Nima Nonejad

Particle Gibbs with Ancestor Sampling for Stochastic Volatility Models with: Heavy Tails, in Mean Effects, Leverage, Serial Dependence and Structural Breaks

Particle Gibbs with ancestor sampling (PG-AS) is a new tool in the family of sequential Monte Carlo methods. We apply PG-AS to the challenging class of unobserved component time series models and demonstrate its flexibility under different circumstances. We also combine discrete structural breaks within the unobserved component model framework. We do this by modeling and forecasting time series characteristics of postwar US inflation using a long memory autoregressive fractionally integrated moving average model with stochastic volatility where we allow for structural breaks in the level, long and short memory parameters contemporaneously with breaks in the level, persistence and the conditional volatility of the volatility of inflation.

Has the 2008 financial crisis and its aftermath changed the impact of inflation on inflation uncertainty in member states of the european monetary union

We study to what extent the financial crisis of 2008 and its aftermath have changed the impact of inflation on inflation uncertainty in the 12 original member states of the European Monetary Union (EMU). We adopt a time‐varying coefficient regression model with stochastic volatility effects, and extract two measures of inflation uncertainty from our data, namely, (1) The conditional volatility of inflation, (2) The conditional volatility of steady‐state inflation. (1)–(2) represent short‐run and steady‐state inflation uncertainty, respectively. The time‐varying impact of inflation on inflation uncertainty is analyzed using Markov‐switching regressions, where switching between the low and high inflation uncertainty regime is determined via an unobserved Markov process. Results suggest that the 2008 financial crisis and its aftermath have changed the impact of inflation on (1) and (2) across the selected EMU member states. However, a uniform pattern cannot be detected. For some member states, we document a strong link, whereas for others, the impact of inflation on inflation uncertainty is relatively weaker.

Crude oil price volatility dynamics and the great recession

ABSTRACT Using a very simple econometric framework, we identify two major changes in the dynamics of crude oil price volatility based on data from 1997 to 2017. More precisely, we model weekly West Texas Intermediate (WTI) crude oil price realized volatility in a two-regime setting, one where realized volatility evolves as a plain autoregressive (AR) process (static), and the other where the level, persistence and innovation volatility of the AR process are subject to changes (dynamic). We use a Markov chain to model the probability that the process is in the static regime. The post Great Recession period sees a longer duration of the dynamic regime as well as smaller changes in the level and conditional volatility of realized volatility when switching actually occurs. Crude oil volatility also responds more aggressively to changes in economic variables, such as the t-bill rate and equity market volatility in the dynamic regime.