How to identify the different phases of stock market bubbles statistically?

Horváth, Lajos; Li, Hemei; Liu, Zhenya

doi:10.1016/j.frl.2021.102366

Cited by 7 publications

(2 citation statements)

References 18 publications

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“…8. Note that the majority of previous empirical research on speculative bubbles used monthly data rather than daily/weekly data, for example, in stock markets (Homm and Breitung, 2012, Horváth et al , 2021), commodity markets (Zhao et al , 2015; Brooks, Prokopczuk, and Wu, 2015; Long et al , 2016; Pan, 2018, Khan and Köseoğlu, 2020), as well as in housing (Hu and Oxley, 2018) and currency markets (Hu and Oxley, 2017). …”

Section: Notesmentioning

confidence: 99%

Bubble contagion effect between the main precious metals

Maghyereh

Abdoh

2022

SEF

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Purpose This study examines the extent to which gold and silver bubbles are correlated and which metal’s bubble spills over to the other. In addition, the overlap in bubble-like episodes for the two metals is demonstrated and the influence of crises (global financial crises, European debt crisis and the COVID-19 pandemic) on the development of these episodes is compared. Design/methodology/approach This study proposes a two-step approach. In the first step, price bubbles are identified based on the backward sup augmented Dickey–Fuller of Phillips et al. (2015a, 2015b) and modified by Phillips and Shi (2018). In the second step, the correlation in the contagion effect of the bubbles between the two precious metal prices is measured using a nonparametric regression with a time-varying coefficient approach developed by Greenaway-McGrevy and Phillips (2016). Findings The findings suggest that the safe-haven property of gold and silver during financial market turbulence induces excessive price increases beyond their fundamental values. Furthermore, the results indicate that bubbles are contagious among precious metal markets and flow mainly from gold to silver; these findings are associated with the period after 2005, particularly during the global financial crisis. A contagious bubble effect is not found between gold and silver during the coronavirus disease 2020 pandemic. Practical implications The results suggest that financial market participants should consider portfolio weights in precious markets in light of the bubble correlation between gold and silver, especially during crises. Originality/value To the best of the authors’ knowledge, this is the first study that explores the correlation of bubble-like episodes between gold and silver.

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Section: Notesmentioning

confidence: 99%

Bubble contagion effect between the main precious metals

Maghyereh

Abdoh

2022

SEF

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“…The algorithms provided by Phillips, Wu, and Yu (2011), Phillips, Shi, and Yu (2015a, 2015b) are able to detect bubble behaviour and date‐stamp its origination and collapse. These methods are widely used in the empirical study of asset price bubbles in various markets, including the stock market (see Basse et al ., 2021; Horváth, Li, and Liu, 2021; Li, Wang, and Zhao, 2021), housing market (See Phillips and Yu, 2013; Greenaway‐McGrevy and Phillips, 2016; Shi et al ., 2016), cryptocurrency market (see Cheung, Roca, and Su, 2015; Corbet, Lucey, and Yarovaya, 2018; Bouri, Shahzad, and Roubaud, 2019), oil market (see Fantazzini, 2016; Caspi, Katzke, and Gupta, 2018; Gharib, Mefteh‐Wali, and Jabeur, 2021), precious metals market (see Figuerola‐Ferretti, Gilbert, and McCrorie, 2015; Pan, 2018; and Ma and Xiong, 2021), exchange rate market and others (see Etienne, Irwin, and Garcia, 2014; Kräussl, Lehnert, and Martelin, 2016; Shi, Hurn, and Phillips, 2020). These models are built in a non‐stationary framework.…”

Section: Introductionmentioning

confidence: 99%

Seemingly Unrelated Regression Estimation for VAR Models with Explosive Roots*

Chen

2023

Oxf Bull Econ Stat

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For VAR models with common explosive root, the OLS estimator of the autoregressive coefficient matrix is inconsistent (refer to Nielsen, 2009 andMagdalinos, 2013). Although Phillips & Magdalinos (2013) proposed using the future observations as the instrumental variable for removing the endogeneity from VAR models, type I error occurs when testing for a common explosive root from the distinct explosive roots before the implementation of IV estimation. Such error creates bias and variance in the estimate and further causes incorrect inference in the structural analysis such as forecast error decomposition (FEVD). Hence, we propose using of seemingly unrelated regression (SUR) estimation for VAR models with explosive roots. Our SUR estimator is consistent in the case of both distinct explosive roots and common explosive root. We also consider models with drift in the system for generalization. Simulations show that the SUR estimate performs better than OLS and IV estimate in the case of both a common explosive root and distinct explosive roots case. In structural FEVD analysis, simulations show that SUR yields a different result from OLS and IV. We demonstrate the use of SUR in FEVD for agricultural commodity markets between 3 July 2010, and 29 January 2011. JEL Classification numbers: C12, C13, C58. *We thank the Editor Anindya Banerjee and two anonymous referees for very helpful comments on earlier versions of the paper, which improved the quality of the paper significantly. Chen acknowledges support from National Natural Science Foundation of China (No. 71803138), the Project of Construction and Support for high-level Innovative Teams of Beijing Municipal Institutions (BPHR20220119), and the Project of Cultivation for Young Top-notch Talents of Beijing Municipal Institutions (BPHR202203171). Li acknowledges support from National Natural Science Foundation of China (No.72173052 & No.71803058).

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Forecasting oil commodity spot price in a data-rich environment

2022

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Statistical properties that vary with time represent a challenge for time series forecasting. This paper proposes a change point-adaptive-RNN (CP-ADARNN) framework to predict crude oil prices with high-dimensional monthly variables. We first detect the structural breaks in predictors using the change point technique, and subsequently train a prediction model based on ADARNN. Using 310 economic series as exogenous factors from 1993 to 2021 to predict the monthly return on the WTI crude oil real price, CP-ADARNN outperforms competing benchmarks by 12.5% in terms of the root mean square error and achieves a correlation of 0.706 between predicted and actual returns. Furthermore, the superiority of CP-ADARNN is robust for Brent oil price as well as during the COVID-19 pandemic. The findings of this paper provide new insights for investors and researchers in the oil market.

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How to identify the different phases of stock market bubbles statistically?

Cited by 7 publications

References 18 publications

Bubble contagion effect between the main precious metals

Bubble contagion effect between the main precious metals

Seemingly Unrelated Regression Estimation for VAR Models with Explosive Roots*

Forecasting oil commodity spot price in a data-rich environment

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