DETECTING PONZI SCHEMES IN CRYPTO BY USING ECONOMETRIC MODELS: A CASE STUDY OF FUTURE EXCHANGE TRADING LIMITED (FTX) COLLAPSE

Abstract

The objectives of this research study are to 1) develop a rational speculative bubble model that can identify FTX projects, fraudulent investment scams, and 2) develop a GARCH-M model to detect FTX fraudulent activities as an early warning signal for Investment fraud scheme This research is empirical research that focuses on developing an econometric model to detect schemes. Investment fraud scheme in the cryptocurrency market using FTX as a case study. The research will use historical transaction prices and volume data from FTX to develop and test the model. Research is limited to the use of econometric models. The research methodology involved collecting relevant data on the cryptocurrency, identifying key variables indicative of Ponzi schemes, and applying econometric models. The study looked at how well econometric models worked for finding Ponzi schemes in the FTX coin. The results showed that GARCH-M volatility model and rational speculative models was quite successful in spotting exchange fraud. FTT pricing all showed a substantial correlation with Ponzi scam activity. As a result, GARCH-M model may be used as an early detection tool for illegal activities. This econometric model could detect and prevent fraud in order to protect investors. The GARCH-M volatility model and rational speculative bubble model can be used as tools to identify ponzi schemes and other frauds in advance.