Volatility Clustering, Asymmetry and Hysteresis in Stock Returns
Volatility Clustering, Asymmetry and Hysteresis in Stock Returns

Author: Michel Crouhy

Publisher:

Published: 1998

Total Pages:

ISBN-13:

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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.

Volatility Clustering in Aggregate Stock Market Returns
Volatility Clustering in Aggregate Stock Market Returns

Author: Shahid Ahmed

Publisher:

Published: 2015

Total Pages: 0

ISBN-13:

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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.

Volatility Clustering, Risk-Return Relationship and Asymmetric Adjustment in Canadian Housing Markets
Volatility Clustering, Risk-Return Relationship and Asymmetric Adjustment in Canadian Housing Markets

Author: Pin-te Lin

Publisher:

Published: 2015

Total Pages:

ISBN-13:

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This study applies a Lagrange Multiplier (LM) test for the AutoRegressive Conditional Heteroskedasticity (ARCH) effects and an Exponential Generalized Autoregressive Conditional Heteroskedasticity-in-Mean (EGARCH-M) model to assess whether regional house prices in Canada exhibit financial characteristics similar to stock indices. Volatility clustering, positive risk-return relationships, and leverage effects are empirically shown to exist in the majority of provincial housing markets of Canada. These volatility behaviors, which reflect regional idiosyncrasies, are further found to differ across provinces. More densely populated provinces exhibit stronger volatility clustering of house prices. The existence of these volatility patterns similar to stock indices has important implications ranging from proper portfolio management to government policy.

Stock Market Volatility
Stock Market Volatility

Author: Greg N. Gregoriou

Publisher: CRC Press

Published: 2009-04-08

Total Pages: 654

ISBN-13: 1420099558

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Up-to-Date Research Sheds New Light on This Area Taking into account the ongoing worldwide financial crisis, Stock Market Volatility provides insight to better understand volatility in various stock markets. This timely volume is one of the first to draw on a range of international authorities who offer their expertise on market volatility in devel

Introduction to Econometrics
Introduction to Econometrics

Author: James H. Stock

Publisher: Prentice Hall

Published: 2015

Total Pages: 0

ISBN-13: 9780133486872

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For courses in Introductory Econometrics Engaging applications bring the theory and practice of modern econometrics to life. Ensure students grasp the relevance of econometrics with Introduction to Econometrics-the text that connects modern theory and practice with motivating, engaging applications. The Third Edition Update maintains a focus on currency, while building on the philosophy that applications should drive the theory, not the other way around. This program provides a better teaching and learning experience-for you and your students. Here's how: Personalized learning with MyEconLab-recommendations to help students better prepare for class, quizzes, and exams-and ultimately achieve improved comprehension in the course. Keeping it current with new and updated discussions on topics of particular interest to today's students. Presenting consistency through theory that matches application. Offering a full array of pedagogical features. Note: You are purchasing a standalone product; MyEconLab does not come packaged with this content. If you would like to purchase both the physical text and MyEconLab search for ISBN-10: 0133595420 ISBN-13: 9780133595420. That package includes ISBN-10: 0133486877 /ISBN-13: 9780133486872 and ISBN-10: 0133487679/ ISBN-13: 9780133487671. MyEconLab is not a self-paced technology and should only be purchased when required by an instructor.