We address the calibration issues of the weighted-indexed semi-Markov chain (WISMC) model applied to high-frequency financial data. Specifically, we propose to automate the discretization of the price returns and the volatility index by using four different approaches, two based on statistical...

According to no-arbitrage, risk-adjusted returns should be unpredictable. Using several prominent factor models and a large cross-section of anomalies, we find that past pricing errors predict future risk-adjusted anomaly returns. We show that past pricing errors can be interpreted as deviations...

I entertain a generalization of the standard Bolzmann-Gibbs-Shannon measure of entropy in multiplier preferences of model uncertainty. Using this measure, I derive a generalized exponential certainty equivalent, which nests the exponential certainty equivalent of the standard Hansen-Sargent...

In this paper, we propose a multivariate quantile regression method which enables localized analysis on conditional quantiles and global comovement analysis on conditional ranges for high-dimensional data. The proposed method, hereafter referred to as FActorisable Sparse Tail Event Curves, or...

For many applications, analyzing multiple response variables jointly is desirable because of their dependency, and valuable information about the distribution can be retrieved by estimating quantiles. In this paper, we propose a multi-task quantile regression method that exploits the potential...

Agent based models of financial markets follow different approaches and might be categorized according to major building blocks used. Such building blocks include agent design, agent evolution, and the price finding mechanism. The performance of agent based models in matching key features of...

The Nelson-Siegel-Svensson model is widely-used for modelling the yield curve, yet many authors have reported ‘numerical difficulties' when calibrating the model. We argue that the problem is twofold: firstly, the optimisation problem is not convex and has multiple local optima. Hence standard...

This paper addresses the open debate about the effectiveness and practical relevance of highfrequency (HF) data in portfolio allocation. Our results demonstrate that when used with proper econometric models, HF data offers gains over daily data and more importantly these gains are maintained...

This paper addresses the open debate about the usefulness of high-frequency (HF) data in large-scale portfolio allocation. Daily covariances are estimated based on HF data of the S&P 500 universe employing a blocked realized kernel estimator. We propose forecasting covariance matrices using a...

This paper addresses the open debate about the usefulness of high-frequency (HF) data in large-scale portfolio allocation. We consider the problem of constructing global minimum variance portfolios based on the constituents of the S&P 500 over a four-year period covering the 2008 financial...