Can we use volatility to diagnose financial bubbles? lessons from 40
historical bubbles

Sornette, Didier; Cauwels, Peter; Smilyanov, Georgi

doi:10.3934/qfe.2018.1.486

Cited by 62 publications

(13 citation statements)

References 1 publication

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“…erefore, these results are somewhat consistent with those of Wang et al [44] and Sornette et al [45], who found that China's regional GTFP has an agglomeration effect. e improvement of provincial GTFP will lead to an increase in overall regional production efficiency.…”

Section: Empirical Analysissupporting

confidence: 91%

Impact of Financial Development and Its Spatial Spillover Effect on Green Total Factor Productivity: Evidence from 30 Provinces in China

Zhong

2020

Mathematical Problems in Engineering

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The relationship between financial development and green economic growth has received much attention in recent years. Research on the relationship between financial development and green total factor productivity (GTFP) is of great importance to China and other countries. This study has attempted to reveal the spatial distribution of China’s provincial GTFP and impact of financial development on GTFP by using the method of GML index based on SBM-DDF and the spatial Durbin model (SDM) during the period 1996–2015. Innovation is added to the SDM to reflect the influencing mechanism of financial development on GTFP. The empirical results show the following: (1) The mean of China’s provincial GTFP showed a U-shaped curve in 1996–2015. (2) China’s provincial financial development promotes the growth of GTFP through innovation channel. The reason is that financial development boosts eco-friendly innovation and the introduction of energy saving technology, leading to a decrease in energy consumption and pollutant emissions. (3) Increasing the level of financial development in the surrounding areas will restrain local GTFP. Our results provide new evidence that China’s regional financial development has a spatial spillover effect. (4) China’s provincial GTFP has a significant spatial positive correlation. Finally, several policy implications can be summarized to China’s 30 provinces.

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Section: Empirical Analysissupporting

confidence: 91%

Impact of Financial Development and Its Spatial Spillover Effect on Green Total Factor Productivity: Evidence from 30 Provinces in China

Zhong

2020

Mathematical Problems in Engineering

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“…Empirical work by Sornette et. al., 2018 [29] provides some support for this theoretical conclusion in two-thirds of major market tops. Similarly, this can similarly be tested in mathematical and computer generated models.…”

Section: The Stochastic Dynamics Equation Through the Supply/demand Ementioning

confidence: 80%

“…This is consistent with an empirical study of 40 major market tops and subsequent declines, in which Sornette et. al., 2018 [29] showed that for two-thirds of the cases, volatility is lowest as the market peaks and higher as the market rises or falls rapidly. Similarly, Bitcoin exhibits a valley in volatility near its peak trading price in December 2017 (Caginalp and Caginalp, 2018 [7]).…”

Section: The Stochastic Dynamics Equation Through the Supply/demand Ementioning

confidence: 99%

Stochastic Price Dynamics Equations Via Supply and Demand; Implications for Volatility and Risk

Caginalp

2019

SSRN Journal

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“…Furthermore, while most of the prior studies focus on bubbles that originate in the U.S. or European stock markets, we extend the literature by investigating a bubble that originated in the Chinese economy. The S&P 500 index experienced a marked increase in volatility immediately preceding the crash of the Housing Bubble, while the Shanghai and Shenzhen indexes had marked decreases in volatility during the same period (Sornette et al 2018). However, even with these fluctuations in historical volatility, the volatility persistence could still maintain an elevated level.…”

Section: Financial Contagion and Volatility Persistencementioning

confidence: 99%

Financial Contagion: A Tale of Three Bubbles

Burks

Fadahunsi

Hibbert

2021

JRFM

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The primary purpose of the study is to identify and measure the properties of asset bubbles, volatility clustering, and financial contagion during three recent financial market anomalies that originated in the U.S. and Chinese markets. In particular, we focus on the 2000 DotCom Bubble, the 2008 Housing Crisis, and the 2015 Chinese Bubble. We employ three main empirical methods; the LPPL model to identify asset bubbles, the DCC-GARCH model to measure volatility clustering, and the Diebold-Yilmaz volatility spillover index to measure the level of financial contagion. We provide robust evidence that during the DotCom bubble there was very limited spillover between the S&P 500, the Shanghai, and the Shenzhen Composite Indexes. However, there was significantly more spillover effects in the two more recent crises, i.e., the Housing crisis and the 2015 Chinese Bubble. Together, these results highlight the fact that as financial markets have become more globalized, there are greater levels of volatility transmission and correspondingly fewer potential benefits from international diversification.

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Can we use volatility to diagnose financial bubbles? lessons from 40 historical bubbles

Cited by 62 publications

References 1 publication

Impact of Financial Development and Its Spatial Spillover Effect on Green Total Factor Productivity: Evidence from 30 Provinces in China

Impact of Financial Development and Its Spatial Spillover Effect on Green Total Factor Productivity: Evidence from 30 Provinces in China

Stochastic Price Dynamics Equations Via Supply and Demand; Implications for Volatility and Risk

Financial Contagion: A Tale of Three Bubbles

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