This paper discusses estimation of US inflation volatility using time varying parameter models, in particular whether it should be modelled as a stationary or random walk stochastic process. Specifying inflation volatility as an unbounded process, as implied by the random walk, conflicts with...

Inflation expectations play a key role in determining future economic outcomes. The associated uncertainty provides a direct gauge of how well-anchored the inflation expectations are. We construct a model-based measure of inflation expectations uncertainty by augmenting a standard unobserved...

We propose an easy technique to test for time-variation in coefficients and volatilities. Specifically, by using a noncentered parameterization for state space models, we develop a method to directly calculate the relevant Bayes factor using the Savage-Dickey density ratio — thus avoiding the...

Trend inflation estimates for 12 of the largest Asian economies over 1995-2018 offer important insights on inflation dynamics and inflation expectations. The disinflationary shocks that hit the region since 2014 were partly transitory, but their effects have been different depending on the...

This paper studies the joint dynamics of U.S. inflation and a term structure of average inflation predictions taken from the Survey of Professional Forecasters (SPF). We estimate these joint dynamics by combining an unobserved components (UC) model of inflation and a sticky‐information...

We propose an unobserved components model with stochastic volatility and structural shocks to explore the relevant factors that influence trend inflation in the USA. Using structural shocks that incorporate a broad set of information for the US economy, we find that four structural shocks have...

We propose a new methodology for designing flexible proposal densities for the joint posterior density of parameters and states in a nonlinear, non-Gaussian state space model. We show that a highly efficient Bayesian procedure emerges when these proposal densities are used in an independent...

This paper investigates the return-volatility relation by taking into account the model specification problem. The market volatility is modeled to have two components, one due to the diffusion risk and the other due to the jump risk. The model indicates that under the absence of leverage...

A Hidden Markov Model (HMM) is used to model the VIX (the Cboe Volatility Index). A 4- state Gaussian mixture is fitted to the VIX price history from 1990 to 2022. Using a growing window of training data, the price of the S&P500 is predicted and two trading algorithms are presented, based on the...