This paper develops an innovative regime switching multi-factor model accounting for the different regime switching behaviors in the systematic and idiosyncratic components of asset returns. A Gibbs sampling approach for estimation is proposed to deal with the computational challenges that arise...

This paper assumes that the market returns follow a two-state Markov process that randomly switches between bull and bear states. We show that in this case, the exponential moving average (EMA) represents the optimal trend-following rule. The paper provides the analytical solution to the optimal...

Forni (2005) is the first author to study the politico-economic sustainability of pay-as-you-go social security in the canonical 2-period OLG model that uses Cobb-Douglas production and logarithmic utility under Markovian voting strategies. This paper (1) shows that equilibria can only exist if...

Weakly coupled Markov decision processes (WDPs) arise in dynamic decision-making and reinforcement learning. These models are often high dimensional but decompose into smaller component MDPs when coupling constraints are relaxed. Lagrangian relaxations of WDPs that dualize linking constraints...

Stochastic volatility models are very flexible models able to characterize financial volatility evolution. This article explores computational capabilities based on Graphical Processing Units to simulate many Monte Carlo Markov chains in estimating stochastic volatility model parameters through...

Stochastic volatility models are very flexible models able to characterize financial volatility evolution. This article explores computational capabilities based on Graphical Processing Units to simulate many Monte Carlo Markov chains in estimating stochastic volatility model parameters through...

The RMB exchange rate policy follows a ``two-pillar'' rule, with the market pillar reflecting foreign exchange market condition and the basket pillar stabilizing RMB index. This paper documents a clear pattern of regime-switching in the policy coefficients on these two pillars. Particularly, the...

A natural way to obtain conditional density estimates for time series processes is to adopt a kernel-based (nonparametric) conditional density estimation (KCDE) method. To this end, the data generating process is commonly assumed to be Markovian of finite order. Markov processes, however, have...