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1 – 10 of over 14000Elena Andreou and Eric Ghysels
Despite the difference in information sets, we are able to compare the asymptotic distribution of volatility estimators involving data sampled at different frequencies. To do so…
Abstract
Despite the difference in information sets, we are able to compare the asymptotic distribution of volatility estimators involving data sampled at different frequencies. To do so, we propose extensions of the continuous record asymptotic analysis for rolling sample variance estimators developed by Foster and Nelson (1996, Econometrica, 64, 139–174). We focus on traditional historical volatility filters involving monthly, daily and intradaily observations. Theoretical results are complemented with Monte Carlo simulations in order to assess the validity of the asymptotics for sample sizes and filters encountered in empirical studies.
This paper examines the relationship between volatility, sentiment and returns in terms of levels and changes for both lower and higher data frequencies using quantile regression…
Abstract
Purpose
This paper examines the relationship between volatility, sentiment and returns in terms of levels and changes for both lower and higher data frequencies using quantile regression (QR) method.
Design/methodology/approach
In the first step, the study applies the Granger causality test to understand the causal relationship between realized volatility, returns and sentiment as levels and changes. In the second step, the study employs a QR method to investigate whether investor sentiment and returns can predict realized volatility. This regression method gives robust results irrespective of distributional assumptions and to outliers in the dependent variable.
Findings
Empirical results show that the VIX volatility index is a better fear gauge of market-wide investors' sentiments and has a predictive power for future realized volatility in terms of levels and changes for both higher and lower data frequencies. This study provides evidence that the relationship between realized volatility, investor sentiment and returns, respectively, is not symmetric for all quantiles of QR, as opposed to OLS regression. Furthermore, this work supports the behavioral theory beyond leverage hypothesis in explaining the asymmetric relation between returns and volatility at higher and lower data frequencies.
Originality/value
This paper adds to the limited understanding of investor sentiment’s impact on volatility by proposing a QR model which provides a more complete picture of the relationship at all parts of the volatility distribution for both higher and lower data frequencies and in terms of levels and changes. To the author knowledge, this is the first paper to study the volatility responses to positive and negative sentiment changes for developed market and to use both lower and higher data frequencies as well as data in terms of levels and changes.
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The purpose of this paper is to investigate the interrelationships amongst the sector‐specific indices of the Qatar Exchange (QE) (i.e. Banking and Financial Institutions (BFI)…
Abstract
Purpose
The purpose of this paper is to investigate the interrelationships amongst the sector‐specific indices of the Qatar Exchange (QE) (i.e. Banking and Financial Institutions (BFI), Industrial (IND), Insurance (INS), and Services (SER)). More specifically, three key issues are explored in this study. First, the long‐run relationships amongst the sectors. Second, the short‐run causal relationships amongst them; and third, the relative degree of endogeneity/exogeneity of each sector.
Design/methodology/approach
To address the issues of interest, the author employs the econometric analyses of Johansen's multivariate cointegration, Granger's causality, and generalized forecast error variance decomposition. This battery of techniques gives the opportunity to examine the nature of both long‐ and short‐run intersectoral relationships in the QE. To augment the robustness of the empirical analysis, daily as well as weekly closing stock price indices for the four sectors of the Qatar Exchange are used, spanning the period from January 2, 2008 up to April 7, 2011.
Findings
Based on daily and weekly data, the results of Johansen's multivariate cointegration analysis suggest that the four sector indices of the QE share a long‐term equilibrium relationship. The Granger's causality analysis based on daily and weekly datasets provides clear evidence that the BFI sector seems to be a significant causal factor in regard to the price predictability of the remaining sectors in the short run, and that the SER sector surprisingly seems to have the least influential role. Finally, the results of the generalized forecast error variance decomposition analysis using daily data show that the IND and BFI appear to be the most exogenous sectors, whereas the SER and INS are the most endogenous ones. The results based on weekly data confirm the relative exogeneity of the BFI sector and the relative endogeneity of the SER sector.
Practical implications
The findings of this study hold practical implications for individual and institutional investors alike. The potential gains derived from cross‐sector diversification could be rather limited, given the significant degree of interrelationships found amongst the sector indices of the QE. Moreover, the composition of domestic portfolios based on sector‐level investments should be revisited, particularly after major events. The findings also bring some important insights for policymakers. Given the influential role played by the BFI sector in the Qatari economy, policymakers should design appropriate strategies that curb the spread of unanticipated shocks originating from this sector to its counterparts. Besides, due to the considerable degree of endogeneity of the SER sector, it is essential for policymakers to set up precautionary regulations, with the aim of minimizing its vulnerability to common shocks in turbulent times.
Originality/value
Building upon the extant research and focusing on a relatively unexplored market, the paper represents a pioneer attempt to provide empirical evidence on the interdependence structure amongst the sector‐specific indices of the Qatar Exchange.
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This chapter identifies three crisis warning indicators driven from trading in emerging markets’ carry trades, and empirically examines whether these indicators could predict two…
Abstract
This chapter identifies three crisis warning indicators driven from trading in emerging markets’ carry trades, and empirically examines whether these indicators could predict two major financial crises that hit the global financial markets in the last decades — The 1997–1998 Asian crisis and the 2007–2008 global crisis. The probit regression is used to examine the power of the three indicators in forecasting financial crises, using data from eight Asian emerging countries which serve as proxies for emerging markets, independent of the origination of the crisis. I use both fixed effect and random effect estimation to measure crisis impacts. The empirical results show that financial crises could have been predicted. Probit estimation show that carry trade returns can predict a financial crisis, and the estimation results are robust to both panel level and country-level analysis. These three indicators are by no means an exhaustive list of all possible predictors of financial crisis. The literature suggests other fundamental indicators of financial crises such as the current account deficit and foreign debt. However, this chapter cannot fully consider these indicators for lack of data at this point in time. Although financial crisis may be better predicted by the well-known fundamental indicators, the contribution of this chapter is simply that carry trade-related indicators can help in predicting crises.
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The aim of this study is to examine real estate investment trust exchange-traded funds (REIT ETFs) and test for the existence of the “asymmetric beta puzzle” phenomenon in these…
Abstract
Purpose
The aim of this study is to examine real estate investment trust exchange-traded funds (REIT ETFs) and test for the existence of the “asymmetric beta puzzle” phenomenon in these financial instruments that are relatively new and are gaining popularity. The “asymmetric beta puzzle” phenomenon is used to identify the hedging and diversification benefits of a financial instrument. “Asymmetric beta puzzle” exists when betas in declining markets are higher than betas in advancing markets.
Design/methodology/approach
To study 14 REIT ETFs by using monthly and daily Center for Research in Security Prices (CRSP) data. Capital asset pricing model (CAPM) and Fama–French three-factor model were used to estimate betas in REIT ETFs and those in advancing and declining markets. Both the S&P 500 and the CRSP value-weighted indices were used in the beta estimation. Two hypotheses with regard to betas in both advancing and declining markets were defined and tested to test for the existence of the “asymmetric beta puzzle” phenomenon.
Findings
This study confirms the presence of the “asymmetric beta puzzle” in the data of monthly REIT ETFs as documented by Goldstein and Nelling (1999) and Chatrath et al. (2000) for REITs; however, this phenomenon was not found when using daily data, but quite the opposite – REIT ETF betas are higher in advancing markets than they are in declining markets – was found.
Originality/value
Goldstein and Nelling (1999) and Chatrath et al. (2000) identify the phenomenon of “the asymmetric REIT-beta puzzle” in monthly REIT’s returns. This study revisits the phenomenon identified in the aforementioned authors’ studies by using daily data and a relatively new real estate financial instrument – REIT ETFs. Therefore, this paper fills a void in the literature and would benefit both institutional and retail investors in their portfolio designs.
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Haiya Cai, Yongqing Nan, Yongliang Zhao and Haoran Xiao
The purpose of this study is to regard winter heating as a quasi-natural experiment to identify the possible causal effects of winter heating on population mobility. However…
Abstract
Purpose
The purpose of this study is to regard winter heating as a quasi-natural experiment to identify the possible causal effects of winter heating on population mobility. However, there are scant research studies examining the effect of atmospheric quality on population mobility. There also exists some relevant research studies on the relationship between population mobility and environmental degradation (Lu et al., 2018; Reis et al., 2018; Shen et al., 2018), and these studies exist still some deficiencies.
Design/methodology/approach
The notorious atmospheric quality problems caused by coal-fired heating in winter of northern China have an aroused widespread concern. However, the quantitative study on the effects on population mobility of winter heating is still rare. In this study, the authors regard the winter heating as a quasi-natural experiment, based on the of daily panel data of 58 cities of Tencent location Big Data in China from August 13 to December 30 in 2016 and August 16 to December 30 in 2017, and examine the impacts of winter heating on population mobility by utilizing a regression discontinuity method.
Findings
The findings are as follows, in general, winter heating significantly aggravates regional population mobility, but the impacts on population mobility among different cities are heterogeneous. Specifically, the effects of winter heating on population mobility is greater for cities with relatively good air quality, and the effects is also more obvious for big and medium-sized cities than that in small cities. In addition, different robustness tests, including continuity test, different bandwidth tests and alternative empirical model, are adopted to ensure the reliability of the conclusion. Finally, the authors put forward corresponding policy suggestions from the three dimensions of government, enterprises and residents.
Originality/value
First, regarding winter heating as a quasi-natural experiment, a regression discontinuity design method is introduced to investigate the relationship between winter heating and population mobility, which is helpful to avoid the estimation error caused by endogeneity. Second, the authors use the passenger travel “big data” based on the website of Tencent Location Big Data, which can effectively capture the daily characteristics of China's population mobility. Third, this study discusses the population mobility from the perspective of winter heating and researches population mobility before and after winter heating, which is helpful in enriching the research on population mobility.
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Jun Chen, Yi Chen and Bart Frijns
The aim of this study is to examine the tracking performance and tracking error (TE) of New Zealand exchange traded fsunds (ETFs).
Abstract
Purpose
The aim of this study is to examine the tracking performance and tracking error (TE) of New Zealand exchange traded fsunds (ETFs).
Design/methodology/approach
The authors use regression methods and cointegration analysis to examine tracking performance. Multivariate regressions are used to examine the determinants of TE.
Findings
At the daily frequency, the authors observe that the ETFs have substantially different exposures to their underlying indexes from what they should be, which is confirmed by cointegration analysis. At the monthly frequency, tracking performance improves but still shows significant differences between the ETF and its underlying index. When the authors examine the TEs of the ETFs, the authors observe that these are substantial and that there is considerable variation in TE. Regression analysis shows that both characteristics of the ETF and the constituents of the index the ETF tracks, as well as the volatility of the underlying benchmark are determinants of the TE of the ETFs.
Originality/value
This is the first study to examine New Zealand-based ETFs. The findings contribute to understanding the performance of these ETFs and are of relevance to academics, investors and the ETF provider.
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The purpose of this paper is to study the efficiency of different oil and gas markets. Most previous studies examined the issue using low frequency date sampled at monthly…
Abstract
Purpose
The purpose of this paper is to study the efficiency of different oil and gas markets. Most previous studies examined the issue using low frequency date sampled at monthly, weekly, or daily frequencies. In this study, 30-minute intraday data are used to explore efficiency in energy markets.
Design/methodology/approach
Sophisticated statistical analysis techniques such as Granger-causality regressions, augmented Dickey-Fuller tests, cointegration tests, vector autoregressions are used to explore the transmission of information between oil and gas energy markets.
Findings
This study provides evidence for efficiency in energy markets. The new information that arrives either to futures markets or spot markets is digested correctly, completely, and in a fast manner, and is propagated to the other market. The evidence indicates high efficiency.
Originality/value
This study is one of the first papers that uses 30-minute interval intraday data to investigate efficiency in oil and gas commodity markets.
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Zhe Liu, Chong Huang and Benshuo Yang
This paper investigates the impact of investor attention on the COVID-19 concept stocks in China's stock market from the perspectives of the macroeconomy, the stock market and the…
Abstract
Purpose
This paper investigates the impact of investor attention on the COVID-19 concept stocks in China's stock market from the perspectives of the macroeconomy, the stock market and the COVID-19 pandemic.
Design/methodology/approach
On the basis of controlling the time effects and individual fixed effects, this paper studies the impact of investor attention on the COVID-19 concept stocks in China's stock market through a set of fixed effect panel data models. Among them, investor attention focuses on macroeconomy, stock market and the COVID-19 pandemic, respectively, while stock indicators cover return, volatility and turnover. In addition, this paper also examines the heterogeneity influence of investor attention on the COVID-19 concept stocks from the perspective of time and stock classification.
Findings
Findings indicate that the attention to macroeconomy does not have a statistically significant effect on the return, unlike the attention to stock market and COVID-19 incident. Three types of investor attention have significant positive effects on the volatility and turnover rate. During the outbreak of the domestic epidemic, the impact of investor attention was significantly higher than that during the outbreak of the epidemic overseas. A finer-grained analysis shows that the attention to stock market has significantly increased the return of preventive type and treatment type stocks, while diagnostic-related stocks have been most affected by the attention to COVID-19 incident.
Research limitations/implications
The major limitation of this work is the construction of investor attention. Although Baidu index is widely used, investor attention can be assessed more accurately based on more unstructured data. In addition, the effect of the COVID-19 can also be investigated in a longer time domain. Further research can be combined with the dynamics of the COVID-19 pandemic to more comprehensively evaluate its impact on the stock market.
Originality/value
The research proves that investor attention plays an important role in stock pricing and provides empirical evidence on the behavioral foundations of the conceptual sector of the stock market under uncertainty. It also has practical implications for regulators and investors interested in conducting accurate asset allocation and risk assessment.
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JiHye Park, JaeHong Park and Ho-Jung Yoon
When purchasing digital content (DC), consumers are typically influenced by various information sources on the website. Prior research has mostly focused on the individual effect…
Abstract
Purpose
When purchasing digital content (DC), consumers are typically influenced by various information sources on the website. Prior research has mostly focused on the individual effect of the information sources on the DC choice. To fill the gap in the previous studies, this research includes three main effects: information cascades, recommendations and word of mouth. In particular, the purpose of this paper is to focus on the interaction effect of information cascades and recommendations on the number of software downloads.
Design/methodology/approach
The authors use the panel generalized least squares estimation to test the hypotheses by using a panel data set of 2,000 pieces of software at download.cnet.com over a month-long period. Product ranking and recommendation status are used as key independent variables to capture the effects of information cascades and recommendations, respectively.
Findings
One of this study’s findings is that information cascades positively interact with recommendations to influence the number of software downloads. The authors also show that the impact of information cascades on the number of software downloads is greater than one of the recommendations from a distributor does.
Originality/value
Information cascades and recommendations have been considered as the primary effects for online product choices. However, these two effects typically are not considered together in one research. As previous studies have mainly focused on each effect, respectively, the authors believe that this study may fill the gap by examining how these effects are interacted to one other to influence customers’ choices. The authors also show that the impact of information cascades on the number of software downloads is greater than one of the recommendations from a system does.
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