Program Trading and Intraday Volatility in the Stock Index Futures Market and Spot Market: The Case of Korea

Min, Jae Hoon

doi:10.1300/j098v04n03_04

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…Volatility of stock market can be explained by number of factors i.e. liquidity risk (Min, 2002; Jun et al , 2003; Lesmond, 2005), information asymmetry (Tse et al , 2003), segmentation (Jong and De Roon, 2004; Yeh et al ,2002), inventory (Thille, 2005), number of informed agents (Du and Wei, 2004) and numerous regulations with their associated costs (Green et al , 2000). The impact of these factors on the market performance can be explored by analyzing volatility of market that is conditional variance representing risk measure of security.…”

Section: Introductionmentioning

confidence: 99%

Foresight for stock market volatility – a study in the Indian perspective

Dixit

Agrawal

2019

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Purpose Volatility is a permanent behavior of the stock market around the globe. The presence of the volatility in the stock price makes it possible to earn abnormal profits by risk seeking investors and creates hesitancy among risk averse investors as high volatility means high return with high risk. Investors always consider market volatility before making any investment decisions. Random fluctuations are termed as volatility of stock market. Volatility in financial markets is reflected because of uncertainty in the price and return, unexpected events and non-constant variance that can be measured through the generalized autoregressive conditional heteroscedasticity family models and that will give an insight for investment decision-making. Design/methodology/approach Daily data of the closing value of Bombay Stock Exchange (BSE) (Sensex) and National Stock Exchange (NSE) (Nifty) from April 1, 2011 to March 31, 2017 is collected through the web-portal of BSE (www.bseindia.com) and NSE (www.nseindia.com) for the analysis purpose. Findings The outcome of the study suggested that P-GARCH model is most suitable to predict and forecast the stock market volatility for both the markets. Research limitations/implications Future research can be extended to other stock market segments and sectoral indices to explore and forecast the volatility to establish a trade-off between risk and return. Originality/value The results of previous studies available are not conducive to this research, and very limited scholarly work is available in the Indian context, so required to be re-explored to identify the appropriate model to predict market volatility.

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Section: Introductionmentioning

confidence: 99%

Foresight for stock market volatility – a study in the Indian perspective

Dixit

Agrawal

2019

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“…There are multiple factors which explain the stock returns volatility including information asymmetry (Tse et al , 2003), liquidity risk (Jun et al , 2003; Min, 2002; Lesmond, 2005), segmentation (Yeh et al , 2002; Jong and De Roon, 2004), inventory (Thille, 2005), number of informed agents (Du and Wei, 2004), quality of the banking system (Dellas and Hess, 2002) and the number of regulations and their imbedded costs (Green et al , 2000). Several factors positively affect volatility of stock returns while the remaining ones negatively affect the stock returns.…”

Section: Introductionmentioning

confidence: 99%

Volatility spillovers between crude oil price and stock markets: evidence from BRIC countries

Bagchi¹

2017

IJOEM

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Purpose The purpose of this paper is to examine the dynamic relationship between crude oil price volatility and stock markets in the emerging economies like BRIC (Brazil, Russia, India and China) countries in the context of sharp continuous fall in the crude oil price in recent times. Design/methodology/approach The stock price volatility is partly explained by volatility in crude oil price. The author adopt an Asymmetric Power ARCH (APARCH) model which takes into account long memory behavior, speed of market information, asymmetries and leverage effects. Findings For Bovespa, MICEX, BSE Sensex and crude oil there is an asymmetric response of volatilities to positive and negative shocks and negative correlation exists between returns and volatility indicating that negative information will create greater volatility. However, for Shanghai Composite positive information has greater effect on stock price volatility in comparison to negative information. The study results also suggest the presence long memory behavior and persistent volatility clustering phenomenon amongst crude oil price and stock markets of the BRIC countries. Originality/value The present study makes a number of contributions to the existing literature in the following ways. First, the author have considered crude oil prices up to January 31, 2016, so that the study can reflect the impact of declining trend of crude oil prices on the stock indices which is also regarded as “new oil price shock” to measure the volatility between crude oil price and stock market indices of BRIC countries. Second, the volatility is captured by APARCH model which takes into account long memory behavior, speed of market information, asymmetries and leverage effects.

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References

2016

Dynamic Linkages and Volatility Spillover

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Program Trading and Intraday Volatility in the Stock Index Futures Market and Spot Market: The Case of Korea

Cited by 3 publications

References 17 publications

Foresight for stock market volatility – a study in the Indian perspective

Foresight for stock market volatility – a study in the Indian perspective

Volatility spillovers between crude oil price and stock markets: evidence from BRIC countries

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