Can a stochastic cusp catastrophe model explain stock market crashes?

Baruník, Jozef; Vošvrda, Miloslav

doi:10.1016/j.jedc.2009.04.004

Cited by 61 publications

(49 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, the cusp catastrophe model explains the data very well, and we can conclude that the stock market crash of 1987 was led by internal forces. This result confirms our previous findings in Barunik and Vosvrda (2009), although a comparison cannot be made directly because we used a different sample length in our previous study. When comparing the fits of the unrestricted and restricted models, we can see that they do not differ significantly within the log likelihoods, AIC and BIC.…”

Section: Examples Of the 1987 And 2008 Crashessupporting

confidence: 91%

“…Figure 4 shows the evolution of the S&P 500 stock index returns over almost 27 years and documents how volatility strongly varies over time. One of the possible and very simple solutions in applying cusp catastrophe theory to the stock markets is to consider only a short time window and to fit the catastrophe model to data where volatility can be assumed to be constant (Barunik and Vosvrda, 2009). Although in Barunik and Vosvrda (2009) we were the first to quantitatively apply stochastic catastrophes to explain stock market crashes on localized periods of crashes, this assumption is generally very restrictive.…”

Section: Cusp Catastrophe Under Time-varying Volatilitymentioning

confidence: 99%

“…In choosing the control variables, we follow the successful method from our previous application in Barunik and Vosvrda (2009), where we compared several measures of control variables and showed that fundamentalists, or the asymmetry side of the market, are best described by the ratio of advancing and declining stock volume, and chartists, or the bifurcation side of the model, are best described by the OEX put/call ratio 5 . The variables related to the trading volume generally correlate with the volatility and therefore are considered good measures of the trading activity of large funds and other institutional investors.…”

Section: Data Descriptionmentioning

confidence: 99%

“…The main restriction of Cobb's method of estimation was the requirement of a constant variance, which forces researchers to assume that the volatility of the stock markets (as the standard deviation of the returns) is constant. Quantitative verification of hypotheses about the application of the theory to stock market crashes was pioneered by Barunik and Vosvrda (2009), where we fit the cusp model to two separate, large stock market crashes. However, the successful application Barunik and Vosvrda (2009) brought only preliminary results in a restricted environment.…”

mentioning

confidence: 99%

“…Quantitative verification of hypotheses about the application of the theory to stock market crashes was pioneered by Barunik and Vosvrda (2009), where we fit the cusp model to two separate, large stock market crashes. However, the successful application Barunik and Vosvrda (2009) brought only preliminary results in a restricted environment. Application of the cusp catastrophe theory on stock market data deserves much more attention.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Realizing stock market crashes: stochastic cusp catastrophe model of returns under time-varying volatility

Baruník

Kukačka

2014

Quantitative Finance

View full text Add to dashboard Cite

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. This paper develops a two-step estimation methodology that allows us to apply catastrophe theory to stock market returns with time-varying volatility and to model stock market crashes. In the first step, we utilize high-frequency data to estimate daily realized volatility from returns. Then, we use stochastic cusp catastrophe on data normalized by the estimated volatility in the second step to study possible discontinuities in the markets. We support our methodology though simulations in which we discuss the importance of stochastic noise and volatility in a deterministic cusp catastrophe model. The methodology is empirically tested on nearly 27 years of U.S. stock market returns covering several important recessions and crisis periods. While we find that the stock markets showed signs of bifurcation in the first half of the period, catastrophe theory was not able to confirm this behavior in the second half. Translating the results, we find that the U.S. stock market's downturns were more likely to be driven by the endogenous market forces during the first half of the studies period, while during the second half of the period, the exogenous forces seem to be driving the market's instability,. The results suggest that the propose methodology provides an important shift in the application of catastrophe theory to stock markets. Terms of use: Documents in AbstractThis paper develops a two-step estimation methodology that allows us to apply catastrophe theory to stock market returns with time-varying volatility and to model stock market crashes. In the first step, we utilize high-frequency data to estimate daily realized volatility from returns. Then, we use stochastic cusp catastrophe on data normalized by the estimated volatility in the second step to study possible discontinuities in the markets. We support our methodology through simulations in which we discuss the importance of stochastic noise and volatility in a deterministic cusp catastrophe model. The methodology is empirically tested on nearly 27 years of U.S. stock market returns covering several important recessions and crisis periods. While we find that the stock markets showed signs of bifurcation in the first half of the period, catastrophe theory was not able to confirm this behavior in the second half. Translating the results, we find that the U.S. stock market's downturns were more likely to be driven by the endogenous market forces during the first half of the studied period, while during ...

show abstract

Section: Examples Of the 1987 And 2008 Crashessupporting

confidence: 91%

Section: Cusp Catastrophe Under Time-varying Volatilitymentioning

confidence: 99%

Section: Data Descriptionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Realizing stock market crashes: stochastic cusp catastrophe model of returns under time-varying volatility

Baruník

Kukačka

2014

Quantitative Finance

View full text Add to dashboard Cite

show abstract

Elementary Catastrophe Theory

Wagenmakers

Grasman

Maas

et al. 2015

Wiley StatsRef: Statistics Reference Online

View full text Add to dashboard Cite

Catastrophe theory describes how small, continuous changes in control parameters (i.e., independent variables that influence the state of a system) can have sudden, discontinuous effects on dependent variables. Such discontinuous, jumplike changes are called phase‐transitions or catastrophes . Examples include the sudden collapse of a bridge under slowly mounting pressure, and the freezing of water when temperature is gradually decreased. This entry expands on the ideas inherent in catastrophe models.

show abstract

Financial Connectedness of Energy and Commodity Markets and Systemic Risk

Kahyaoğlu

2021

Contributions to Finance and Accounting

View full text Add to dashboard Cite

Can a stochastic cusp catastrophe model explain stock market crashes?

Cited by 61 publications

References 40 publications

Realizing stock market crashes: stochastic cusp catastrophe model of returns under time-varying volatility

Realizing stock market crashes: stochastic cusp catastrophe model of returns under time-varying volatility

Elementary Catastrophe Theory

Financial Connectedness of Energy and Commodity Markets and Systemic Risk

Contact Info

Product

Resources

About