Calibration of Temperature Futures by Changing the Mean Reversion

2015 ◽  
Author(s):  
Fred Espen Benth ◽  
Salvador Ortiz-Latorre
Keyword(s):  
Mean Reversion ◽  
The Mean

Stock Market Bubbles? A Reply

1995 ◽  
Vol 55 (3) ◽  
pp. 655-665 ◽  
Author(s):  
Eugene N. White
Keyword(s):  
Stock Market ◽  
Mutual Fund ◽  
Stock Prices ◽  
Mean Reversion ◽  
Efficient Markets ◽  
Excess Volatility ◽  
Stock Market Bubbles ◽  
The Mean ◽  
Predictable Returns

Research in finance is guided by powerful intuitions from models of efficient markets. However, researchers have uncovered a number of puzzles that are not explained by these models. Such anomalies include the excess volatility of stock prices, the closed-end mutual fund paradox, and the mean reversion in stock prices that produces predictable returns for long holding periods.1 Whereas financial economists all recognize the existence of these puzzles, they disagree about how they can be explained. Robert J. Shiller argues, for example, that efficient-markets models cannot hope to explain these anomalies and looks to alternatives that incorporate fads.2 In contrast, John H. Cochrane believes that the puzzles can be explained by improved models of fundamentals.3

scholarly journals The short-term mean reversion of stock price and the change in trading volume

2021 ◽  
Vol 29 (3) ◽  
pp. 190-214
Author(s):  
Woosung Jung ◽  
Mhin Kang
Keyword(s):  
Term Structure ◽  
Stock Price ◽  
Trading Volume ◽  
Price Index ◽  
Mean Reversion ◽  
Ratio Test ◽  
Short Term ◽  
Contemporaneous Correlation ◽  
The Mean ◽  
Response To Change

This study aims to analyze the effect of change in trading volume on the short-term mean reversion of the stock price in the Korean stock market. Through the variance ratio test, this paper finds that the market shows the mean reversion pattern after 2000, but not before. This study also confirms that the mean reversion property is significantly reduced if the effect of change in trading volume is excluded from the return of a stock with a significant contemporaneous correlation between return and change in trading volume in the post-2000 market. The results appear in both the Korea Composite Stock Price Index and Korea Securities Dealers Automated Quotation. This phenomenon stems from the significance of the return response to change in trading volume per se and not the sign of the response. Additionally, the findings imply that the trading volume has a term structure because of the mean reversion of the trading volume and the return also has a partial term structure because of the contemporaneous correlation between return and change in trading volume. This conclusion suggests that considering the short-term impact of change in trading volume enables a more efficient observation of the market and avoidance of asset misallocation.

scholarly journals STOCK RETURNS, VOLATILITY AND MEAN REVERSION IN EMERGING AND DEVELOPED FINANCIAL MARKETS

2018 ◽  
Vol 24 (3) ◽  
pp. 1149-1177 ◽  
Author(s):  
Rizwan Raheem AHMED ◽  
Jolita VVEINHARDT ◽  
Dalia ŠTREIMIKIENĖ ◽  
Saghir Pervaiz GHAURI
Keyword(s):  
Emerging Markets ◽  
Stock Returns ◽  
Stock Markets ◽  
Mean Reversion ◽  
Conditional Variance ◽  
Trade Off ◽  
Risk Return ◽  
Developed Markets ◽  
The Mean ◽  
Developed Stock Markets

The objective of this research is to measure and examine volatilities between important emerging and developed stock markets and to ascertain a relationship between volatilities and stock returns. This research paper also analyses the Mean reversion phenomenon in emerging and developed stock markets. For this purpose, seven emerging markets and five developed markets were considered. Descriptive statistics showed that the emerging markets have higher returns with the higher risk-return trade-off. In contrast, developed markets have low annual returns with a low risk-return trade-off. Correlation analysis indicated the significant positive correlation among the developed markets, but emerging and developed markets have shown relatively insignificant correlation. Results of ARCH and GARCH revealed that the value of likelihood statistics ratio is large, that entails the GARCH (1,1) model is a lucrative depiction of daily return pattern, that effectively and efficiently capturing the orderly reliance of volatility. The findings of the study showed that the estimate ‘β’ coefficients given in conditional variance equation are significantly higher than the ‘α’, this state of affair entails that bigger market surprises tempt comparatively small revision in future volatility. Lastly, the diligence of the conditional variance estimated by α + β is significant and proximate to integrated GARCH (1,1) model, thus, this indicates, the existing evidence is also pertinent in order to forecast the future volatility. The results signified that the sum of GARCH (1,1) coefficients for all the equity returns’ is less than 1 that is an important condition for mean reversion, as the sum gets closer to 1, hence the Mean reversion process gets slower for all the emerging and developed stock markets.

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