Center of Risk Management

HEC Lausanne

Université de Lausanne

1015 Lausanne

Optimal Strategies for ESG Portfolios

Fabio Alessandrini and Eric Jondeau


Journal of Portfolio Management, 47(2), 114-138


Previous research provides evidence that in the last decade, investing according to screening based on environmental, social, and governance (ESG) criteria would have allowed investors to considerably improve the ESG quality of their portfolio without deteriorating its financial performance. However, a drawback of such a screening process is that it possibly generates undesirable regional, sectoral, and risk factor exposures. In this paper, we propose an investment strategy that maximizes the ESG quality of the portfolio while maintaining regional, sectoral, and risk factor exposures within stated limits. We provide evidence that such a portfolio would have produced a risk-adjusted performance at least as high as the standard MSCI benchmark for a wide range of ESG criteria and regions over the 2007{2018 investment period.




ESG Investing: From Sin Stocks to Smart Beta

Fabio Alessandrini and Eric Jondeau


Journal of Portfolio Management, 46(2), 75-94


Research on socially responsible investment in equity markets initially focused on sin stocks. Since then, the availability of data has been extended substantially and now covers environmental, social, and governance (ESG) criteria. Using ESG scores of firms belonging to the MSCI World universe, we measure the impact of score-based exclusion on both passive investment and smart beta strategies. We find that exclusion leads to improved scores of otherwise standard portfolios without deterioration of their risk-adjusted performance. Smart beta strategies exhibit a similar pattern, often in a more pronounced way. Moreover, our results demonstrate that exclusion also implies regional and sectoral tilts as well as (possibly undesirable) risk exposures of the portfolios.




Dynamic Conditional Beta and Systemic Risk in Europe

Robert Engle, Eric Jondeau, and Michael Rockinger


Review of Finance, 19(1), 145-190


Systemic risk can be defined as the propensity of a financial institution to be under-capitalized when the financial system as a whole is under-capitalized. It combines the market capitalization of the firm, the sensitivity of its equity return to market shocks, and its financial leverage. In this paper, we describe an econometric approach designed to measure systemic risk for non-U.S. institutions. We extend the approach developed by Brownlees and Engle (2010) to the case with several factors explaining the dynamic of financial firms' return and with asynchronicity of the time zones. Our model combines a DCC model to estimate the dynamic of the beta parameters, univariate GARCH models to estimate the dynamic of the volatility of the error terms, and a dynamic t copula to estimate the dynamic of the dependence structure between the innovations. We apply this methodology to the 194 largest European financial firms and estimate their systemic risk over the 2000-2012 period. We find that banks and insurance companies bear about 80% and 20% of the systemic risk in Europe, whereas systemic risk is essentially unaffected by financial services and real estate firms. Over the recent period, the systemically riskiest countries are the UK and France, and the riskiest firms are Deutsche Bank and BNP Paribas.

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Non-parametric estimation of intraday spot volatility: disentangling instantaneous trend and seasonality

Thibault Vatter, Hau-Tieng Wu, Valérie Chavez-Demoulin, and Bin Yu

November 2013


We provide a new framework to model trends and periodic patterns in high-frequency financial data. Seeking adaptivity to ever changing market conditions, we enlarge the popular Fourier flexible form into a richer (time- varying) functional class and relax the assumptions from usual (static) intraday models in two directions. First, the realized volatility (i.e. the slowly time-varying component) is modelled as an instantaneous trend which evolves in real time. Second, the seasonality is no longer assumed constant over the sample. We provide the estimators associated with our class models and show that they have low variance and are essentially un- biased. As an application, we analyze the trajectories of the spot volatility in the foreign exchange market. Our results suggest that failing to factor in the seasonality’s dynamic properties may lead to severe underestima- tion/overestimation of the intraday spot volatility.

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An Extreme Value Approach For Modeling Operational Risk Losses Depending on Covariates

Valérie Chavez-Demoulin, Paul Embrechts, Marius Hofert

November 2013


A general methodology for modeling loss data depending on covariates is developed. The parameters of the frequency and severity distributions of the losses may depend on covariates. The loss frequency over time is modeled via a non-homogeneous Poisson process with integrated rate function depending on the covariates. This corresponds to a generalized additive model which can be estimated with spline smoothing via penalized maximum likelihood estimation. The loss severity over time is modeled via a nonstationary generalized Pareto model depending on the covariates. Whereas spline smoothing can not be directly applied in this case, an efficient algorithm based on orthogonal parameters is suggested. The methodology is applied to a database of (mostly banking) operational risk losses. Estimates, including confidence intervals, for risk measures such as Value-at-Risk or Expected-Shortfall as required by the Basel II/III framework are computed. We provide links to a detailed R implementation of the statistical methodology.

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Assessing the Drivers of Change for Cross-Border Supply Chains

Ari-Pekka Hameri and Johua Hintsa


International Journal of Physical Distribution & Logistics, 39(9), 741–761


Purpose – This paper aims to systematically document drivers of change and the implications they will have on international supply chain management in the coming two decades.

Design/methodology/approach – This study was commissioned by the World Customs Organization (WCO) at the end of June 2006. Because of increased trade volumes, emerging complex supply networks and heightened security concerns, the WCO saw the need to assess future trends and drivers in supply chain management. The Delphi method was applied to identify a set of foreseeable drivers of change and to assess their predicted impact on global supply chain management in the coming ten to 20 years. Based on a literature review of 150 recent publications and interviews among 33 industry, academic and customs experts, a survey was designed and conducted to collect current and potential change drivers in global supply chains. These drivers were compiled and prioritized by an eclectic team of 12 specialists.

Findings – The main results of the study are strongly connected to strategic and operational supply chain planning for the next ten to 20 years. They are related to increased off-shoring of operations through truly global manufacturing, characterized by its intercontinental supply of materials; increased product complexity with shorter product life cycles; increased importance of business-to-government networking for operational and security efficiency; introduction of new supply chain services integrating financial, physical and information flows leading to further consolidation in the logistics markets; and the overall increase in risks and vulnerabilities in international supply chains.

Originality/value – This paper provides a 360 degree view of the future of international supply chain management and the challenges companies will face to compete in the twenty-first century business environment.