We propose a discrete-time stochastic volatility model in which regimeswitching serves three purposes. First, changes in regimes capture low frequency variations, which is their traditional role. Second, they specify intermediate frequency dynamics that are usually assigned to smooth...

We propose a method to aggregate heterogeneous individual beliefs, givena competitive equilibrium in complete asset markets, into a single “marketprobability” such that it generates, if commonly shared by all investors, thesame marginal valuation of assets by the market (the same equilibrium...

This paper investigates risk-taking in the liquid portfolios held by a large panel of Swedish twins. We document that the portfolio share invested in risky assets is an increasing and concave function of financial wealth, leading to different risk sensitivities across investors. Human capital,...

This paper investigates the determinants of value and growth investing in a large administrative panel of Swedish residents over the 1999-2007 period. We document strong relationships between a household's portfolio tilt and the household's financial and demographic characteristics. Value...

This paper investigates the efficiency of household investment decisions in a unique dataset containing the disaggregated wealth and income of the entire population of Sweden. The analysis focuses on two main sources of inefficiency in the financial portfolio: underdiversification of risky...

As the Great Financial Crisis reminds us, extreme movements in the level and volatility of asset prices are key features of financial markets. These phenomena are difficult to quantify using traditional models that specify extreme risk as a rare event. Multifractal analysis, whose use in finance...

Filtering methods are powerful tools to estimate the hidden state of a state-space model from observations available in real time. However, they are known to be highly sensitive to the presence of small misspecifications of the underlying model and to outliers in the observation process. In this...

In nonlinear state-space models, sequential learning about the hidden state can proceed by particle filtering when the density of the observation conditional on the state is available analytically (e.g. Gordon et al. 1993). This condition need not hold in complex environments, such as the...