scholarly journals Structural Breaks and Volatility Persistence of Stock Returns: Evidence from the US and UK Equity Markets

2018 ◽  
Vol 5 (6) ◽  
pp. 76
Author(s):  
Chikashi Tsuji
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Structural Breaks ◽  
Garch Models ◽  
Stock Market Index ◽  
Market Returns ◽  
Stock Market Returns ◽  
Volatility Persistence ◽  
The Us ◽  
Parameter Values

This paper quantitatively investigates the effects of structural breaks on stock return volatility persistence by using the US and UK stock market index return data. Applying two kinds of representative univariate GARCH models of standard GARCH and EGARCH models, we derive the following interesting findings. (1) First, we find that for both the US and UK stock market returns, the volatility persistence parameter values of standard GARCH models decrease when structural breaks are taken into account. (2) Second, we further reveal that for both the US and UK stock market returns, the volatility persistence parameter values of EGARCH models again decline when structural breaks are taken into consideration.

scholarly journals U.S. STOCK MARKET P/E RATIOS, STRUCTURAL BREAKS, AND LONG-TERM STOCK RETURNS

2018 ◽  
Vol 19 (1) ◽  
pp. 110-123 ◽  
Author(s):  
Chung BAEK ◽  
Ingyu LEE
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Structural Breaks ◽  
Structural Changes ◽  
Market Returns ◽  
Stock Market Returns ◽  
Long Run ◽  
Cumulative Sum Control ◽  
The Relationship

Our study investigates structural changes in the market P/E ratio and shows how structural changes affect long-term stock market returns. Using the cumulative sum control chart and the Bai-Perron algorithm, we identify multiple structural breakpoints in the market P/E ratio and find that those structural changes are significantly perceived over the long run. Unlike previous studies that do not consider structural changes, our study is the first one that shows how structural changes asymmetrically influence long-term stock returns depending on the high or low P/E period. This implies that structural changes in the market P/E ratio play an important role in explaining long-term stock returns. We propose that structural changes should be taken into account in some manner to establish the relationship between P/E ratios and long-term stock returns.

A quantile regression approach to evaluate the relative impact of global and local factors on the MENA stock markets

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Slah Bahloul ◽  
Nawel Ben Amor
Keyword(s):  
Stock Market ◽  
Quantile Regression ◽  
Stock Returns ◽  
Stock Markets ◽  
Financial Integration ◽  
Ordinary Least Squares ◽  
Market Returns ◽  
Stock Market Returns ◽  
Content Type ◽  
The Impact

PurposeThis paper investigates the relative importance of local macroeconomic and global factors in the explanation of twelve MENA (Middle East and North Africa) stock market returns across the different quantiles in order to determine their degree of international financial integration.Design/methodology/approachThe authors use both ordinary least squares and quantile regressions from January 2007 to January 2018. Quantile regression permits to know how the effects of explanatory variables vary across the different states of the market.FindingsThe results of this paper indicate that the impact of local macroeconomic and global factors differs across the quantiles and markets. Generally, there are wide ranges in degree of international integration and most of MENA stock markets appear to be weakly integrated. This reveals that the portfolio diversification within the stock markets in this region is still beneficial.Originality/valueThis paper is original for two reasons. First, it emphasizes, over a fairly long period, the impact of a large number of macroeconomic and global variables on the MENA stock market returns. Second, it examines if the relative effects of these factors on MENA stock returns vary or not across the market states and MENA countries.

Weather, investor psychology and stock returns: evidence from Fiji's stock market

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Janesh Sami
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Sunshine Duration ◽  
Market Returns ◽  
Modeling Framework ◽  
Stock Market Returns ◽  
Content Type ◽  
Asset Pricing Models ◽  
Weather Changes ◽  
Practical Implications

PurposeThis paper investigates whether weather affects stock market returns in Fiji's stock market.Design/methodology/approachThe author employed an exponential general autoregressive conditional heteroskedastic (EGARCH) modeling framework to examine the effect of weather changes on stock market returns over the sample period 9/02/2000–31/12/2020.FindingsThe results show that weather (temperature, rain, humidity and sunshine duration) have robust but heterogenous effects on stock market returns in Fiji.Research limitations/implicationsIt is useful for scholars to modify asset pricing models to include weather-related variables (temperature, rain, humidity and sunshine duration) to better understand Fiji's stock market dynamics (even though they are often viewed as economically neutral variables).Practical implicationsInvestors and traders should consider their mood while making stock market decisions to lessen mood-induced errors.Originality/valueThis is the first attempt to examine the effect of weather (temperature, rain, humidity and sunshine duration) on stock market returns in Fiji's stock market.

scholarly journals Effect Of Macroeconomic Variables On Stock Market Returns For Four Emerging Economies: Brazil, Russia, India, And China

2011 ◽  
Vol 7 (3) ◽  
Author(s):  
Robert D. Gay, Jr.
Keyword(s):  
Stock Market ◽  
Emerging Economies ◽  
Stock Market Index ◽  
Market Returns ◽  
Stock Market Returns ◽  
Macroeconomic Variables ◽  
Share Prices ◽  
Market Index ◽  
The Relationship ◽  
India And China

The relationship between share prices and macroeconomic variables is well documented for the United States and other major economies. However, what is the relationship between share prices and economic activity in emerging economies? The goal of this study is to investigate the time-series relationship between stock market index prices and the macroeconomic variables of exchange rate and oil price for Brazil, Russia, India, and China (BRIC) using the Box-Jenkins ARIMA model. Although no significant relationship was found between respective exchange rate and oil price on the stock market index prices of either BRIC country, this may be due to the influence other domestic and international macroeconomic factors on stock market returns, warranting further research. Also, there was no significant relationship found between present and past stock market returns, suggesting the markets of Brazil, Russia, India, and China exhibit the weak-form of market efficiency.

scholarly journals Opportunity or Crisis? Volatility and Leverage Effect in the World’s Largest Economies

2019 ◽  
Vol 9 (4) ◽  
pp. 1
Author(s):  
Amalendu Bhunia ◽  
Devrim Yaman
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Market Volatility ◽  
Future Market ◽  
Market Returns ◽  
Stock Market Returns ◽  
The Us ◽  
The World ◽  
The Impact ◽  
The Relationship

This paper examines the relationship between asset volatility and leverage for the three largest economies (based on purchasing power parity) in the world; US, China, and India. Collectively, these economies represent Int$56,269 billion of economic power, making it important to understand the relationship among these economies that provide valuable investment opportunities for investors. We focus on a volatile period in economic history starting in 1997 when the Asian financial crisis began. Using autoregressive models, we find that Chinese stock markets have the highest volatility among the three stock markets while the US stock market has the highest average returns. The Chinese market is less efficient than the US and Indian stock markets since the impact of new information takes longer to be reflected in stock prices. Our results show that the unconditional correlation among these stock markets is significant and positive although the correlation values are low in magnitude. We also find that past market volatility is a good indicator of future market volatility in our sample. The results show that positive stock market returns result in lower volatility compared to negative stock market returns. These results demonstrate that the largest economies of the world are highly integrated and investors should consider volatility and leverage besides returns when investing in these countries.

Effects of Daylight-Saving Time Changes on Stock Market Returns and Stock Market Volatility: Rebuttal

2013 ◽  
Vol 112 (1) ◽  
pp. 89-99 ◽  
Author(s):  
Mark J. Kamstra ◽  
Lisa A. Kramer ◽  
Maurice D. Levi
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Market Returns ◽  
Stock Market Returns ◽  
Daylight Saving Time ◽  
Daylight Saving ◽  
Time Changes ◽  
Saving Effect

In a 2011 reply to our 2010 comment in this journal, Berument and Dogen maintained their challenge to the existence of the negative daylight-saving effect in stock returns reported by Kamstra, Kramer, and Levi in 2000. Unfortunately, in their reply, Berument and Dogen ignored all of the points raised in the comment, failing even to cite the Kamstra, et al. comment. Berument and Dogen continued to use inappropriate estimation techniques, over-parameterized models, and low-power tests and perhaps most surprisingly even failed to replicate results they themselves reported in their previous paper, written by Berument, Dogen, and Onar in 2010. The findings reported by Berument and Dogen, as well as by Berument, Dogen, and Onar, are neither well-supported nor well-reasoned. We maintain our original objections to their analysis, highlight new serious empirical and theoretical problems, and emphasize that there remains statistically significant evidence of an economically large negative daylight-saving effect in U.S. stock returns. The issues raised in this rebuttal extend beyond the daylight-saving effect itself, touching on methodological points that arise more generally when deciding how to model financial returns data.

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