scholarly journals The Volatility Effect of Single-Stock Futures Trading on the Pakistani Stock Market

2013 ◽  
Vol 2 (1) ◽  
pp. 65-93
Author(s):  
Adil Awan ◽  
Amir Rafique

The impact of single-stock futures on spot market volatility is still debated in the finance literature. The aim of this study is to analyze the effect of the introduction of single-stock futures on the volatility of the Karachi Stock Exchange (KSE). We examine changes in the level of volatility and structure after the introduction of single-stock futures, evaluating 24 companies listed on the KSE. The study applies the F-test to determine differences in variance as a traditional measure for volatility and uses GARCH (1,1) as an econometric technique for detecting time-varying volatility. The results show that there is no effect on the volatility level but that changes occur in the structure of volatility after stock futures trading.

2020 ◽  
Vol 1 (1) ◽  
pp. 13-27
Author(s):  
Pedro Pablo Chambi Condori

What happens in the international financial markets in terms of volatility, have an impact on the results of the local stock market financial markets, as a result of the spread and transmission of larger stock market volatility to smaller markets such as the Peruvian, assertion that goes in accordance with the results obtained in the study in reference. The statistical evaluation of econometric models, suggest that the model obtained can be used for forecasting volatility expected in the very short term, very important estimates for agents involved, because these models can contribute to properly align the attitude to be adopted in certain circumstances of high volatility, for example in the input, output, refuge or permanence in the markets and also in the selection of best steps and in the structuring of the portfolio of investment with equity and additionally you can view through the correlation on which markets is can or not act and consequently the best results of profitability in the equity markets. This work comprises four well-defined sections; a brief history of the financial volatility of the last 15 years, a tight summary of the background and a dense summary of the methodology used in the process of the study, exposure of the results obtained and the declaration of the main conclusions which led us mention research, which allows writing, evidence of transmission and spread of the larger stock markets toward the Peruvian stock market volatility, as in the case of the American market to the market Peruvian stock market with the coefficient of dynamic correlation of 0.32, followed by the Spanish market and the market of China. Additionally, the coefficient of interrelation found by means of the model dcc mgarch is a very important indicator in the structure of portfolios of investment with instruments that they quote on the financial global markets.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mincheol Woo ◽  
Meong Ae Kim

The National Pension Service (NPS) of Korea is one of the largest institutional investors in the world and it has been known as the market stabilizer in the Korean stock market. Nevertheless, it is hard to find the research about the impact of the NPS on the futures market. We investigated the effect of the NPS’s trading KOSPI200 futures on the returns, the liquidity and the volatility of the market using the recent ten years’ transaction data. The main findings are as follows. First, the NPS’s net investment flow (NIF) in the KOSPI200 futures market shows the predictability about the returns of both KOSPI200 futures and KOSPI200 spot index. Second, the NPS’s NIF in the KOSPI200 futures market improves the liquidity of the KOSPI market, where the transactions involved in both the spot market and the futures market occur. Third, the NPS’s NIF in the KOSPI200 futures market reduces the volatility of both the KOSPI200 futures market and the KOSPI market. Unlike the prior studies showing that our futures market tends to increase the volatility of the stock market through the volatility transfer, our finding suggests that the NPS’s trading KOSPI200 futures contributes to decreasing the volatility in both markets. To the best of the authors’ knowledge, this paper is the first study that investigates the impact of the NPS’s trading KOSPI200 futures on the KOSPI200 futures market and the stock market. It shows that the NPS plays a role of the market stabilizer in the futures market. In addition, the NPS’s trading KOSPI200 futures also affects the KOSPI stock market, stabilizing it in terms of both the liquidity and the volatility.


2004 ◽  
Vol 29 (4) ◽  
pp. 25-42 ◽  
Author(s):  
Harvinder Kaur

This paper investigates the nature and characteristics of stock market volatility in India. The volatility in the Indian stock market exhibits characteristics similar to those found earlier in many of the major developed and emerging stock markets. Various volatility estimators and diagnostic tests indicate volatility clustering, i.e., shocks to the volatility process persist and the response to news arrival is asymmetrical, meaning that the impact of good and bad news is not the same. Suitable volatility forecast models are used for Sensex and Nifty returns to show that: The ‘day-of-the-week effect’ or the ‘weekend effect’ and the ‘January effect’ are not present while the return and volatility do show intra-week and intra-year seasonality. The return and volatility on various weekdays have somewhat changed after the introduction of rolling settlements in December 1999. There is mixed evidence of return and volatility spillover between the US and Indian markets. The empirical findings would be useful to investors, stock exchange administrators and policy makers as these provide evidence of time varying nature of stock market volatility in India. Specifically, they need to consider the following findings of the study: For both the indices, among the months, February exhibits highest volatility and corresponding highest return. The month of March also exhibits significantly higher volatility but the magnitude is lesser as compared to February. This implies that, during these two months, the conditional volatility tends to increase. This phenomenon could be attributed to probably the most significant economic event of the year, viz., presentation of the Union Budget. The investors, therefore, should keep away from the market during March after having booked profits in February itself. The surveillance regime at the stock exchanges around the Budget should be stricter to keep excessive volatility under check. Similarly, the month of December gives high positive returns without high volatility and, therefore, offers good opportunity to the investors to make safe returns on Sensex and Nifty. On the contrary, in the month of September, i.e., the time when the third quarter corporate results are announced, volatility is higher but corresponding returns are lower. The situation is, therefore, not conducive to investors. The ‘weekend effect’ or the ‘Monday effect’ is not present. For other weekdays, the ‘higher (lower) the risk, higher (lower) the return’ dictum does not hold consistently and Wednesday provides higher returns with lower volatility making it a good day to invest. The domestic investors and the stock exchange administrators do not need to lose sleep over gyrations in the major US markets since there is no conclusive evidence of consistent relationship between the US and the domestic markets. The volatility forecast models presented for Sensex and Nifty can be used to forecast future volatility of these indices.


2018 ◽  
Vol 17 (2_suppl) ◽  
pp. S239-S258 ◽  
Author(s):  
Hisham Al Refai ◽  
Gazi Mainul Hassan

This study examines the impact of market-wide volatility on time-varying risk using the heteroscedastic market model with EGARCH (1,1) specification. Using daily sector returns from the Qatar Stock Exchange (QSE) market over the period 2007–2015, we find that in terms of systematic risk, the large sectors are as vulnerable to overall market volatility as the small ones. In addition, the results reveal evidence for asymmetry in time-varying risk due to the impact of market-wide shocks on sector returns. Specifically, we find that market-wide upswings reduce the systematic risk for industrials, while market-wide downswings increase the systematic risk for real estate, telecommunication and transportation. Our modified model survives a battery of robustness checks.


2011 ◽  
Vol 25 (1) ◽  
pp. 123-137 ◽  
Author(s):  
Gülüzar Kurt Gümüş ◽  
A. Tülay Yücel ◽  
Deniz Karaoğlan ◽  
Şaban Çelik

2009 ◽  
Vol 7 (2) ◽  
pp. 279-295 ◽  
Author(s):  
Johan de Beer

The introduction of single stock futures to a market presents the opportunity to assess an individual company’s response to futures trading directly, in contrast to the market-wide impact obtained from index futures studies. The listed shares of thirty-eight South African companies were evaluated in terms of a possible volatility effect due to the initial trading of their respective single stock futures contacts. A GARCH(1,1) model established a volatility structure (pattern of behaviour) per company. Results, in general, showed a reduction in the level and changes in the structure of spot market volatility post single stock futures.


Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1212
Author(s):  
Pierdomenico Duttilo ◽  
Stefano Antonio Gattone ◽  
Tonio Di Di Battista

Volatility is the most widespread measure of risk. Volatility modeling allows investors to capture potential losses and investment opportunities. This work aims to examine the impact of the two waves of COVID-19 infections on the return and volatility of the stock market indices of the euro area countries. The study also focuses on other important aspects such as time-varying risk premium and leverage effect. This investigation employed the Threshold GARCH(1,1)-in-Mean model with exogenous dummy variables. Daily returns of the euro area stock markets indices from 4th January 2016 to 31st December 2020 has been used for the analysis. The results reveal that euro area stock markets respond differently to the COVID-19 pandemic. Specifically, the first wave of COVID-19 infections had a notable impact on stock market volatility of euro area countries with middle-large financial centres while the second wave had a significant impact only on stock market volatility of Belgium.


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