A Long Run And Short Run Component Model Of Stock Return Volatility Pdf

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a long run and short run component model of stock return volatility pdf

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There is evidence for a double relation between volatility and returns in equity markets. Longer-term fluctuations of volatility mostly reflect risk premiums and hence establish a positive relation to returns.

Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. The impact of subprime mortgage crisis on the short-run and long-run volatility components of the Malaysian stock market Abstract: This study investigates the long-run and short-run movements of two emerging stock market volatilities using a volatility decomposition methodology.

We studied the impact of subprime mortgage crisis on the transitory and permanent volatility components in terms of two empirical stylized facts, the leverage effect and volatility persistence. In order to do so, the long spanning data are separated into three different periods.

For the former stylized fact, the crisis impact on the leverage effect is mainly temporary with no long-run effect to the stock markets. This finding explains that the leverage effect is mostly difficult to adjust in the short-run transitory volatility during the crisis periods.

However with proper risk management and long term strategies, most of the market participants are able to anticipate and handle this news impact in the long-run.

For the latter stylized fact, the crisis has slightly increased the volatility persistence in all the markets. From the viewpoint of heterogeneous market hypothesis, the higher intensity of volatility persistence implies the stock markets are less informational efficient. Article :. DOI: Need Help?

Long-term effects of the asymmetry and persistence of the prediction of volatility: Evidence for the equity markets of Latin America. This article proposes an extension to the CGARCH model in order to capture the characteristics of short-run and long-run asymmetry and persistence, and examine their effects in modeling and forecasting the conditional volatility of the stock markets from the region of Latin America during the period from 2 January to 31 December In the sample analysis, the estimation results of the CGARCH-class model family reveal the presence of short-run and long-run significant asymmetric effects and long-run persistency in the structure of stock price return volatility. The empirical results also show that the use of symmetric and asymmetric loss functions and the statistical test of Hansen are sound alternatives for evaluating the predictive ability of the asymmetric CGARCH models. In addition, the inclusion of long-run asymmetry and long-run persistency in the variance equation improves significantly the out of sample volatility forecasts for emerging stock markets of Argentina and Mexico. The new millennium has witnessed the transformation and fast growth of the equity markets in the emerging economies. In the context of globalization and financial integration, the equity markets of Latin America have experienced astounding growth rates that surpass those of advanced economies.

We study portfolio stock return behavior that exhibits both a positive autocorrelation over short horizons and a negative autocorrelation over long horizons. These autocorrelations are more significant in small size portfolios. Among various forms of temporary components in stock prices, an AR 2 component is the simplest model compatible with this pattern of returns, which yields an ARMA 2,2 model of stock returns. We show that the significance of this model is that it requires the presence of feedback trading, which is a form of irrational trades, and the market's slow adjustment to the market fundamentals, which is consistent with recent modelings of stock prices. We find that the variation of the temporary component becomes greater as the firm size gets smaller.

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Adrian and Joshua V. Adrian , Joshua V.

Chauvet, Marcelle and Senyuz, Zeynep and Yoldas, Emre : What does financial volatility tell us about macroeconomic fluctuations? This paper provides an extensive analysis of the predictive ability of financial volatility measures for economic activity. We construct monthly measures of aggregated and industry-level stock volatility, and bond market volatility from daily returns. We model log financial volatility as composed of a long-run component that is common across all series, and a short-run component. If volatility has components, volatility proxies are characterized by large measurement error, which veils analysis of their fundamental information and relationship with the economy.

Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions.

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  3. Scott M. 04.02.2021 at 22:32

    stock returns, this paper develops and estimates a parsimonious model for idiosyncratic volatility consisting of a short-run and long-run.

  4. Yunca C. 08.02.2021 at 03:51

    Request PDF | Stock Returns and Volatility: Pricing the Short-Run and The short-run component captures market skewness risk, which we interpret as a that a model including short-and long-term volatility components.