Comparative Bayesian Analysis of GARCH and Stochastic Volatility Models using R and Stan

This study uses modelling and model comparison to compare three widely used GARCH models with their stochastic volatility (SV) counterparts in modelling the dynamics of inflation rates using the Bayesian approach. BRICS country consumer price index (CPI) data are used to assess these models. We find that the stochastic volatility models perform better than the GARCH models most of the time. The stochastic volatility in the leverage (SV-L) model is also demonstrated to be the most effective for the BRICS nations that we took into consideration. The article also looks at which model attributes are crucial in simulating inflation rates. It turns out that when modelling inflation rates, inflation volatility feedback is an important component to take into account. For each of the five countries we took into consideration, SV-L outperforms all other models. The study was done in rstan, a programming language for statistical inference, and the simulation uses the Hamiltonian Monte Carlo (HMC) algorithm of the Markov chain Monte Carlo (MCMC) to sample from the posterior distribution

Bayesian Survival Analysis of Acute Encephalitis Syndrome with Censoring Mechanism using Brms Package

Acute encephalitis syndrome(AES) most commonly affects children and young adults and can lead to considerable morbidity and mortality. In June 2019, the outbreak of acute encephalitis syndrome occurred in Muzaffarpur district and their neighbouring district of Bihar. This paper presents the Bayesian survival analysis of AES data of the Muzaffarpur district. AES data extracted from the SKMCH and KM hospital of Muzaffarpur. The Weibull, Log-normal, and Exponential, these survival models have been used for fitting of AES data with the help of brms packages of R and compared these models with the Leave one out cross-validation. brms package uses the Hamiltonian Monte Carlo(HMC) sampler and its extension, no-U-turn sampler (NUTS) algorithm of MCMC, for the simulation study. In addition, the Logistic regression model is used to predict the risk of death on the basis of observed characteristics or covariates

Modeling high-frequency financial data using R and Stan: A bayesian autoregressive conditional duration approach

In econometrics, Autoregressive Conditional Duration (ACD) models use high-frequency economic or financial duration data, which mostly exhibit irregular time intervals. The ACD model is widely used to examine the duration of transaction volume and duration of price variations in stock markets. In this work, our goal is to devise testing that will aid in the identification of the best potential duration model among a set of four models using Bayesian approach. We test three models that rely on conditional mean duration (Weibull ACD, Log Weibull ACD, Generalized Gamma ACD) and one conditional median duration model (Birnbaum-Saunders ACD), and are being compared each other. The study was done in Rstan, a programming language for statistical inference, and the simulation uses the Hamiltonian Monte Carlo (HMC) algorithm of Markov Chain Monte Carlo (MCMC) to sample from the posterior distribution. Our findings show that Log Weibull ACD (second-generation model) as best among the four models followed by Birnbaum-Saunders ACD (third-generation model). The result offers methodological implications for algorithmic trading (algo-trading), high-frequency trading and risk management