The global minimum variance portfolio computed using the sample covariance matrix is known to be negatively affected by parameter uncertainty, an important component of model risk. Using a robust approach, we introduce a portfolio rule for investors who wish to invest in the global minimum...

The global minimum variance portfolio computed using the sample covariance matrix is known to be negatively affected by parameter uncertainty. Using a robust control approach, we introduce a portfolio rule for investors who wish to invest in the global minimum variance portfolio due to its...

In contrast with existing literature that focuses on conditional Value-At-Risk (CVaR) as a portfolio risk measure, we examine here the properties of portfolios built to minimize CVaR. We look into the stability and performance potential of CVaR-optimal portfolios and compare our results with...

We present a stock selection methodology that maximizes the expected returns of equity portfolios by efficiently managing their exposures to a given ensemble of risk premia, also known as factors. Our approach is mathematically grounded, robust in its design, and applicable in practice. It...

For investors subject to Solvency II regulations, the capital charges and risk associated with holding equities are high. This reduces the appeal of this asset class as an investment of choice for realizing long-term growth objectives. Here we propose a simple risk mitigation technique that...

Efficient and robust techniques are crucial for hedging a book of variable annuities. For insurers, the profitability of variable annuity contracts heavily depend on the hedging schemes used to mitigate their associated financial liabilities. Beyond financial performance, the inclusion of...

This paper proposes intraday High Frequency Risk (HFR) measures for market risk in the case of irregularly spaced high-frequency data. In this context, we distinguish three concepts of value-at-risk (VaR): the total VaR, the marginal (or per-time-unit) VaR, and the instantaneous VaR. Since the...

This paper introduces a kernel-based nonparametric inferential procedure to test for Granger-causality in distribution. This test is a multivariate extension of the kernel-based Granger-causality test in tail-event introduced by Hong et al. (2009) and hence shares its main advantage, by checking...

We introduce in this paper a testing approach that allows checking whether two financial institutions are systemically equivalent, with systemic risk measured by CoVaR (Adrian and Brunnermeier, 2011). The test compares the difference in CoVaR forecasts for two financial institutions via a...

This paper introduces a kernel-based nonparametric inferential proce-