Volatility Clustering in IT Index

2021 ◽  
pp. 83-97
Author(s):  
Anurag Agnihotri
Keyword(s):  
Volatility Clustering

scholarly journals Financial Contagion: A Tale of Three Bubbles

2021 ◽  
Vol 14 (5) ◽  
pp. 229
Author(s):  
Nathan Burks ◽  
Adetokunbo Fadahunsi ◽  
Ann Marie Hibbert
Keyword(s):  
Spillover Effects ◽  
Financial Contagion ◽  
Housing Crisis ◽  
Market Anomalies ◽  
Volatility Clustering ◽  
Asset Bubbles ◽  
Potential Benefits ◽  
Chinese Markets ◽  
Composite Indexes ◽  
Robust Evidence

The primary purpose of the study is to identify and measure the properties of asset bubbles, volatility clustering, and financial contagion during three recent financial market anomalies that originated in the U.S. and Chinese markets. In particular, we focus on the 2000 DotCom Bubble, the 2008 Housing Crisis, and the 2015 Chinese Bubble. We employ three main empirical methods; the LPPL model to identify asset bubbles, the DCC-GARCH model to measure volatility clustering, and the Diebold-Yilmaz volatility spillover index to measure the level of financial contagion. We provide robust evidence that during the DotCom bubble there was very limited spillover between the S&P 500, the Shanghai, and the Shenzhen Composite Indexes. However, there was significantly more spillover effects in the two more recent crises, i.e., the Housing crisis and the 2015 Chinese Bubble. Together, these results highlight the fact that as financial markets have become more globalized, there are greater levels of volatility transmission and correspondingly fewer potential benefits from international diversification.

scholarly journals Volatility clustering in land markets

2014 ◽  
Vol 32 (5) ◽  
pp. 378-385 ◽  
Author(s):  
Helen Xiaohui Bao ◽  
Helen Hui Huang ◽  
Yu-Lieh Huang ◽  
Pin-te Lin
Keyword(s):  
Economic Theory ◽  
Portfolio Management ◽  
Government Policy ◽  
Design Methodology ◽  
Land Markets ◽  
Time Varying ◽  
Content Type ◽  
Volatility Clustering ◽  
Systematic Pattern ◽  
Empirical Approaches

Purpose – The purpose of this paper is to investigate the volatility clustering in the return of land markets through both theoretical and empirical approaches. Design/methodology/approach – Using extensive monthly panel data at the provincial level from 1986 to 2013, the authors identify the existence of time-correlated and time-varying returns in Canadian land markets. Findings – Consistent with the proposed theory, volatility clustering in land markets tends to be observed in more populated areas. Originality/value – The result has significant implications for portfolio management, economic theory and government policy by revealing the systematic pattern of volatility clustering in land markets.

scholarly journals A New Class of Heavy-Tailed Distribution and the Stock Market Returns in Germany

2017 ◽  
Vol 4 (2) ◽  
pp. 13 ◽  
Author(s):  
John Oden ◽  
Kevin Hurt ◽  
Susan Gentry
Keyword(s):  
Stock Market ◽  
Global Economy ◽  
Heavy Tails ◽  
Financial Sector ◽  
Superior Performance ◽  
Market Returns ◽  
Stock Market Returns ◽  
Volatility Clustering ◽  
Empirical Performance ◽  
Heavy Tailed

As the fourth largest economy over the world, Germany’s financial sector plays a key role in the global economy. As one of the most important components of the financial sector, the equity market played a more and more important role. Thus, risk management of its stock market is crucial for welfare of its market participants. To account for the two stylized facts, volatility clustering and conditional heavy tails, we take advantage of the framework in Guo (2016) and consider empirical performance of the GARCH model with normal reciprocal inverse Gaussian distribution in fitting the German stock return series. Our results indicate the NRIG distribution has superior performance in fitting the stock market returns.

Modeling S&P Bombay Stock Exchange BANKEX Index Volatility Patterns Using GARCH Model

2019 ◽  
pp. 259-268
Keyword(s):  
Time Series ◽  
Banking Sector ◽  
Stock Exchange ◽  
Time Lag ◽  
Garch Model ◽  
Recent Past ◽  
Volatility Clustering ◽  
Asymmetric Volatility ◽  
Empirical Results ◽  
Asymmetric Garch

The main objective of this chapter is to estimate volatility patterns in the case of S&P Bombay Stock Exchange (BSE) BANKEX index in India. In recent past, the Indian banking sector was one of the fastest-growing industries and all major banks have been included in S&P BANKEX index as index benchmark constituent companies. The financial econometric framework is based on asymmetric GARCH (1, 1) model which is performed in order to capture asymmetric volatility clustering and leptokurtosis. Data time lag is considered from the first transaction day of January 2002 to last transaction day of June 2014. The empirical results revealed the existence of volatility shocks in the selected time series and also volatility clustering. The volatility impact has generated highly positive clockwise and impacted actual stocks. Moreover, the empirical findings reveal that the BANKEX index grown over 17 times in 12 years and volatility returns have been found present in listed stocks.

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