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Asymmetric Response of Volatility

Asymmetric Response of Volatility
Author: Jun Yu
Publisher:
Total Pages: 29
Release: 2004
Genre: Bayesian statistical decision theory
ISBN:

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Examines the news impact function (NIF) in the context of stochastic volatility models using daily index data on S & P500 and non-parametrically using realized daily volatility based on the high frequency data on the same index.


Derivatives and Asymmetric Response of Volatility to News in Indian Stock Market

Derivatives and Asymmetric Response of Volatility to News in Indian Stock Market
Author: Puja Padhi
Publisher:
Total Pages: 13
Release: 2008
Genre:
ISBN:

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The purpose of this article is to investigate the effect of the introduction of stock index futures on the volatility of the spot equity market and to test the impact of the introduction of the stock index futures contracts, a GARCH model is modified along the lines of GJR-GARCH and EGARCH model, especially to take into account the link between information and volatility. This paper provides the evidence that there is not much change in the volatility pattern after the introduction of futures in the Indian stock market. The impacts of futures trading for the post futures period can be captured by the asymmetric coefficient (gamma), suggest that there is a statistically significant and positive asymmetric effect. Thus the introduction of futures trading has impact on the asymmetric coefficient. It shows the similar pattern for the pre and post futures period. Empirical research can be further expanded by selecting and analyzing high frequency intraday data and the inclusion of additional economic variables in the conditional variance equation.


Asymmetric Covariance, Volatility and the Impact of News

Asymmetric Covariance, Volatility and the Impact of News
Author: Warren G. Dean
Publisher:
Total Pages: 53
Release: 2001
Genre:
ISBN:

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In this paper we investigate whether or not the conditional covariance between stock and market returns is asymmetric in response to good and bad news. Empirical observations such as the mean reversion of stock prices and asymmetric volatility can be readily explained by time varying risk premiums and it is the link between risk premiums and conditional covariance that we explore. Previous research has focussed on time varying betas but we propose that covariance asymmetry is a more powerful method of explaining such observed behaviour. Our model of conditional covariance accommodates both the sign and magnitude of return innovations and we find significant covariance asymmetry that can explain, at least in part, the mean reversion of stock prices and volatility feedback. We find little evidence in support of the leverage hypothesis of Christie (1982) in explaining asymmetric volatility. The results we obtain appear consistent across firm size, firm leverage, and temporal and cross sectional aggregations.


Asymmetric Volatility and Risk in Equity Markets

Asymmetric Volatility and Risk in Equity Markets
Author: Geert Bekaert
Publisher:
Total Pages: 76
Release: 1997
Genre: Investments
ISBN:

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It appears that volatility in equity markets is asymmetric: returns and conditional volatility are negatively correlated. We provide a unified framework to simultaneously investigate asymmetric volatility at the firm and the market level and to examine two potential explanations of the asymmetry: leverage effects and time-varying risk premiums. Our empirical application uses the market portfolio and portfolios with different leverage constructed from Nikkei 225 stocks, extending the empirical evidence on asymmetry to Japanese stocks. Although volatility asymmetry is present and significant at the market and the portfolio levels, its source differs across portfolios. We find that it is important to include leverage ratios in the volatility dynamics but that their economic effects are mostly dwarfed by the volatility feedback mechanism. Volatility feedback is enhanced by a phenomenon that we term covariance asymmetry: conditional covariances with the market increase only significantly following negative market news. We do not find significant asymmetries in conditional betas.


Volatility Asymmetry in Functional Threshold GARCH Model

Volatility Asymmetry in Functional Threshold GARCH Model
Author: Hao Sun
Publisher:
Total Pages: 0
Release: 2020
Genre:
ISBN:

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Modeling volatility is one of the prime objectives of financial time-series analysis. A significant feature encountered in the modeling of financial data is the asymmetric response to the volatility process of unanticipated shocks. With improvements in data acquisition, functional versions of the heteroskedastic models have emerged to deal with the high-frequency observations. Although previous studies have developed some functional time-series methods, it remains a necessity to analyze the variations in the asymmetry of the discrete model and the function model. In this study, we propose a functional threshold GARCH (fTGARCH) model and extend the news impact curve (NIC) and the cumulative impact response function (CIRF) within the functional heteroskedastic framework. We find that the fTGARCH model can describe the asymmetry of the observation data, which are revealed by the sample cross-correlation functions. The slope of the NIC changes with time for functional GARCH class models, and the changes are asymmetrical for the fTGARCH model. Using the generalized CIRF, we can explore the persistent effects of volatility for the functional GARCH class models. By fitting the models to the S&P 500 stock market index, we conclude that the fTGARCH model has some flexibility and superiority in regard to volatility asymmetry.


Asymmetric Return and Volatility Responses to Composite News from Stock Markets

Asymmetric Return and Volatility Responses to Composite News from Stock Markets
Author: Thomas Chinan Chiang
Publisher:
Total Pages: 32
Release: 2016
Genre:
ISBN:

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This paper examines the hypothesis that both stock returns and volatility are asymmetric functions of past information derived from domestic and U.S. stock-market news. The results show the presence of negative autocorrelation, which is consistent with the dominance of positive-feedback trading behavior. By employing a double-threshold autoregressive GARCH model to investigate four major index-return series, the study finds significant evidence to sustain the asymmetric hypothesis of stock returns. Specifically, this paper finds that negative news will cause a decline in national stock returns that is larger than the gain caused by good news of an equivalent magnitude. This also holds true for the conditional variance. The return appears to be more volatile and persistent when bad news hits the market than when good news does.


Volatility and Time Series Econometrics

Volatility and Time Series Econometrics
Author: Mark Watson
Publisher: Oxford University Press
Total Pages: 432
Release: 2010-02-11
Genre: Business & Economics
ISBN: 0199549494

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A volume that celebrates and develops the work of Nobel Laureate Robert Engle, it includes original contributions from some of the world's leading econometricians that further Engle's work in time series economics


Financial and Macroeconomic Connectedness

Financial and Macroeconomic Connectedness
Author: Francis X. Diebold
Publisher: Oxford University Press
Total Pages: 285
Release: 2015-02-03
Genre: Business & Economics
ISBN: 0199338329

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Connections among different assets, asset classes, portfolios, and the stocks of individual institutions are critical in examining financial markets. Interest in financial markets implies interest in underlying macroeconomic fundamentals. In Financial and Macroeconomic Connectedness, Frank Diebold and Kamil Yilmaz propose a simple framework for defining, measuring, and monitoring connectedness, which is central to finance and macroeconomics. These measures of connectedness are theoretically rigorous yet empirically relevant. The approach to connectedness proposed by the authors is intimately related to the familiar econometric notion of variance decomposition. The full set of variance decompositions from vector auto-regressions produces the core of the 'connectedness table.' The connectedness table makes clear how one can begin with the most disaggregated pair-wise directional connectedness measures and aggregate them in various ways to obtain total connectedness measures. The authors also show that variance decompositions define weighted, directed networks, so that these proposed connectedness measures are intimately related to key measures of connectedness used in the network literature. After describing their methods in the first part of the book, the authors proceed to characterize daily return and volatility connectedness across major asset (stock, bond, foreign exchange and commodity) markets as well as the financial institutions within the U.S. and across countries since late 1990s. These specific measures of volatility connectedness show that stock markets played a critical role in spreading the volatility shocks from the U.S. to other countries. Furthermore, while the return connectedness across stock markets increased gradually over time the volatility connectedness measures were subject to significant jumps during major crisis events. This book examines not only financial connectedness, but also real fundamental connectedness. In particular, the authors show that global business cycle connectedness is economically significant and time-varying, that the U.S. has disproportionately high connectedness to others, and that pairwise country connectedness is inversely related to bilateral trade surpluses.