The Experts below are selected from a list of 115659 Experts worldwide ranked by ideXlab platform

Chairat Chuwonganant - One of the best experts on this subject based on the ideXlab platform.

  • Market Volatility and stock returns: The role of liquidity providers
    Journal of Financial Markets, 2018
    Co-Authors: Kee H. Chung, Chairat Chuwonganant
    Abstract:

    Abstract This study shows that Market Volatility affects stock returns both directly and indirectly through its impact on liquidity provision. The negative relation between Market Volatility and stock returns arises not only from greater risk premiums but also greater illiquidity premiums that are associated with higher Market Volatility. Consistent with our expectation, we also find that stock returns are more sensitive to Volatility shocks in the high-frequency trading era, and after the regulatory changes in the U.S. Markets that increased competition between public traders and Market makers, reduced the tick size, and decreased the role of Market makers.

  • Market Volatility and Stock Returns: The Role of Liquidity Providers
    SSRN Electronic Journal, 2017
    Co-Authors: Kee H. Chung, Chairat Chuwonganant
    Abstract:

    This study shows that Market Volatility affects stock returns both directly and indirectly through its impact on liquidity provision and the negative relation between Market Volatility and stock returns arises not only from greater risk premiums but also greater illiquidity premiums that are associated with higher Market Volatility. In particular, we show that a stock’s return is more sensitive to unexpected changes in Market Volatility when its liquidity disappears more in response to Volatility shocks, which indicates that liquidity providers play an important role in determining the effect of Market Volatility on stock returns. Stock returns are more sensitive to Volatility shocks in the high-frequency trading era, and after the regulatory changes in the US Markets that increased competition between public traders and Market makers, reduced the tick size, and decreased the role of Market makers.

Libo Yin - One of the best experts on this subject based on the ideXlab platform.

  • Understanding stock Market Volatility: What is the role of U.S. uncertainty?
    The North American Journal of Economics and Finance, 2019
    Co-Authors: Tong Fang, Libo Yin
    Abstract:

    Abstract This study investigates the spillover of U.S. economic uncertainty on the stock Market Volatility of six industrialized and three emerging-Market countries, using a bivariate GARCH-MIDAS model. We consider three different U.S. uncertainty indices: economic policy uncertainty (EPU), financial uncertainty (FU), and news implied uncertainty (NVIX). Our results indicate that EPU is positively associated with the industrialized countries’ stock Market Volatility; FU does not appropriately predict long-term stock Market Volatility; and NVIX is the more powerful predictor of Market Volatility, with higher NVIX leading to lower Volatility. Our study highlights a new channel of Market contagion and furthers our understanding of the sources of stock Market Volatility.

  • Does NVIX matter for Market Volatility? Evidence from Asia-Pacific Markets
    Physica A: Statistical Mechanics and its Applications, 2018
    Co-Authors: Tong Fang, Libo Yin
    Abstract:

    Abstract Forecasting financial Market Volatility is an important issue in the area of econophysics, and revealing the determinants of the Market Volatility has drawn much attentions of the academics. In order to better predict Market volatilities, we use news-based implied Volatility (NVIX) to measure uncertainty, and examine the predictive power of NVIX on the stock Market Volatility in both long and short-term among Asia-Pacific Markets via GARCH-MIDAS model. We find that NVIX does not well explain long-term Volatility variants in the full sample period, and it is positively associated with Market Volatility through a subsample analysis starting from the Financial Crisis. We also find that NVIX is more efficient in determining short-term Volatility than the long-term Volatility, indicating that the impact of NVIX is short-lived and information that investors concern could be quickly reflected in the stock Market volatilities.

Dengpan Luo - One of the best experts on this subject based on the ideXlab platform.

  • Market Volatility and Mutual Fund Cash Flows
    2003
    Co-Authors: Dengpan Luo
    Abstract:

    This paper examines the relation between Market Volatility and monthly mutual fund cash flows. We find that bond fund investors in the period of 1984 through 1998 do not respond to past stock Market Volatility at the aggregate level after we take into account the persistency of Volatility over time and the relation between risks and returns. On the other hand, stock fund investors respond negatively to concurrent and past long term (semi-annual and annual) Market Volatility. Stock fund investors' Volatility timing behavior explains why fund managers decrease Market exposure during periods of high Market Volatility. We also find that the negative relation between stock fund flows and Market Volatility is not entirely driven by the persistency of Volatility over time or the relation between risks and returns. Using semi-variance of daily stock Market returns, we find no evidence that investors are only concerned about downside Volatility. Both upside Volatility and downside Volatility have negative impact on subsequent stock fund flows. We also find that stock fund flows in our sample period have strong positive impact on the subsequent Market

  • Market Volatility and Mutual Fund Cash Flows
    2003
    Co-Authors: Dengpan Luo
    Abstract:

    This paper examines the relation between Market Volatility and monthly mutual fund cash flows. We find that bond fund investors in the period of 1984 through 1998 do not respond to past stock Market Volatility at the aggregate level after we take into account the persistency of Volatility over time and the relation between risks and returns. On the other hand, stock fund investors respond negatively to concurrent and past long term (semi-annual and annual) Market Volatility. Stock fund investors' Volatility timing behavior explains why fund managers decrease Market exposure during periods of high Market Volatility. We also find that the negative relation between stock fund flows and Market Volatility is not entirely driven by the persistency of Volatility over time or the relation between risks and returns. Using semi-variance of daily stock Market returns, we find no evidence that investors are only concerned about downside Volatility. Both upside Volatility and downside Volatility have negative impact on subsequent stock fund flows. We also find that stock fund flows in our sample period have strong positive impact on the subsequent Market Volatility. It provides some evidence that the momentum of mutual fund investors, often referred to as "noisy traders", do destabilize the overall stock Market to some extent.

Kee H. Chung - One of the best experts on this subject based on the ideXlab platform.

  • Market Volatility and stock returns: The role of liquidity providers
    Journal of Financial Markets, 2018
    Co-Authors: Kee H. Chung, Chairat Chuwonganant
    Abstract:

    Abstract This study shows that Market Volatility affects stock returns both directly and indirectly through its impact on liquidity provision. The negative relation between Market Volatility and stock returns arises not only from greater risk premiums but also greater illiquidity premiums that are associated with higher Market Volatility. Consistent with our expectation, we also find that stock returns are more sensitive to Volatility shocks in the high-frequency trading era, and after the regulatory changes in the U.S. Markets that increased competition between public traders and Market makers, reduced the tick size, and decreased the role of Market makers.

  • Market Volatility and Stock Returns: The Role of Liquidity Providers
    SSRN Electronic Journal, 2017
    Co-Authors: Kee H. Chung, Chairat Chuwonganant
    Abstract:

    This study shows that Market Volatility affects stock returns both directly and indirectly through its impact on liquidity provision and the negative relation between Market Volatility and stock returns arises not only from greater risk premiums but also greater illiquidity premiums that are associated with higher Market Volatility. In particular, we show that a stock’s return is more sensitive to unexpected changes in Market Volatility when its liquidity disappears more in response to Volatility shocks, which indicates that liquidity providers play an important role in determining the effect of Market Volatility on stock returns. Stock returns are more sensitive to Volatility shocks in the high-frequency trading era, and after the regulatory changes in the US Markets that increased competition between public traders and Market makers, reduced the tick size, and decreased the role of Market makers.

J. Jimmy Yang - One of the best experts on this subject based on the ideXlab platform.

  • News sentiment and stock Market Volatility
    Review of Quantitative Finance and Accounting, 2021
    Co-Authors: Yen-ju Hsu, J. Jimmy Yang
    Abstract:

    This study investigates the effect of news sentiment on stock Market Volatility using the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and measures the asymmetric effect with the GJR-GARCH model. We adopt patented linguistic analysis that considers the semantic orientation process to quantify financial news that may attract investor attention. This study distinguishes between unclassified Market news sentiment and macroeconomic-related news effects. The evidence suggests that both contemporaneous and lagged news are determinants of Market Volatility. The effect is especially strong with the Market aggregate news sentiment index ( ANSI ) and the negative ANSI , particularly during the 2008–2009 financial crisis period. This analysis of news sentiment improves the accuracy of in-sample and out-of-sample Volatility forecasting.