Joint value at risk: a new conditional risk measure

In this PhD thesis a new firm level conditional risk measure is developed. It is named Joint Value at Risk (JVaR) and is defined as a quantile of a conditional distribution of interest, where the conditioning event is a latent upper tail event. It addresses the problem of how risk changes under extreme volatility scenarios. The properties of JVaR are studied based on a stochastic volatility representation of the underlying process. We prove that JVaR is leverage consistent, i.e. it is an increasing function of the dependence parameter in the stochastic representation. A feasible class of nonparametric M-estimators is introduced by exploiting the elicitability of quantiles and the stochastic ordering theory. Consistency and asymptotic normality of the two stage M-estimator are derived, and a simulation study is reported to illustrate its finite-sample properties. Parametric estimation methods are also discussed. The relation with the VaR is exploited to introduce a volatility contribution measure, and a tail risk measure is also proposed. The analysis of the dynamic JVaR is presented based on asymmetric stochastic volatility models. Empirical results with S&P500 data show that accounting for extreme volatility levels is relevant to better characterize the evolution of risk. The work is complemented by a review of the literature, where we provide an overview on quantile risk measures, elicitable functionals and several stochastic orderings.

Sex differences in coronary artery disease severity and mortality: the impact of conventional cardiovascular risk factors

It is still unknown whether traditional risk factors may have a sex specific impact on the severity of coronary artery disease (CAD) and subsequent mortality in acute coronary syndromes (ACS). We identified 14 793 patients who underwent coronary angiography for acute coronary syndromes in the ISACS-TC (NCT01218776) registry from 2010 to 2019. The main outcome measure was the association between conventional risk factors and severity of CAD and its relationship with 30-day mortality. Risk ratios (RRs) and 95% CIs were calculated from the ratio of the absolute risks of women versus men using inverse probability of weighting. Severity of disease was categorized as obstructive (≥50% stenosis) versus nonobstructive CAD, specifically Ischemia and No Obstructive Coronary Artery disease (INOCA) and Myocardial Infarction with Non obstructive Coronary Arteries (MINOCA). The RR ratio for obstructive CAD in women versus men among people without diabetes mellitus was 0.49(95%CI,0.41–0.60) and among those with diabetes mellitus was 0.89(95% CI,0.62–1.29), with an interaction by diabetes mellitus status of P =0.002. Exposure to smoking shifted the RR ratios from 0.50 (95% CI, 0.41–0.61) in nonsmokers to 0.75 (95%CI, 0.54–1.03) in current smokers, with an interaction by smoking status of P=0.018. There were no significant sex-related interactions with hypercholesterolemia and hypertension. Women with obstructive CAD had higher 30-day mortality rates than men (RR, 1.75; 95% CI, 1.48–2.07). No sex differences in mortality were observed in patients with INOCA/MINOCA. In conclusion, obstructive CAD in women signifies a higher risk for mortality compared with men. Current smoking and diabetes mellitus disproportionally increase the risk of obstructive CAD in women. Achieving the goal of improving cardiovascular health in women still requires intensive efforts toward further implementation of lifestyle and treatment interventions.

Topics in climate change risk

In this Thesis, we analyze how climate risk impacts economic players and its consequences on the financial markets. Essentially, literature unravels two main channels through which climate change poses risks to the status quo, namely physical and transitional risk, that we cover in three works. Firstly, the call for a global shift to a net-zero economy implicitly devalues assets that contribute to global warming that regulators are forcing to dismiss. On the other hand, abnormal changes in the temperatures as well as weather-related events challenge the environmental equilibrium and could directly affect operations as well as profitability. We start the analysis with the physical component, by presenting a statistical measure that generally represents shocks to the distribution of temperature anomalies. We oppose this statistic to classical physical measures and assess that it is the driver of the electricity consumption, in the weather derivatives market, and in the cross-section of equity returns. We find two transmission channels, namely investor attention, and firm operations. We then analyze the transition risk component, by associating a regulatory horizon characterization to fixed income valuation. We disentangle a risk driver for corporate bond overperformance that is tight to change in credit riskiness. After controlling a statistical learning algorithm to forecast excess returns, we include carbon emission metrics without clear evidence. Finally, we analyze the effects of change in carbon emission on a regulated market such as the EU ETS by selecting utility sector corporate bond and, after controlling for the possible risk factor, we document how a firm’s carbon profile differently affects the term structure of credit riskiness.