Volatility Clustering in Stock Returns at Low Frequencies PDF Download
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Author: Ben Jacobsen
Publisher:
ISBN:
Category :
Languages : en
Pages : 31
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Book Description
Author: Ben Jacobsen
Publisher:
ISBN:
Category :
Languages : en
Pages : 31
Get Book Here
Book Description
Author: Ben Jacobsen
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
We investigate volatility clustering using a modeling approach based on the temporal aggregation results for generalized autoregressive conditional heteroscedasticity (GARCH) models in Drost and Nijman [Econometrica, 1993]. Our findings highlight that volatility clustering, contrary to widespread belief, is not only present in high-frequency financial data. Monthly data also exhibit significant serial dependence in the second moments. We show that the use of temporal aggregation to estimate low-frequency models reduces parameter uncertainty substantially.
Author: Allan Timmermann
Publisher:
ISBN:
Category : Rate of return
Languages : en
Pages : 32
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Author: Michel Crouhy
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Encompassing a very broad family of ARCH-GARCH models we show that heteroskedasticity, already well documented for the US market, is a worldwide phenomenon. The AT-GARCH (1,1) model, where volatility rises more in response to bad news than to good news, and where news is considered bad only below a certain level, is found to be a remarkably robust representation of worldwide stock market returns. The residual structure is then captured by extending ATGARCH (1,1) to an hysteresis model, HGARCH, where we model structured memory effects from past innovations. Obviously, this feature relates to the psychology of the markets and the way traders process information. For the French stock market we show that a shock of either sign may affect volatility differently, depending on the recent past being characterized by either all positive or all negative returns. In the same way a longer term trend of either sign may also influence the impact on volatility of current innovations. It is found that bad news is discounted very quickly in volatility, this effect is reinforced when it comes after a negative trend in the stock index. On the opposite, good news has a very small impact on volatility except when it is clustered over a few days, which in this case reduces volatility substantially.
Author: Yi Xue
Publisher:
ISBN:
Category :
Languages : en
Pages : 61
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Book Description
Volatility clustering, with autocorrelations of the hyperbolic decay rate, is unquestionably one of the most important stylized facts of financial time series. This paper presents a market microstructure model, that is able to generate volatility clustering with hyperbolic autocorrelations through traders with multiple trading frequencies using Bayesian information updating in an incomplete market. The model illustrates that signal extraction, which is induced by multiple trading frequency, can increase the persistence of the volatility of returns. Furthermore, we show that the local temporal memory of the underlying time series of returns and their volatility varies greatly with the number of traders in the market.
Author: Thomas Lux
Publisher:
ISBN: 9783931052027
Category :
Languages : en
Pages : 28
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Author: Grant Richard McQueen
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
We develop a preference-based equilibrium asset pricing model that explains low-frequency conditional volatility. Similar to Barberis, Huang, and Santos (2001), agents in our model care about wealth changes, experience loss aversion, and keep a mental scorecard that affects their level of risk aversion. A new feature of our model is that when perturbed by unexpected returns, investors become temporarily more sensitive to news. Gradually investors become accustomed to the new level of wealth, restoring prior levels of risk aversion and news sensitivity. The state-dependent sensitivity to news creates the type of volatility clustering found in low-frequency stock returns. We find empirical support for our model`s predictions that relate the scorecard to conditional volatility and skewness.
Author: Georg Michael Rockinger
Publisher:
ISBN: 9782854185218
Category :
Languages : en
Pages : 48
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Résumé en anglais
Author: Shahid Ahmed
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
This study is an attempt to model the volatility of stock returns in Indian market for the period 1997-2006 using GARCH, TARCH and E-GARCH. Results point out that returns exhibit persistence and volatility clustering in both NSE Nifty and BSE Sensex. Asymmetric volatility effect has been observed in both the series using TARCH and E-GARCH model. While forecasting returns it is found that GARCH-M performs better compared to alternative econometric models, namely, RW, OLS, GARCH, GARCH-M, TARCH and E-GARCH models. It is revealed that one-step ahead forecast improves by using GARCH and its variant models, which goes against the concept of random walk hypothesis. Results of this study also indicate that certain anomalies still exist which makes the stock market inefficient. In this context, SEBI is expected to play proactive role in a manner, which makes market capable to value the intrinsic price of assets.
Author: Radovan Vojtko
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Volatility clustering is a well-known effect in equity markets. In simple meaning, volatility clustering refers to a tendency of large changes in asset prices to follow large changes and small changes in asset prices to follow small changes. We tested two hypotheses: (1) firstly, if there is a volatility clustering present in equity factor strategies, (2) secondly, whether past factor volatility predicts future factor performance. We were able to confirm the first hypothesis. However, a factor allocation trading strategy based on volatility predictability doesn't perform well.
