The Sasin Research Seminar series continued with a talk by Assistant Professor Wantanee Poonvoralak, Ph.D., DIC, CStat relating to a paper entitled “Bayesian inference in GARCH(1,1) Model for Cryptocurrencies”. Part of the paper was presented at the Quantitative Method in Finance (QMF) Conference in Sydney, Australia 17-20th December 2019 and has been under review since 2019. The lecture began with Assistant Professor Wantanee explaining the GARCH model and how it is used. Essentially, GARCH is a volatility model used to analyze and measure volatility in financial markets and data. There are different types of volatility stylized that need to be analyze in both research and banking industries. Cryptocurrency financial data and returns prices were then discussed. Such data are incredibly volatile but have rapidly grown in popularity, especially during the Covid-19 outbreak. Bitcoin is still the largest and most active cryptocurrency market, followed by Ethereum, so these were used for the study. Assistant Professor Wantanee then gave some insights into the financial market movements of these currencies and talked about how blockchain worked, along with its advantages. At present, as crypto is peer-to-peer and operates outside the financial markets, central banks are excluded. Assistant Professor Wantanee then explained the Generalized Autoregressive-Conditional Heteroskedasticity (GARCH) model. GARCH (1,1) is one of the most popular and useful financial volatility models worldwide. It is used to identify the best true volatility effects of the cryptocurrency data. The Bayesian GARCH(1,1) probability model was then introduced, along with the formulas and parameters behind it and the complexities in the estimation process that involved. For this presentation, Assistant Professor Wantanee applied the simple univariate GARCH(1,1) and Student-t GARCH(1,1) model using statistical inferences for the estimation. In her summary, she introduced the Bayesian inference as her further work and suggested that the BASEL committee must consider whether cryptocurrency should play as part of the financial market data relating to market risk management. The presentation was ended with two questions from about 30+ audiences. The first was whether it is a good idea to invest in cryptocurrencies where the answer was yes. The last was the reason why GARCH was presented more as a value to the cryptocurrency than capturing the volatility effect; the answer was purely based on how the banking industry expected in the final report of this type of model for each financial market data.