Measuring Leverage Effect of Covid-19 on Stock Price Volatility of Energy Companies Using High Frequency Data

2021 ◽  
Author(s):  
Bharat Kumar Meher ◽  
Iqbal Thonse Hawaldar ◽  
Mathew Thomas Gil ◽  
Deebom, Zorle Dum
Keyword(s):  
High Frequency ◽  
Stock Price ◽  
Price Volatility ◽  
High Frequency Data ◽  
Leverage Effect ◽  
Frequency Data ◽  
Stock Price Volatility ◽  
Energy Companies

The dependence structure in volatility between Shanghai and Shenzhen stock market in China

2016 ◽  
Vol 6 (3) ◽  
pp. 264-283 ◽  
Author(s):  
Mingyuan Guo ◽  
Xu Wang
Keyword(s):  
Stock Market ◽  
High Frequency ◽  
Estimation Method ◽  
Garch Model ◽  
Price Volatility ◽  
Market Volatility ◽  
High Frequency Data ◽  
Dependence Structure ◽  
Frequency Data ◽  
Content Type

Purpose – The purpose of this paper is to analyse the dependence structure in volatility between Shanghai and Shenzhen stock market in China based on high-frequency data. Design/methodology/approach – Using a multiplicative error model (hereinafter MEM) to describe the margins in volatility of China’s Shanghai and Shenzhen stock market, this study adopts static and time-varying copulas, respectively, estimated by maximum likelihood estimation method to describe the dependence structure in volatility between Shanghai and Shenzhen stock market in China. Findings – This paper has identified the asymmetrical dependence structure in financial market volatility more precisely. Gumbel copula could best fit the empirical distribution as it can capture the relatively high dependence degree in the upper tail part corresponding to the period of volatile price fluctuation in both static and dynamic view. Originality/value – Previous scholars mostly use GARCH model to describe the margins for price volatility. As MEM can efficiently characterize the volatility estimators, this paper uses MEM to model the margins for the market volatility directly based on high-frequency data, and proposes a proper distribution for the innovation in the marginal models. Then we could use copula-MEM other than copula-GARCH model to study on the dependence structure in volatility between Shanghai and Shenzhen stock market in China from a microstructural perspective.

scholarly journals Realized Measures to Explain Volatility Changes over Time

2020 ◽  
Vol 13 (6) ◽  
pp. 125
Author(s):  
Christos Floros ◽  
Konstantinos Gkillas ◽  
Christoforos Konstantatos ◽  
Athanasios Tsagkanos
Keyword(s):  
High Frequency ◽  
Realized Volatility ◽  
High Frequency Data ◽  
Leverage Effect ◽  
Frequency Data ◽  
Specific Group ◽  
Volatility Feedback ◽  
Changes Over Time ◽  
The Relationship ◽  
Over Time

We studied (i) the volatility feedback effect, defined as the relationship between contemporaneous returns and the market-based volatility, and (ii) the leverage effect, defined as the relationship between lagged returns and the current market-based volatility. For our analysis, we used daily measures of volatility estimated from high frequency data to explain volatility changes over time for both the S&P500 and FTSE100 indices. The period of analysis spanned from January 2000 to June 2017 incorporating various market phases, such as booms and crashes. Based on the estimated regressions, we found evidence that the returns of S&P500 and FTSE100 indices were well explained by a specific group of realized measure estimators, and the returns negatively affected realized volatility. These results are highly recommended to financial analysts dealing with high frequency data and volatility modelling.

Stock Price Jumps and News Sentiment: A Case of Investor Overreaction

2018 ◽  
Vol 11 (2) ◽  
pp. 20-37
Author(s):  
Vinay Kumar Apparaju ◽  
Ashwani Kumar ◽  
Ritu Yadav
Keyword(s):  
High Frequency ◽  
Stock Price ◽  
Strong Form ◽  
High Frequency Data ◽  
Options Market ◽  
Frequency Data ◽  
Innovative Method ◽  
Investor Behaviour ◽  
News Sentiment ◽  
The Impact

The research paper develops an understanding on how news based sentiment capture investor behaviour reflected in price jumps in stock markets. It compares the impact on two models of stock price jumps; the non-parametric model proposed by BNS and the wavelet based method. The study is also a perspective on the semi strong form of market efficiencyUsing the high frequency data from the stock and options market along with the actual high frequency news data from Bloomberg, the two alternative methodologies of jumps have been tested. In addition, options trades have been simulated to see whether profits can be earned from the news sentiment captured by jumps.Methodologically, jumps based on wavelets were found to be better related  with the news sentiment compared to the BNS method. Also,   the news sentiment based jumps were found to present opportunities in the simulated trades that could be exploited for earning profits suggesting that investors overreact.The paper uses an innovative method for computation of the news based sentiment. To the best of our knowledge, the paper is the first to evaluate jumps and news sentiment using the actual news data. A perspective on the semi strong form of market efficiency is presented, that too by departing from the event study based models. 

Sign in / Sign up

Export Citation Format

Share Document