Volatility and Market Risk of Rubber Price in Malaysia: Pre- and Post-Global Financial Crisis

Goh, Han Hwa; Tan, Kim Leng; Khor, Chia Ying; Ng, Sew Lai

doi:10.1007/s40953-016-0037-4

Cited by 26 publications

(15 citation statements)

References 14 publications

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“…While studies have been done on an aggregate basis, the less attended sub-period analysis seems essential to capture the dynamism in the market. Specifically, while the impact of financial crisis has been studied with respect to other phenomena like weak form market efficiency (Jain, Vyas & Roy, 2013), investment strategies (Vardhan, Sinha & Vij, 2015) and price volatility (Goh, Tan, Khor & Ng, 2016), its impact on the Indian stock market with respect to market anomalies has not been addressed. Besides, while robustness has been checked with respect to different measures of value —book-to-market/earnings-to-price/dividends-to-price/cash’’flow-to-price (Sehgal & Balakrishnan, 2013) —robustness to different methods of calculating returns (equal-weighted/value-weighted based on trimmed/winsorized data) remain under-attended (an exception to this is the study by Manjunatha & Mallikarjunappa [2011] who study both equal-weighted and value-weighted returns).…”

Section: Research Gapsmentioning

confidence: 99%

Changing Nature of the Value Premium in the Indian Stock Market

Harshita

Singh

Yadav

2018

Vision

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The article aims to test whether the nature of value premium has changed over time in the Indian stock market. Contrary to the existing literature on the Indian market, the analysis suggests that post the global crisis, the value premium has nearly ceased to exist. Using price-to-book (P/B) ratio as the proxy, the findings are robust to employing capital asset pricing model (CAPM) and the Fama and French three-factor (FF) model, and five alternative measures of returns: equal-weighted (whole and trimmed sample) and value-weighted (whole, trimmed and winsorized sample). Chow test suggests that the model parameters have changed over time. While the decrease in premium has been discovered for other countries, there was no such study in the Indian context. This is the first attempt of its kind. The findings have important implications for academia and investors.

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Section: Research Gapsmentioning

confidence: 99%

Changing Nature of the Value Premium in the Indian Stock Market

Harshita

Singh

Yadav

2018

Vision

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“…It can follow because of the presence of outliers (very small or very large). The GARCH model (Goh and Khor C 2016) is one of the most advanced statistical techniques which is applied in volatility. It is used to analyze forecast volatility.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Forecasting of Sunspots Cycles: An Application of ARMA (p, q)-GARCH (1, 1) Model

Zaffar¹

2021

Preprint

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The influence of the earth climatic condition of oscillations of solar activity is measurable only in the long run duration. Modeling of the sunspots is an initial role for the mankind utilization of the benefits because solar activity has influenced on the earth’s climates. Time series analysis and modeling have proved to stick out amidst with other statistical tools when estimating and predicting solar activities. This study emphasis on the appropriateness of the generalized autoregressive conditional heteroskedasticity (GARCH) models with specification autoregressive ARMA (p, q) process in terms of their performance for delivering volatility forecasts for Sunspot cycles. In this study, individual sunspots cycle’s ranging from cycle 1st to 24th (1755–2019) are considered. To notice the appropriateness of Autoregressive Conditional Heteroscedastic (ARCH) effect on sunspot cycles data, Lagrange Multiplier test is used. ARMA (p, q)-GARCH (1, 1) process expresses leptokurtic that is fat and heavy tail (values are strongly correlated to each other). The sunspot cycles ARMA (p, q)-GRACH (1, 1) process expresses the positive skewness except cycles 4th and 19th. Most of the Sunspot cycles (1st, 4th, 12th, 13th, 14th, 15th, 16th, 19th, 20th, 23rd and 24th ) follow Auto-Regressive and moving Average (ARMA (2, 2))-GARCH (1, 1). Sunspot cycles (5th, 6th, 7th and 15th ) follow ARMA (3, 3)-GARCH model. Whereas the cycles (2nd and 11th ) show appropriate model is ARMA (5, 1) -GARCH (1, 1) process. ARMA (5, 3) -GARCH (1, 1) process expresses cycles (18th and 19th ). The ARMA (2, 2)-GARCH (1, 1) stationary volatility model expresses the finest forecasting model as compared with other models. Though, ARMA (2, 2)-GARCH (1, 1) is the adequate model for estimation and forecasting most of the sunspot cycles. The results that are obtained by this study are very beneficial for observing the influence of solar activity on the earth's climate.

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“…From the very beginning of global meltdown of 2008, there have been wild fluctuations in the prices of most commodities including rubber. The Financial Crisis has caused some distractions in the future supply and demand for natural rubber that has led to higher price volatility (Goh et al , 2016). Though there has been an uptrend in the prices of rubber in general, it has been susceptible to serious price fluctuations over the years, which is more severe than those were prevailed during pre-crisis days.…”

Section: Introductionmentioning

confidence: 99%

Recession effect in pricing efficiency of rubber futures: the emerging market’s experience

Nair

2019

JADEE

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Purpose The purpose of this paper is to investigate the recession effects in market efficiency of natural rubber futures contracts traded in India. Design/methodology/approach The research draws inferences from Granger causality and Engle–Granger cointegration tests, which are administered separately on 14 year daily price data spanning into two distinct, non-overlapping time series of 2004–2008 and 2009–2017. Findings Analysis shows that rubber futures market is informationally efficient in price discovery. The results of cointegartion tests indicate that a long-term relationship does exist between futures and spot prices of the natural rubber in India. The recession effects in the market efficiency of rubber futures contracts are evident from the increase in optimal hedge ratios estimated with the cointegration methodology. Research limitations/implications The study pursues a simple cointegration methodology to assess the causal relations between spot and futures market prices in the Indian context. Future studies investigating the long-run causal relations, with error correction framework, between spot and future prices of rubber from other leading rubber producing countries can validate the findings more on this issue. Practical implications The research expects to pass on vital information inputs on the implications of future contracts to rubber traders for managing their portfolios. The study of this kind definitely will be a great help to farmers and exporters who are potentially interested in gaining access to a hedging vehicle. Originality/value The paper is unique in terms of understanding the effects of economic recession in information efficiency of futures market. Moreover, a limited number of studies have explored the functional utilities of rubber futures in emerging market context.

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Volatility and Market Risk of Rubber Price in Malaysia: Pre- and Post-Global Financial Crisis

Cited by 26 publications

References 14 publications

Changing Nature of the Value Premium in the Indian Stock Market

Changing Nature of the Value Premium in the Indian Stock Market

Modeling and Forecasting of Sunspots Cycles: An Application of ARMA (p, q)-GARCH (1, 1) Model

Recession effect in pricing efficiency of rubber futures: the emerging market’s experience

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