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...

We construct a network-based turbulence score that proves useful for analyzing the relationship between financial interconnectedness, and global market risk, and for identifying systemically important markets, with the highest contribution to financial turbulence. We apply our measure to study...

Longevity risk has emerged as an important risk in the early 21st century for the providers of pension benefits and annuities. Any changes in the assumptions for future mortality rates can have a major financial impact on the valuation of these liabilities and motivates many of the...

In this paper, we decompose credit default swap (CDS) spreads into a transitory component and a persistent component and test how these components are affected by the theoretical explanatory variables. We use three benchmark iTraxx Europe indices of two different maturities (5 and 10 years) and...

We represent credit spreads across ratings as a function of common unobservable factors of the mean-reverting normal (Vasicek) form. Using a state-space approach we estimate the factors, their process parameters, and the exposure of each observed credit spread series to each factor. We find that...

Given expected returns and return covariances, portfolio weights are known in closed form in a mean-variance framework. The real difficulty is in estimating these parameters. Using recent advances in Bayesian techniques, we show how investors can incorporate any prior information for optimal...

Recent theoretical models imply that liquidity is fragile: financial markets are liquid in some equilibria and illiquid in others. This paper employs an intuitively appealing Markov-switching regime model to investigate the episodic nature of stock market illiquidity and the intertemporal...

There is ample empirical evidence that the distribution of short-term security returns is non-normal. In spite of such evidence financial data analysis generally does not explicitly incorporate the higher statistical moments of skewness and kurtosis within a likelihood-based framework. In this...

We present and estimate a Bayesian Hierarchical model of mutual fund returns. In our model, a fund's alpha reflects not only that fund's return history, but also information from other fund returns. Because parameters are estimated simultaneously for all funds, we can identify common residual...

We extend the variance decomposition model of Campbell (1991) to allow for time-varying stock market volatility. Specifically, we introduce a model in which the covariance matrix of the vector autoregression (VAR) follows a multivariate stochastic volatility (MSV) process. This VAR-MSV model...