Modelling Regulatory Change V's Volume, of Trading Effects in HSIF and HSI Volatility

2008 ◽  
Vol 11 (01) ◽  
pp. 47-59 ◽  
Author(s):  
Gerard Gannon ◽  
Siu Pang Au-Yeung
Keyword(s):  
Trading Volume ◽  
Structural Breaks ◽  
Structural Changes ◽  
Systematic Approach ◽  
Futures Market ◽  
Likelihood Ratio Tests ◽  
Market Volatility ◽  
Regulatory Change ◽  
Index Futures ◽  
Dummy Variables

In an earlier paper, we adopted a bi-variate BEKK–GARCH framework and employed a systematic approach to examine structural breaks in the Hang Seng Index and Index Futures market volatility. Switching dummy variables were included and tested in the variance equations to check for any structural changes in the autoregressive volatility structure due to the events that have taken place in the Hong Kong market surrounding the Asian markets crisis. In this paper, we include measures of daily trading volume from both markets in the estimation. Likelihood ratio tests indicate the switching dummy variables become insignificant and the GARCH effects diminish but remain significant. There is some evidence that the Sequential Arrival of Information Model (SIM) provides a platform to explain these market induced effects when volume of trade is accounted for.

Volatility persistence and trading volume in an emerging futures market

2010 ◽  
Vol 11 (3) ◽  
pp. 296-309 ◽  
Author(s):  
Pratap Chandra Pati ◽  
Prabina Rajib
Keyword(s):  
Capital Market ◽  
Trading Volume ◽  
Price Volatility ◽  
Futures Market ◽  
Market Volatility ◽  
Leverage Effect ◽  
Time Varying ◽  
Conditional Volatility ◽  
Content Type ◽  
Futures Contract

PurposeThe purpose of this paper is to estimate time‐varying conditional volatility, and examine the extent to which trading volume, as a proxy for information arrival, explain the persistence of futures market volatility using National Stock Exchange S&P CRISIL NSE Index Nifty index futures.Design/methodology/approachTo estimate the volatility and capture the stylized facts of fat‐tail distribution, volatility clustering, leverage effect, and mean‐reversion in futures returns, appropriate ARMA‐generalized autoregressive conditional heteroscedastic (GARCH) and ARMA‐EGARCH models with generalized error distribution have been used. The ARMA‐EGARCH model is augmented by including contemporaneous and lagged trading volume to determine their contribution to time‐varying conditional volatility.FindingsThe paper finds evidence of leverage effect, which indicates that negative shocks increase the futures market volatility more than positive shocks of the same magnitude. In addition, the results indicate that inclusion of both contemporaneous and lagged trading volume in the GARCH model reduces the persistence in volatility, but contemporaneous volume provides a greater reduction than lagged volume. Nevertheless, the GARCH effect does not completely vanish.Practical implicationsResearch findings have important implications for the traders, regulatory bodies, and practitioners. A positive volume‐price volatility relationship implies that a new futures contract will be successful only to the extent that there is enough price uncertainty associated with the underlying asset. Higher trading volume causes higher volatility; so, it suggests the need for greater regulatory restrictions.Originality/valueEquity derivatives are relatively new phenomena in Indian capital market. This paper extends and updates the existing empirical research on the relationship between futures price volatility and volume in the emerging Indian capital market using improved methodology and recent data set.

The Impact of Introduction of QFIIs Trading on the Lead and Volatility Behavior: Evidence for Taiwan Index Futures Market

2006 ◽  
Vol 09 (01) ◽  
pp. 25-49 ◽  
Author(s):  
Wen-Hsiu Kuo ◽  
Shih-Ju Chan
Keyword(s):  
Futures Market ◽  
Market Volatility ◽  
Spot Markets ◽  
Noise Traders ◽  
Index Futures ◽  
Market Inefficiency ◽  
Foreign Institutional Investors ◽  
The Impact ◽  
Opening Up

This paper investigates whether the introduction of trading by qualified foreign institutional investors (QFIIs) has impacted the lead and volatility behavior of the futures market when the macroeconomic effects and some major economic events are controlled. First, we detect that some market inefficiency exists in Taiwan index futures market. Second, the evidence shows a strengthening in the lead of index futures over index spot markets following the introduction of trading by QFIIs. Third, we find evidence of an increase in the level of futures market volatility, implying that the quantity of information flowing into the futures market increases following the onset of trading by QFIIs. Finally, the asymmetries do not reduce after the opening up of the futures market to QFIIs. This finding is inconsistent with the view that the introduction of informed foreign investors may improve the reliability and quality of information and mitigate the effect of noise traders on market volatility.

scholarly journals Impact of index derivatives on Indian stock market volatility-an application of arch and GARCH model

2008 ◽  
Vol 6 (3) ◽  
pp. 39-44
Author(s):  
S. V. Ramana Rao ◽  
Naliniprava Tripathy
Keyword(s):  
Stock Market ◽  
Structural Changes ◽  
Garch Model ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Index Futures ◽  
Market Factors ◽  
Indian Stock Market ◽  
The Impact ◽  
Derivative Products

The present study examined the impact of introduction of index futures derivative and index option derivative on Indian stock market by using ARCH and GARCH model to capture the time varying nature of volatility presence in the data period from October 1995 to July 2006. The results reported that the introduction of index futures and index options on the Nifty has produced no structural changes in the conditional volatility of Nifty but however the market efficiency has been improved after the introduction of the derivative products. The study concludes that financial derivative products are not responsible for increase or decrease in spot market volatility, but there could be other market factors which influenced the market volatility

scholarly journals Expiration-Day Effects of Index Futures in a Frontier Market: The Case of Ho Chi Minh Stock Exchange

2021 ◽  
Vol 10 (1) ◽  
pp. 3
Author(s):  
Anh Thi Kim Nguyen ◽  
Loc Dong Truong ◽  
H. Swint Friday
Keyword(s):  
Regression Models ◽  
Trading Volume ◽  
Stock Exchange ◽  
Market Volatility ◽  
Market Returns ◽  
Index Futures ◽  
Daily Return ◽  
Market Return ◽  
Index Stock ◽  
Level Of Significance

This study employs OLS, GARCH and EGARCH regression models to test the expiration-day effects of index stock futures on market returns, volatility and trading volume for the Ho Chi Minh Stock Exchange (HOSE). Data used in this study is from a daily return series of the VN30-Index for the period from 10August 2017 through 30 June 2020. The results derived from GARCH(1,1) and EGARCH(1,1) models consistently confirm that Index futures expiration-day effects on market returns exists in the HOSE. Specifically, the average market return for expiration days is significantly lower than other trading days, by 0.13% at the 5% level of significance. However, the results obtained from the regression models indicate that the expiration-day has no impact on market volatility and trading volume.

Measuring the Toxicity of Order Flow using High Frequency Data: The Case of KOSPI200 Index Futures

2014 ◽  
Vol 22 (1) ◽  
pp. 117-139
Author(s):  
Ki Yool Ohk ◽  
Ming Wu
Keyword(s):  
High Frequency ◽  
Futures Market ◽  
Informed Trading ◽  
Market Volatility ◽  
High Frequency Data ◽  
Order Flow ◽  
Frequency Data ◽  
Conditional Probability Distribution ◽  
Index Futures ◽  
Probability Of Informed Trading

This study presents a new informed trading probability measure VPIN (Volume-Synchronized Probability of Informed Trading) to estimate toxic order flow of KOSPI200 index futures in a high frequency world. This measure does not require to estimate non-observable parameters as PIN. Also, it is estimated based on volume time, so it can estimate toxicity of order flow in a high frequency world. We show a relation between KOSPI200 index futures VPIN and futures market volatility using correlation and conditional probability distribution. A main empirical result is that persistently high VPIN signifies a high risk of subsequent large futures market volatility. It means that VPIN is a useful measure to estimate a toxicity induced volatility.

CROSS-MARKET FINANCIAL RISK ANALYSIS: AN AGENT-BASED COMPUTATIONAL FINANCE

2011 ◽  
Vol 10 (03) ◽  
pp. 563-584 ◽  
Author(s):  
XIONG XIONG ◽  
MEI WEN ◽  
WEI ZHANG ◽  
YONG JIE ZHANG
Keyword(s):  
Stock Market ◽  
Futures Market ◽  
Market Volatility ◽  
Computational Finance ◽  
Future Market ◽  
Investment Strategies ◽  
Chinese Stock Market ◽  
Stock Market Volatility ◽  
Agent Based ◽  
Index Futures

Using the method of agent-based computational finance, this paper designs ten experiments to examine the impacts of the index futures market, typical investment strategies, and different trading mechanisms on the volatility of the Chinese stock market, taking into account the behavior of investors. We have the following results. First, the volatility of the stock market decreases with the index future market and cross-market arbitrageurs. Second, different investment strategies have different effects on stock market volatility. In many cases, both market-imitating and stop-loss strategies can increase stock market volatility. Third, the mechanism of price limits for the index futures market can help to stabilize the fluctuation of the stock market.

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