The effectiveness of price limits in the South African white maize futures market

Sayed, Ayesha; Auret, Christo

doi:10.1080/10293523.2018.1475591

Cited by 11 publications

(5 citation statements)

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“…The dynamic analysis of volatility across futures markets has important policy implications as any imposed regulatory initiative will be unsuccessful if markets are interrelated (Hernandez et al (2014). Sayed and Auret (2018) empirically investigated the effectiveness of price limits in the South African white maize future market over the period March 2010-December 2017 The authors found them to be ineffective and even contributing to excess volatility instead of constraining it. The presence of volatility transmissions and spillover effects between futures markets could help explain why price limits have been ineffective in controlling volatility in the South African white maize futures market.…”

Section: Introductionmentioning

confidence: 99%

Volatility transmission in the South African white maize futures market

Sayed

Auret

2019

Eurasian Econ Rev

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Research in the United States' agricultural futures markets have found maize (what they refer to as corn) to be the commodity that most broadly received and transmitted volatility transmissions. South Africa is the main emerging market for price discovery of maize in Africa, with white maize being the largest and most liquid agricultural commodity futures contract traded on the South African Futures Exchange (SAFEX). This paper examines volatility spillover effects in white maize futures against several other domestic grain and external market futures listed on SAFEX. Using daily return data, a multivariate GARCH approach is employed to study spillover effects between grain futures (white maize, yellow maize, wheat and sunflower seed), currency futures (Dollar/Rand and Euro/Rand), equity futures (JSE Top 40 Index) and interest rate futures (JIBA). A Dynamic Conditional Correlation (DCC) model is used to evaluate the degree of interdependence between futures markets which is measured through a time-variant conditional correlation matrix. The results indicate that the South African futures markets analyzed are highly interrelated, with significant dependence and volatility transmissions being observed. Furthermore, the results also highlight that these interrelations are changing over time. The findings have important implications for portfolio allocations, hedging strategies and policy and regulatory initiatives.

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Section: Introductionmentioning

confidence: 99%

Volatility transmission in the South African white maize futures market

Sayed

Auret

2019

Eurasian Econ Rev

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“…Key among them is Levy, Qadan, and Yagil's (2013) work on Pork Bellies and Oats futures in the US based on a 41-year dataset. More recently, Sayed and Auret (2018) show the same for Maize futures in South Africa.…”

Section: Studies Confirming the Magnet Effectmentioning

confidence: 80%

A survey on the magnet effect of circuit breakers in financial markets

Sifat

Mohamad

2020

International Review of Economics & Finance

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Proponents of circuit breakers justify the practice citing its utility in placating stressed markets, persuading agents to reflect on available information, and to trade rationally. Opponents counter by calling it an infringement on laissez-faire price discovery process citing the lack of conclusive evidence of their effectiveness in market crises. After nearly three decades of theoretical and empirical scrutiny, this discord persists. Most of the empirical focus in this domain revolves around ex-post performance of circuit breakers in cooling off the market, interference in trading, volatility splattering, and delayed assimilation of information. A less explored hypothesis is a potential for traders to hasten trading plans fearing illiquidity or trading blockade. Thus, the existence of the circuit breaker alone can induce its tripping. Known formally as the magnet effect, this hypothesis remains less explored due-inter alia-to paucity of data and methodological limitations. Greater availability of high-frequency datasets in recent times, however, has spurred a growth in empirical works focusing purely on the magnet effect hypothesis. As this nascent subdiscipline in market microstructure grows, this paper undertakes one of the first formal surveys looking to consolidate theoretical and empirical works on magnet effect. Moreover, we discuss methodological challenges and analytic limitations which strain the credibility of academic research findings in this domain; particularly among regulators.

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“…Furthermore, price limits are intended to provide opportunities for investors to obtain good information, hence creating rational decisions in the capital market. Daily stock price limit related to stock return volatility (Abdelzaher & Elgiziry, 2017;Sayed & Auret, 2018;Li et al, 2014;Upadhyaya et al, 2020;Thuy & Thuy, 2019;Soleymani et al, 2017;Siddiqui, 2016;Herwany et al, 2021) stated that limit regulations are not effective in dealing with volatility levels. According to Zhang et al (2016), it was shown that the regulation of price limits increases volatility.…”

Section: Literature Reviewmentioning

confidence: 99%

Decision-making model to predict auto-rejection: An implementation of ARIMA for accurate forecasting of stock price volatility during the Covid-19

Suripto¹

2023

10.5267/j.dsl

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This study aims to determine an accurate forecasting model, especially an error rate of around 0, and to examine how the automatic rejection system reacts to stock price as a result of the pandemic. The statistical clustering method is used for the dataset in form of daily observations, while the sample covers the period of cases before and after COVID-19 pandemic from 02 January 2019 to 20 June 2020 at the Trinitan Minerals and Metal Company. Furthermore, the data used in the estimation are the opening and closing price of returns, which are later processed using SAS analysis tools. It is shown that the most appropriate decision-making processes are those proven to be most effective. Therefore, predicting future events based on a suitable time series model will help policymakers and strategists make decisions and develop appropriate strategic plans regarding the stock market. Meanwhile, 98% of the ARIMA (1,1,1) is a forecasting model which can be applied to predict stock prices. The new approach of this study is an integrated autoregressive moving average used as an attempt to accurately predict stock prices during a pandemic.

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The effectiveness of price limits in the South African white maize futures market

Cited by 11 publications

References 20 publications

Volatility transmission in the South African white maize futures market

Volatility transmission in the South African white maize futures market

A survey on the magnet effect of circuit breakers in financial markets

Decision-making model to predict auto-rejection: An implementation of ARIMA for accurate forecasting of stock price volatility during the Covid-19

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