The hype surrounding nonfungible tokens is well documented. Allegations of price manipulation and rampant speculation abound for this nascent asset class. However, are these hypes warranted? Or is it merely fear of a new asset class? Together with my coauthor Syed Tariq, I attempt an answer to this question by studying a large NFT transaction database to detect non-humanlike trading patterns which could be indicative of wash trading.
More details to follow.
In a forthcoming paper at the International Review of Financial Analysis, my co-authors and I document how interbank credit risk transmits from the US to five important emerging countries: Brazil, Russia, India, China, and South Africa. We achieve this by creating synthetic spreads representing funding liquidity risk and estimate a Bayesian model with time-varying parameters on daily data covering the bulk of the Global Financial Crisis, European Sovereign Debt Crisis, and the Covid-19 pandemic.
First, global policy indicators are weakly associated with interbank credit market situations in BRICS economies. Instead, the state of US financial system matters more. This finding derives from the overwhelmingly significant results stemming from Chicago Fed’s NFCI indicator, which has a reputation for a leading indicator of US economic activities.
Second, our results’ temporal patterns imply that key central banking decisions precede or coincide with reduced spillover.
Third, we further examine whether interbank credit crunch depresses market liquidity in the corresponding domestic markets using a Granger causality approach. The results indicate that it often does, and the augmented conditional causality analysis shows that the state of fear and credit market conditions in the US economy holds some causal influence on the aforesaid relationship.
The published version of the paper is linked below:
If you require an author’s copy PDF, do send me an e-mail. Alternatively, you can check an earlier version on SSRN or Researchgate.
Imtiaz Sifat (Radboud University), Alireza Zarei (Coventry University), Seyed Hosseini (Cardiff University), and Elie Bouri (Lebanese American University)
This paper investigates the predictive ability of web search behavior in connection with stock market returns and trading volume in five emerging economies in the ASEAN region using econometric and signal-processing techniques. More specifically, we use Vector Error Correction Model in conjunction with Wavelet analysis and find consistently low predictive ability of search activity in Google. In fact, investors in nearly all five markets appear to be interested in searching terms related to the market after high returns or high trading activities occur. In other words, high returns or high activities precede search interest. Our findings are at odds with the general results reported in earlier studies conducted in developed countries. Additionally, our analysis in the time-frequency domain detects a two-week lead-lag phenomenon in the association between search behavior and market returns for all markets but the Philippines.
Full paper can be accessed at publisher’s website: