Mean reversion in stock prices of seven Asian stock markets: Unit root test and stationary test with Fourier functions

Juan, Wang; Zhang, Dongxiang; Zhang, Jian

doi:10.1016/j.iref.2014.11.020

Cited by 35 publications

(27 citation statements)

References 49 publications

(54 reference statements)

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“…is less than one. The previous literature has also found the same conclusion (Ahmad et al, 2016;Chi et al, 2016;Hart et al, 2015;Mishra, 2017;Tripathy, 2017;Wang et al, 2015). The results of the study also demonstrate that there is a direct relation between stock returns and volatilities of the emerging and developed stock markets.…”

Section: Discussionsupporting

confidence: 84%

“…Thus, econometricians try to calculate the actual potential of undervalued stock by using different techniques. The efficient market hypothesis is also described in terms of a random walk; it is very complicated to explore the undervalued company stock and its future performance (Mishra, 2017;Tripathy, 2017;Wang, Zhang, & Zhang, 2015;Zakamulin, 2016).…”

Section: Background Of the Research Studymentioning

confidence: 99%

“…This proposition does not follow any specific pattern; therefore, the returns cannot be predicted. The random walk is an alternative terminology for the efficient market hypothesis; it is very complicated to explore the undervalued stock, and to predict whether the stock can perform more strongly in the future (Mishra, 2017;Tripathy, 2017;Wang et al, 2015;Zakamulin, 2016). According to Al-Hajieh (2017) and Dupernex (2007), this unpredictability no longer exists for the equity returns.…”

Section: Substantiation From the Literaturementioning

confidence: 99%

“…The flow of research studies regarding the short-term mean reversion has also been increased due to the access of information on a daily basis regarding the investments. Hence, short-term mean reversion was found to be more attractive than the long-term mean reversion process (Chaves & Viswanathan, 2016;Huang, 2017;Neaime, 2015;Wang et al, 2015). If the stock underperforms within a one-year time period and after one year it overperforms then this phenomenon is known as the long-term mean reversion.…”

Section: Substantiation From the Literaturementioning

confidence: 99%

See 3 more Smart Citations

Mean reversion in international markets: evidence from G.A.R.C.H. and half-life volatility models

Ahmed

Vveinhardt

Štreimikienė

et al. 2018

Economic Research-Ekonomska Istraživanja

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The objective of this research is to examine and compare the mean reversion phenomenon in developed and emerging stock markets. An important aim is to measure and compare the speed of mean reversion and half-life of volatility shocks of emerging and developed markets. For this purpose, we have selected five developed and seven emerging markets, and used daily market indices for the period of 1 January 2000 to 30 June 2016. We employed autoregressive conditional heteroskedasticity -Lagrange multiplier (A.R.C.H.-L.M.), generalised autoregressive conditional heteroskedasticity (G.A.R.C.H.) (1, 1), and half-life volatility shock techniques to carry out this research. The results of the study confirmed the mean-reverting process in developed and emerging markets. The South Korean market has the slowest mean reversion, and thus has the highest comparative volatility over a longer period of time. However, the Pakistan stock exchange exhibited the fastest mean reverting process. It is also concluded that the relative volatilities are higher in emerging markets, whereas the comparative volatilities are higher in developed markets. Therefore, it is further concluded that the mean reversion process is much faster in emerging indices except the South Korean and Chinese markets. The study recommends that if investors want higher returns in a shorter period of time then they should invest in emerging markets.

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Section: Discussionsupporting

confidence: 84%

Section: Background Of the Research Studymentioning

confidence: 99%

Section: Substantiation From the Literaturementioning

confidence: 99%

Section: Substantiation From the Literaturementioning

confidence: 99%

See 2 more Smart Citations

Mean reversion in international markets: evidence from G.A.R.C.H. and half-life volatility models

Ahmed

Vveinhardt

Štreimikienė

et al. 2018

Economic Research-Ekonomska Istraživanja

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show abstract

“…Therefore, these investors use various techniques' that help them for identifying these undervalued stocks. However, according to efficient market hypothesis also known as the random walk, it is impossible to identify the securities that can outperform (e.g., Zakamulin 2016;Wang et al 2015;Kühnlenz 2014;etc. ).…”

Section: Introductionmentioning

confidence: 99%

Speed of Mean Reversion: An Empirical Analysis of Kse, Lse and Ise Indices

Palwasha

Ahmad

Ahmed

et al. 2018

Technological and Economic Development of Economy

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Abstract. The purpose of this study is to determine the presence of mean reversion in the stock markets indices of Pakistan, moreover, to measure, and compare the speed of mean reversion of the stock markets indices across Pakistan. In order to carry out the research study, the daily data of three stock indices of Pakistan such as: KSE-100, LSE-25 and ISE-10 are collected from 2003 to 2014. After the application of tests such as ARCH and GARCH, it was found that returns series of KSE-100, LSE-25 and ISE-10 indices exhibit mean reversion, indicating that the returns revert back to their historical value after reaching an extreme value. Further, the mean reversion rate shows that KSE-100 index has the slowest mean reversion, however, the ISE-10 index has the fastest mean reversion among the three indices. Therefore, the results of the study concluded that KSE-100 index, due to the slowest mean reversion rate has higher volatility over a longer period of time. On the contrary, since, ISE-10 index has exhibited the fastest mean reversion with the lowest volatility as compared to others. But due to fast mean reversion rate, it will help investors to gain profits over a shorter period of time. Thus, it can be recommended that the investor willing to bear the risk of time and looking for long-term investment should invest in KSE-100 index. However, investors looking for higher profits in a shorter period can invest in the ISE-10 index but with higher risk-returns trade-off.

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Estimating structural credit risk models when market prices are contaminated with noise

Lee

2015

Appl Stoch Models Bus & Ind

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In this paper, sequential estimation on hidden asset value and model parameter estimation is implemented under the Black-Cox model. To capture short-term autocorrelation in the stock market, we assume that market noise follows a mean reverting process. For estimation, Bayesian methods are applied in this paper: the particle filter algorithm for sequential estimation of asset value and the generalized Gibbs and multivariate adapted Metropolis methods for model parameters estimation. The first simulation study shows that sequential hidden asset value estimation using both option price and equity price is more efficient than estimation using equity price alone. The second simulation study shows that, by applying the generalized Gibbs sampling and multivariate adapted Metropolis methods, model parameters can be estimated successfully. In an empirical analysis, the stock market noise for firms with more liquid stock is estimated as having smaller volatility.The mean reverting process assumption is closer to real stock market behavior, which shows short-term dependence. The stock price depends on its previous values, but the shocks to stock prices are all temporary, and stock prices will eventually return to the trend path. Explanations for and empirical testing of the existence of autocorrelation in the stock market are well documented in the studies of Lo and Mackinlay [10,11], Sias and Starks [12], and Chordia and Swaminathan [13]. Following on from the studies of DeBondt and Thaler [14], Poterba [15], and Fama and French [16], who demonstrated the mean reversion property of stock prices, in recent years, there has been increasing evidence supporting the mean reversion hypothesis [17][18][19][20][21].Second, we estimate parameters and unknown asset processes together under the Black-Cox structural model [22]. Previous studies on estimation [6,7] were based on the Merton model, which gives a relatively simple inverse function of equity and asset values. The iterative scheme [23] and the well-known Kealhofer, McQuown and Vasicek (KMV) method (or transformed-data maximum likelihood (ML) method described in the study of Duan [24,25]) are based on the Merton model. However, the study of Jones et al. [26] showed that the predicted bond price for 1977-1981 obtained from the Merton model was over-estimated by 4.52% on average.The Black-Cox model assumes that default can occur at any time prior to the maturity of the bond. It has a default assumption that is more relaxed and closer to the real world, in that under the usual bond contract in practice, a firm needs to pay annual interest to the bond holder, meaning that default can occur at any time before the bond matures. Third, we model the prices of multiple asset classes (debts, equity, and options on equity) in order to gain a better understanding of a firm's capital structure as a whole and also to enhance the precision of the estimation. In the financial market, corporations tend to issue multiple classes of securities to raise their capital. Therefore, the stock marke...

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Mean reversion in stock prices of seven Asian stock markets: Unit root test and stationary test with Fourier functions

Cited by 35 publications

References 49 publications

Mean reversion in international markets: evidence from G.A.R.C.H. and half-life volatility models

Mean reversion in international markets: evidence from G.A.R.C.H. and half-life volatility models

Speed of Mean Reversion: An Empirical Analysis of Kse, Lse and Ise Indices

Estimating structural credit risk models when market prices are contaminated with noise

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