Topics in financial econometrics

In the first chapter, we consider the joint estimation of objective and risk-neutral parameters for SV option pricing models. We propose a strategy which exploits the information contained in large heterogeneous panels of options, and we apply it to S&P 500 index and index call options data. Our approach breaks the stochastic singularity between contemporaneous option prices by assuming that every observation is affected by measurement error. We evaluate the likelihood function by using a MC-IS strategy combined with a Particle Filter algorithm. The second chapter examines the impact of different categories of traders on market transactions. We estimate a model which takes into account traders’ identities at the transaction level, and we find that the stock prices follow the direction of institutional trading. These results are carried out with data from an anonymous market. To explain our estimates, we examine the informativeness of a wide set of market variables and we find that most of them are unambiguously significant to infer the identity of traders. The third chapter investigates the relationship between the categories of market traders and three definitions of financial durations. We consider trade, price and volume durations, and we adopt a Log-ACD model where we include information on traders at the transaction level. As to trade durations, we observe an increase of the trading frequency when informed traders and the liquidity provider intensify their presence in the market. For price and volume durations, we find the same effect to depend on the state of the market activity. The fourth chapter proposes a strategy to express order aggressiveness in quantitative terms. We consider a simultaneous equation model to examine price and volume aggressiveness at Euronext Paris, and we analyse the impact of a wide set of order book variables on the price-quantity decision.

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.

Traditional risk factors and lifetime risk of acute coronary events

Objective: To investigate the association between the four traditional coronary heart disease (CHD) risk factors (hypertension, smoking, hypercholesterolemia, and diabetes) and outcomes of first ACS. Methods: Data were drawn from the ISACS Archives. The study participants consisted of 70953 patients with first ACS, but without prior CHD. Primary outcomes were patient’ age at hospital presentation and 30-day all-cause mortality. The risk ratios for mortality among subgroups were calculated using a balancing strategy by inverse probability weighting. Trends were evaluated by Pearson's correlation coefficient (r). Results: For fatal ACS (n=6097), exposure to at least one traditional CHD-risk factor ranged from 77.6% in women to 74.5% in men. The presence of all four CHD-risk factors significantly decreased the age at time of ACS event and death by nearly half a decade compared with the absence of any traditional risk factors in both women (from 67.1±12.0 to 61.9±10.3 years; r=-0.089, P<0.001) and men (from 62.8±12.2 to 58.9±9.9 years; r=-0.096, P<0.001). By contrast, there was an inverse association between the number of traditional CHD-risk factors and 30-day mortality. The mortality rates in women ranged from 7.7% with four traditional CHD-risk factors to 16.3% with no traditional risk factors (r=0.073, P<0.001). The corresponding rates in men were 4.8% and 11.5% (r=0.078, P<0.001), respectively. The risk ratios among individuals with at least one CHD-risk factors vs. those with no traditional risk factors were 0.72 (95%CI:0.65-0.79) in women and 0.64 (95%CI:0.59-0.70) in men. This association was consistent among patient subgroups managed with guideline-recommended therapeutic options. Conclusions: The vast majority of patients who die for ACS have traditional CHD-risk factor exposure. Patients with CHD-risk factors die much earlier in life, but they have a lower relative risk of 30-day mortality than those with no traditional CHD-risk factors, even in the context of equitable evidence‐based treatments after hospital admission.

Models and methods for damage and quantitative risk assessment of Natech scenarios

Natural events are a widely recognized hazard for industrial sites where relevant quantities of hazardous substances are handled, due to the possible generation of cascading events resulting in severe technological accidents (Natech scenarios). Natural events may damage storage and process equipment containing hazardous substances, that may be released leading to major accident scenarios called Natech events. The need to assess the risk associated with Natech scenarios is growing and methodologies were developed to allow the quantification of Natech risk, considering both point sources and linear sources as pipelines. A key element of these procedures is the use of vulnerability models providing an estimation of the damage probability of equipment or pipeline segment as a result of the impact of the natural event. Therefore, the first aim of the PhD project was to outline the state of the art of vulnerability models for equipment and pipelines subject to natural events such as floods, earthquakes, and wind. Moreover, the present PhD project also aimed at the development of new vulnerability models in order to fill some gaps in literature. In particular, a vulnerability model for vertical equipment subject to wind and to flood were developed. Finally, in order to improve the calculation of Natech risk for linear sources an original methodology was developed for Natech quantitative risk assessment methodology for pipelines subject to earthquakes. Overall, the results obtained are a step forward in the quantitative risk assessment of Natech accidents. The tools developed open the way to the inclusion of new equipment in the analysis of Natech events, and the methodology for the assessment of linear risk sources as pipelines provides an important tool for a more accurate and comprehensive assessment of Natech risk.

Developing a radiomic based machine learning model to identify patients with resected non-small-cell lung cancer at high risk of relapse

Background There is a wide variation of recurrence risk of Non-small-cell lung cancer (NSCLC) within the same Tumor Node Metastasis (TNM) stage, suggesting that other parameters are involved in determining this probability. Radiomics allows extraction of quantitative information from images that can be used for clinical purposes. The primary objective of this study is to develop a radiomic prognostic model that predicts a 3 year disease free-survival (DFS) of resected Early Stage (ES) NSCLC patients. Material and Methods 56 pre-surgery non contrast Computed Tomography (CT) scans were retrieved from the PACS of our institution and anonymized. Then they were automatically segmented with an open access deep learning pipeline and reviewed by an experienced radiologist to obtain 3D masks of the NSCLC. Images and masks underwent to resampling normalization and discretization. From the masks hundreds Radiomic Features (RF) were extracted using Py-Radiomics. Hence, RF were reduced to select the most representative features. The remaining RF were used in combination with Clinical parameters to build a DFS prediction model using Leave-one-out cross-validation (LOOCV) with Random Forest. Results and Conclusion A poor agreement between the radiologist and the automatic segmentation algorithm (DICE score of 0.37) was found. Therefore, another experienced radiologist manually segmented the lesions and only stable and reproducible RF were kept. 50 RF demonstrated a high correlation with the DFS but only one was confirmed when clinicopathological covariates were added: Busyness a Neighbouring Gray Tone Difference Matrix (HR 9.610). 16 clinical variables (which comprised TNM) were used to build the LOOCV model demonstrating a higher Area Under the Curve (AUC) when RF were included in the analysis (0.67 vs 0.60) but the difference was not statistically significant (p=0,5147).