Double-Question Survey Measures for the Analysis of Financial Bubbles and Crashes

Abstract

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. -or under-valued), and another regarding his/her expectations of the future price of that asset. Responses to these two questions are then used to measure the extent to which prices are likely to move towards or away from the subjectively perceived fundamental values. Using a theoretical asset pricing model with heterogenous agents we show that there exists a negative relationship between the agents expectations of price changes and their asset valuation. Double question surveys on equity, gold and house prices provide evidence in support of such relationships, particularly in the case of house price expectations. The effects of demographic factors, such as sex, age, education, ethnicity, and income are also investigated. It is shown that for house price expectations such demographic factors cease to be statistically significant once we condition on the respondents. location and their asset valuation indicator. The results of the double-question surveys are then used to construct leading bubble and crash indicators, and their potential value is illustrated in the context of a dynamic panel regression of realized house price changes across a number of key Metropolitan Statistical Areas in the US. Terms of use: Documents inJEL-Codes: C830, D840, G120, G140.

Sentiment-Induced Bubbles in the Cryptocurrency Market

“…Recent tests proposed by (Phillips et al 2011(Phillips et al , 2015 provide powerful tests, essentially based on the supremum of sequential unit root test statistics, and have been applied to the cryptocurrency markets by (Cheung et al 2015;Corbet et al 2018;Hafner 2018), where the latter accounts for time-varying volatility. These tests, however, are purely statistical in nature and do not allow us to infer if structural breaks detected in the time series processes of asset prices are evidence of bubbles or are due to breaks in the underlying (unobserved) fundamentals (Pesaran and Johnsson 2018). An inclusion of extracted sentiment information, representing the sentiment in the crypto community with their specific linguistic features, contributes to solving this inconclusive puzzle and adds economic and behavioral information into the statistical settings.…”