The empirical linkages among market returns, return volatility, and trading volume: Evidence from the S&P 500 VIX Futures

2020 ◽  
Vol 54 ◽  
pp. 100871 ◽  
Author(s):  
Yu-Sheng Kao ◽  
Hwei-Lin Chuang ◽  
Yu-Cheng Ku
Keyword(s):  
Trading Volume ◽  
Return Volatility ◽  
Market Returns ◽  
Vix Futures

A GARCH Modelling of Volatility and M-GARCH Approach of Stock Market Linkages of North America

2018 ◽  
Vol 19 (6) ◽  
pp. 1538-1553 ◽  
Author(s):  
Ajaya Kumar Panda ◽  
Swagatika Nanda
Keyword(s):  
North America ◽  
Stock Market ◽  
Stock Markets ◽  
Market Integration ◽  
Return Volatility ◽  
Sample Period ◽  
Market Returns ◽  
Stock Market Returns ◽  
Current Period ◽  
Highly Sensitive

The present study attempts to capture the return volatility and the extent of dynamic conditional correlation between the stock markets of North America region. The data contain weekly stock market returns spanning from the second week of 1995 to the fourth week of June 2016. Using univariate ARCH and GARCH approaches, the study finds evidence of return volatility and its persistence within the region. Mexican stock market neither reacts intensely to immediate market fluctuations nor the part of the realized past volatility spill over to the current period, whereas the stock markets of Canada and USA experience high persistence of return volatility and Bermuda stock market returns are highly sensitive to the immediate market fluctuations. Using MGARCH-DCC, this article finds that emerging markets are less linked to the developed market in terms of return and that there also exists a weak co-movement between the stock markets. There is no evidence of market integration throughout the sample period. Correlations tend to spread out equally throughout the sample period, but the co-variances were found to be more volatile during 2008–2010. This article reveals that changes in co-movement are not due to a change in the correlations between markets but is simply due to volatility.

Assessment of Asymmetric Information Cost in Indian Stock Market: A Sectoral Approach

2020 ◽  
pp. 097215091986508
Author(s):  
Aritra Pan ◽  
Arun Kumar Misra
Keyword(s):  
Asymmetric Information ◽  
Stock Prices ◽  
Trading Volume ◽  
Share Price ◽  
Return Volatility ◽  
Information Cost ◽  
Market Conditions ◽  
Price Variation ◽  
Bid Ask Spread ◽  
The Impact

Bid-ask spread, along with profit, also encompass the impact of asymmetric information cost and order processing cost. Asymmetric information influences stock prices with varying degree of investors’ perception. Estimation of asymmetric information cost and its determinants have been explored significantly under low-frequency trading. The literature hardly attempts to study asymmetric information cost under high-frequency trading (HFT). Asymmetric information cost significantly influences bid-ask spread, and hence the nature of its impact under different market conditions needs to be analyzed under HFT. The study attempts to estimate asymmetric information cost in HFT and analyze its determinants under different industry sectors and market conditions. The study followed Affleck-Graves et al. (1994 , The Journal of Finance, 49(4), 1471–1488) model to estimate the asymmetric information cost using 5 minutes interval data for a period of 82 trading days. Information gets reflected in equity through the movement in price, variation in trading volume, and return volatility. The study has found share price, traded volume, return volatility and trading frequency as the major determinants of asymmetric information cost in different market conditions. The findings of the study have significant implications for market microstructure for trading, lowering information asymmetry in market and enhancing market quality.

A THRESHOLD MODEL FOR STOCK RETURN VOLATILITY AND TRADING VOLUME

2000 ◽  
Vol 03 (03) ◽  
pp. 467-472 ◽  
Author(s):  
GIULIA IORI
Keyword(s):  
Transaction Costs ◽  
Trading Volume ◽  
Stock Return ◽  
Cross Correlation ◽  
Threshold Model ◽  
Feedback Mechanism ◽  
Return Volatility ◽  
Volatility Clustering ◽  
Stock Return Volatility ◽  
Future Trading

We propose a model with heterogeneous interacting traders which can explain the observed cross-correlation between stock return volatility and trading volume. Transaction costs are introduced which, by responding to price movements, create a feedback mechanism on future trading and generates volatility clustering.

On the Verges of Overconfidence

2015 ◽  
Vol 29 (4) ◽  
pp. 3-8 ◽  
Author(s):  
Ulrike Malmendier ◽  
Timothy Taylor
Keyword(s):  
Public Policy ◽  
Stock Market ◽  
Capital Structure ◽  
Trading Volume ◽  
Market Returns ◽  
Empirical Measures ◽  
Stock Market Returns ◽  
Managerial Overconfidence ◽  
Rule Out

This symposium provides several examples of overconfidence in certain economic contexts. Michael Grubb looks at “Overconfident Consumers in the Marketplace.” Ulrike Malmendier and Geoffrey Tate consider “Behavioral CEOs: The Role of Managerial Overconfidence.” Kent Daniel and David Hirshleifer discuss “Overconfident Investors, Predictable Returns, and Excessive Trading.” A number of insights and lessons emerge for our understanding of markets, public policy, and welfare. How do firms take advantage of consumer overconfidence? Might government attempts to rule out such practices end up providing benefits to some consumers but imposing costs on others? How are empirical measures of CEO overconfidence related to investment and the capital structure of firms? Can overconfidence among at least some investors help to explain prominent anomalies in stock markets like high levels of trading volume and certain predictable patterns in stock market returns?

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