus disease (COVID-19) has quickly evolved from a provincial health scare to a global meltdown. While it has brought nearly half the world to a standstill it has affected the financial markets in unseen ways by eroding a quarter of wealth in nearly a month. This paper investigates the reaction of financial markets globally in terms of their decline and volatility as Coronavirus epicentre moved from China to Europe and then to the US. Findings suggest that the earlier epicentre China has stabilized while the global markets have gone into a freefall especially in the later phase of the spread. Even the relatively safer commodities have suffered as the pandemic moves into the US.
Simulation is used to measure the robustness and the efficiency of the forecasting
techniques performance over complex systems. A method for simulating multivariate
time series was presented in this study using vector autoregressive base-process. By
applying the methodology to the multivariable meteorological time series, a simulation
study was carried out to check for the model performance. MAPE and MAE performance
measurements were used and the results show that the proposed method that consider
persistency in volatility gives better performance and the accuracy error is six time smaller
than the normal hybrid model.
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.
This paper analyzes the asymmetric long memory volatility dependency of the interday prices of Composite Index (CI) at Bursa Malaysia by using GARCH family models. The GARCH type models are used with the assumption that the innovations series follow either one of the following distributions: Gaussian, Student -t and skewed Student -t. The stock returns' long memory dependency is determined using the Hurst parameter. The long memory and asymmetric volatility are modelled by fractionally integrated GARCH models. It is found that the asymmetric and long memory GARCH models with skewed student-t distribution give better predictive ability on the volatility of the Kuala Lumpur Composite Index (KLCI).
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.
The main objective of this paper is to explore the varying volatility dynamic of inflation rate in Malaysia for the period from January 1980 to December 2004. The GARCH, GARCH-Mean, EGARCH and EGARCH-Mean models are used to capture the stochastic variation and asymmetries in the economic instruments. Results show that the EGARCH model gives better estimates of sub-periods volatility. Further analysis using Granger causality test show that there is sufficient empirical evidence that higher inflation rate level will result in higher future inflation uncertainty and higher level of inflation uncertainty will lead to lower future inflation rate.
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.
The heterogeneous autoregressive (HAR) models are used in modeling high frequency multipower realized volatility of the
S&P 500 index. Extended from the standard realized volatility, the multipower realized volatility representations have
the advantage of handling the possible abrupt jumps by smoothing the consecutive volatility. In order to accommodate
clustering volatility and asymmetric of multipower realized volatility, the HAR model is extended by the threshold
autoregressive conditional heteroscedastic (GJR-GARCH) component. In addition, the innovations of the multipower realized
volatility are characterized by the skewed student-t distributions. The extended model provides the best performing insample
and out-of-sample forecast evaluations.
Biochar has been considered as a potential tool to mitigate soil ammonia (NH3) volatilization and greenhouse gases (GHGs) emissions in recent years. However, the aging effect of biochar on soils remains elusive, which introduces uncertainty on the effectiveness of biochar to mitigate global warming in a long term. Here, a meta-analysis of 22 published works of literature with 217 observations was conducted to systematically explore the aging effect of biochar on soil NH3 and GHGs emissions. The results show that, in comparison with the fresh biochar, the aging makes biochar more effective to decrease soil NH3 volatilization by 7% and less risk to contribute CH4 emissions by 11%. However, the mitigation effect of biochar on soil N2O emissions is decreased by 15% due to aging. Additionally, aging leads to a promotion effect on soil CO2 emissions by 25% than fresh biochar. Our findings suggest that along with aging, particularly the effect of artificial aging, biochar could further benefit the alleviation of soil NH3 volatilization, whereas its potential role to mitigate global warming may decrease. This study provides a systematic assessment of the aging effect of biochar to mitigate soil NH3 and GHGs, which can provide a scientific basis for the sustainable green development of biochar application.
Single Stock Futures (SSFs) was introduced in Bursa Malaysia on 28th April 2006. There have been many studies on derivative instruments in Malaysia; however, none is on SSFs. Various statistical methods have been used to analyse the SSFs and its spot returns, namely Descriptive Statistics, Unit Root test, VAR, Johansen and Juselius Co-integration test, Granger Causality test, Variance Decomposition test, VECM, and GARCH model. This study analyses the SSFs and spot returns of eight companies listed in Bursa Malaysia. It found that Berjaya Sports Toto Bhd and Genting Bhd have no long-run and short-run causality (Genting Bhd has bi-directional causality) while AirAsia Bhd and AMMB Holdings Bhd’s spot returns’ volatility decreased after the introduction of SSFs; it increased in the other seven companies. In addition, only AMMB Holdings Bhd futures return did not affect its spot return. Bursa Malaysia Bhd and RHB Capital Bhd spot returns lead their futures returns
A large amount of ammonia volatilization from the agricultural system causes environmental problems and increases production costs. Conservation agriculture has emerged as an alternate and sustainable crop production system. Therefore, in the present study, ammonia losses from different agricultural practices were evaluated for the wheat crop under different tillage practices. The results of the present study showed that the cumulative emission of ammonia flux from the wheat field varied from 6.23 to 24.00 kg ha-1 (P ≤ 0.05) in conservation tillage (CA) and 7.03 to 26.58 kg ha-1 (P ≤ 0.05) in conventional tillage (CT) among different treatments. Application of basal 80% nitrogen resulted in the highest ammonia flux in conventional and conservation tillage practices. The ammonia volatilization followed the following trend: urea super granules with band placement > neem-coated urea with band placement > neem-coated urea with broadcast before irrigation > neem-coated urea with broadcast after irrigation > slow-release N fertilizer (urea stabilized with DCD and N(n-butyl)thiophosphoric triamide) with band placement. The conservation agricultural practices involving conservation tillage appear to be a sustainable approach for minimizing ammonia volatilization and improving wheat productivity.
Compound-specific isotope analysis offers potential for fingerprinting of diesel fuels, however, possible confounding effects of isotopic fractionation due to evaporation need to be assessed. This study measured the fractionation of the stable carbon and hydrogen isotopes in n-alkane compounds in neat diesel fuel during evaporation. Isotope ratios were measured using a continuous flow gas chromatograph/isotope ratio mass spectrometer. Diesel samples were progressively evaporated at 24 ± 2°C for 21 days. Increasing depletion of deuterium in nC12-nC17 alkanes in the remaining liquid with increasing carbon chain length was observed. Negligible carbon isotope fractionation was observed. Preferential vaporization was measured for the shorter chain n-alkanes and the trend decreased with increasing chain length. The decrease in δ(2) H values indicates the preferential vaporization of the isotopically heavier species consistent with available quantitative data for hydrocarbons. These results are most important in the application of stable isotope technology to forensic analysis of diesel.
Polygonum odoratum Lour. has been reclassified as Persicaria odorata (Lour.) Soják [Wilson, K. L. Polygonum sensu lato (Polygonaceae) in Australia. Telopea 1988, 3, 177-182]; other synonyms currently used are Vietnamese mint or Vietnamese coriander and, in Malaysia, Daun Laksa or Laksa plant. The aerial parts of Laksa plant are highly aromatic, and they contain many organic compounds such as (Z)-3-hexenal, (Z)-3-hexenol, decanal, undecanal, and dodecanal that are typical for green, citrus, orange peel, and coriander odors. In addition to these aldehydes, 3-sulfanyl-hexanal and 3-sulfanyl-hexan-1-ol were discovered for the first time in this herb. The fresh leaves are pungent when they are chewed, although the active compound has never been identified. The pungency of Persicaria hydropiper (L.) Spach (formerly Polygonum hydropiper L., synonym water pepper) is produced by polygodial, a 1,4-dialdehyde derived from drimane terpenoids. We also identified polygodial as the active pungent compound in P. odorata (Lour.) Soják.
The outbreak of COVID-19 has weakened the economy of Australia and its capital market since early 2020. The overall stock market has declined. However, some sectors become highly vulnerable while others continue to perform well even in the crisis period. Given this new reality, we seek to investigate the initial volatility and the sectoral return. In this study, we analyse data for eight sectors such as, transportation, pharmaceuticals, healthcare, energy, food, real estate, telecommunications and technology of the Australian stock market. In doing so, we obtain data from Australian Securities Exchange (ASX) and analysed them based on 'Event Study' method. Here, we use the 10-days window for the event of official announcement of the COVID-19 outbreak in Australia on 27 February 2020. The findings of the study show that on the day of announcement, the indices for food, pharmaceuticals and healthcare exhibit impressive positive returns. Following the announcement, the telecommunications, pharmaceuticals and healthcare sectors exhibit good performance, while poor performance is demonstrated by the transportation industry. The findings are vital for investors, market participants, companies, private and public policymakers and governments to develop recovery action plans for vulnerable sectors and enable investors to regain their confidence to make better investment decisions.
Dividend policy is one of the most important element to measure changes in stock
prices. The relationship between dividend policy and stock price volatility shows
different results based on different studies. This paper focuses on the relationship
between dividend policy and stock price volatility through seminal literature on both
theoretical and empirical evidences from 1989 to 2016. The various consequences of
this relationship depend upon each country specific characteristics, different data
sample and different methodology techniques which utilized by researchers in
developed and developing countries. Most of the studies found negative relationship
between dividend policy and stock price volatility, while developed countries have
more impact on this relationship as compared to developing countries. Based on the
prior literature, this paper argues that the relationship between dividend policy and
stock price volatility is inconsistent and the organization needs to decide the dividend
policy according to their capability and organizational culture.
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.