This paper reinvestigates the existing anomalous relationship between inflation and REIT returns in the USA. To analyze it, we employ both observed and unobserved regime-switching VAR models, along with two additional variables, namely, relative price variability and output growth. We find that...

We propose a reinforcement learning (RL) approach to solve the continuous-time mean-variance portfolio selection problem in a regime-switching market, where the market regime is unobservable. To encourage exploration for learning, we formulate an exploratory stochastic control problem with an...

We develop a new dynamic model of annuitization in which a constant relative risk aversion utility maximizing individual receives non-traded labor income and has to decide on her allocation between a stock and a risk-free asset, as well as decide on the time when she starts her retirement...

The long-memory and nonlinearity coexist in realised volatility. This paper incorporates the linear AR and HAR models with regime-switching models, including the smooth transition and Markov-switching approaches, to assess the forecasting performance of realized volatility. In-sample results...

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...

Aggregate U.S. labor market dynamics are well approximated by a dual labor market supplemented with a third, predominantly, home-production segment. We uncover this structure by estimating a Hidden Markov Model, a machine-learning method. The different market segments are identified through...

A recent strand of the literature has proposed stochastic time-varying coefficient models for modelling structural change in the macroeconomy under both exogeneity and endogeneity. Subsequently, a new class of kernel based non-parametric estimators has been introduced for these models. These...

Aggregate U.S. labor market dynamics are well approximated by a dual labor market supplemented with a third, predominantly, home-production segment. We uncover this structure by estimating a Hidden Markov Model, a machine-learning method. The different market segments are identified through...

Parrondo’s paradox was introduced by Juan Parrondo in 1996. In game theory, this paradox is described as: A combination of losing strategies becomes a winning strategy. At first glance, this paradox is quite surprising, but we can easily explain it by using simulations and mathematical...

Answering a counterfactual query (CQ) in a hidden Markov model (HMM) requires embedding the HMM in a so-called structural causal model (SCM). In general, however, we don't have sufficient information to fully specify the SCM and so we cannot provide an exact answer to the CQ. Nonetheless, the...