As the field of modelling mortality has grown in recent years, the number and importance of identifiability issues within mortality models has grown in parallel. This has led both to robustness problems and to difficulties in making projections of future mortality rates. In this paper, we...

We propose a novel conditional quantile prediction method based on the complete subset averaging (CSA) for quantile regressions. All models under consideration are potentially misspecified and the dimension of regressors goes to infinity as the sample size increases. Since we average over the...

Random forest regression (RF) is an extremely popular tool for the analysis of high-dimensional data. Nonetheless, its benefits may be lessened in sparse settings, due to weak predictors, and a pre-estimation dimension reduction (targeting) step is required. We show that proper targeting...

A variable annuity (VA) is an equity-linked annuity that provides investment guarantees to its policyholder and its contributions are normally invested in multiple underlying assets (e.g., mutual funds), which exposes VA liability to significant market risks. Hedging the market risks is...

From September 2011 to January 2015, the Swiss National Bank (SNB) implemented a minimum exchange rate regime (i.e. a one-sided target zone) vis- à-vis the euro to fight deflationary pressures in the aftermath of the Great Financial Crisis. During this period of unconventional monetary policy,...

This paper constructs individual-specific density forecasts for a panel of firms or households using a dynamic linear model with common and heterogeneous coefficients and cross-sectional heteroskedasticity. The panel considered in this paper features a large cross-sectional dimension N but...

We discuss estimation of so-called long vector autoregressions for multivariate series exhibiting possibly time-varying mean and (co)variances. In applied work, such changes often escape undetected, and we ask how standard tools (least squares estimation, point forecasts, and estimated impulse...

We propose factor-augmented out of sample forecasting models for the real exchange rate between Korea and the US. We estimate latent common factors by applying an array of data dimensionality reduction methods to a large panel of monthly frequency time series data. We augment benchmark...

We assess financial theory-based and machine learning-implied measurements of stock risk premia by comparing the quality of their return forecasts. In the low signal-to-noise environment of a one-month horizon, it is preferable to rely on a theory-based approach instead of engaging in...

In this paper we develop a general framework to analyze state space models with time-varying system matrices where time variation is driven by the score of the conditional likelihood. We derive a new filter that allows for the simultaneous estimation of the state vector and of the time-varying...