This study aims to overcome the problem of dimensionality, accurate estimation, and forecasting Value-at-Risk (VaR) and Expected Shortfall (ES) uncertainty intervals in high frequency data. A Bayesian bootstrapping and backtest density forecasts, which are based on a weighted threshold and...

Factor modeling is a popular strategy to induce sparsity in multivariate models as they scale to higher dimensions. We develop Bayesian inference for a recently proposed latent factor copula model, which utilizes a pair copula construction to couple the variables with the latent factor. We use...

You're probably familiar, at least in passing, with the 'convexity' of long-term bonds - i.e. that yields dropping 1% produce a bigger price move than yields rising 1%. A significant amount of brainpower has gone into understanding all the ramifications of this convexity in the fixed income...

This paper documents the existence of a slowly evolving trend in the dividend-price ratio, dp, determined by a demographic variable, MY: the middle-aged to young ratio. Deviations of the dividend-price ratio from this slowly evolving long-run component explain transitory but persistent...

The assumption that equity returns follow the normal distribution, most commonly made in financial economics theory and applications, is strongly rejected by empirical evidence presented in this paper. As it was found in many other studies, we confirm that stock returns follow a leptokurtic...

The bedrock of financial economics is that there should be a tradeoff between risk and reward: an investment with low risk should have a low expected return, while one that could make you rich should also be one which could lose you a lot of money. A lot of research in finance is focused on...

These days it's become convention (reinforced by the media's treatment of wealth) to assess our net worth by tallying up the market value of our financial assets, even though it's more natural and useful to think of our wealth as a stream of dollars over time given the nature of our income and...

Biases may reduce variability, which increases the decision maker's (concave) expected utility. Hence seeking unbiased estimates can be a strictly dominated decision approach under the expected utility criterion. Moreover, James-Stein shrinkage demonstrates that, by aggregating unrelated tasks...

Regularizing Bayesian predictive regressions provides a framework for prior sensitivity analysis via the regularization path. We jointly regularize both expectations and variance-covariance matrices using a pair of shrinkage priors. Our methodology applies directly to vector autoregressions...

We explore the performance of mixed-frequency predictive regressions for stock returns from the perspective of a Bayesian investor. We develop a constrained parameter learning approach for sequential estimation allowing for belief revisions. Empirically, we find that mixed-frequency models...