Doubly multiplicative error models with long- and short-run components

We suggest the Doubly Multiplicative Error class of models (DMEM) for modeling and forecasting realized volatility, which combines two components accommodating long-run, respectively, short-run features in the data. Three such models are considered, the SPLINE-MEM which fits a spline to the slow-moving pattern of volatility, the Component-MEM which uses daily data for both components, and the MEM-MIDAS which exploits the logic of MIxed-DAta Sampling (MIDAS) methods. The parameters are estimated by the Generalized Method of Moments (GMM), for which we establish the theoretical properties and the equivalence with the Quasi Maximum Likelihood (QML) estimator under a Gamma assumption. The empirical application involves the S&P 500, NASDAQ, FTSE 100, DAX, Nikkei and Hang Seng indices: irrespective of the market, the DMEM’s generally outperform the HAR and other relevant GARCH-type models

Characteristics and patterns of care of endometrial cancer before and during COVID-19 pandemic

Objective: Coronavirus disease 2019 (COVID-19) outbreak has correlated with the disruption of screening activities and diagnostic assessments. Endometrial cancer (EC) is one of the most common gynecological malignancies and it is often detected at an early stage, because it frequently produces symptoms. Here, we aim to investigate the impact of COVID-19 outbreak on patterns of presentation and treatment of EC patients. Methods: This is a retrospective study involving 54 centers in Italy. We evaluated patterns of presentation and treatment of EC patients before (period 1: March 1, 2019 to February 29, 2020) and during (period 2: April 1, 2020 to March 31, 2021) the COVID-19 outbreak. Results: Medical records of 5,164 EC patients have been retrieved: 2,718 and 2,446 women treated in period 1 and period 2, respectively. Surgery was the mainstay of treatment in both periods (p=0.356). Nodal assessment was omitted in 689 (27.3%) and 484 (21.2%) patients treated in period 1 and 2, respectively (p<0.001). While, the prevalence of patients undergoing sentinel node mapping (with or without backup lymphadenectomy) has increased during the COVID-19 pandemic (46.7% in period 1 vs. 52.8% in period 2; p<0.001). Overall, 1,280 (50.4%) and 1,021 (44.7%) patients had no adjuvant therapy in period 1 and 2, respectively (p<0.001). Adjuvant therapy use has increased during COVID-19 pandemic (p<0.001). Conclusion: Our data suggest that the COVID-19 pandemic had a significant impact on the characteristics and patterns of care of EC patients. These findings highlight the need to implement healthcare services during the pandemic

Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

A Time-varying Mixture Memory Multiplicative Error Model

The dynamics of financial volatility shows a behavior characterized by alternating periods of turbulence and relative quiet. We suggest modelling it as a mixture memory model where time-varying mixing weights are a function of some forcing variable capable of sudden changes. In choosing a mixture approach we rely on previous evidence on the presence of a short– and a long–memory component in the observed series. We apply our model to the main Spanish stock index (IBEX) using the spread between the sovereign national and German bond rates as the forcing variable. The results show a good performance in sample, pointing to the fact that fixed weights may be a limitation to an accurate description of volatility behavior

Choosing the frequency of volatility components within the Double Asymmetric GARCH–MIDAS–X model

The Double Asymmetric GARCH–MIDAS (DAGM) model has the advantage of modelling volatility as the product of two components: a slow–moving term involving variables sampled at lower frequencies and a short–run part, each with an asymmetric behavior in volatility dynamics. Such a model is extended in three directions: first, by including a market volatility index as a daily lagged variable in the short–run component (the so-called “–X” term); second, by adding the same variable in the long–run component as variations of data aggregated at any desired frequency; third, by proposing a data-driven method to find the optimal number of lags to be included in the positive and negative parts of the long–run component. The resulting model, labelled as DAGM–X–2K, is extensively evaluated under several alternative configurations, producing satisfactory evidence when applied to the S&P 500 and NASDAQ indices. The out–of–sample results show that the “–X” addition significantly improves the performance, making the proposed DAGM–X–2K model enter the Model Confidence Set, even for large forecasting horizons (for 1 to 60 days)

Using mixed-frequency and realized measures in quantile regression

Quantile regression is an efficient tool when it comes to estimate popular measures of tail risk such as the conditional quantile Value at Risk. In this paper we exploit the availability of data at mixed frequency to build a volatility model for daily returns with low– (for macro– variables) and high–frequency (which may include an “–X” term related to realized volatility measures) components. The quality of the suggested quantile regression model, labeled MF– Q–ARCH–X, is assessed in a number of directions: we derive weak stationarity properties, we investigate its finite sample properties by means of a Monte Carlo exercise and we apply it on financial real data. VaR forecast performances are evaluated by backtesting and Model Confidence Set inclusion among competitors, showing that the MF–Q–ARCH–X has a consistently accurate forecasting capability

Double Asymmetric GARCH-MIDAS model: new insights and results

Il presente lavoro illustra una estensione del modello Double Asymmetric GARCH–MIDAS (DAGM), recentemente proposto. Nella modellizazione, oltre agli effetti asimmetrici nelle componenti di lungo e di breve periodo, `e stata introdotta una misura di volatilit`a realizzata giornaliera come variabile addizionale per la componente di breve periodo (la cosiddetta parte “–X”). Inoltre, `e stata sviluppata una procedura per le previsioni multi-step-ahead, valida per tutti i modelli GARCH– MIDAS (GM), anche con un termine aggiuntivo “–X”. La performance del DAGM– X, che generalizza il modello DAGM e il modello GM, `e stata valutata in riferimento all’indice S&P 500.The recently proposed Double Asymmetric GARCH-MIDAS (DAGM) model aims at separating the positive and negative macro variable variations within the long-run term and adds an asymmetric effect in the short-run component. In this work, the intent is to further extend the model in two main directions. A realized measure is included as a daily lagged variable in the short-run component (the socalled “–X” term) and a multi-step-ahead forecasting procedure is implemented for the class of GARCH–MIDAS (GM) models with the additional “–X” term. The extended DAGM-X model, which nests the DAGM and GM, is extensively evaluated under alternative configurations concerning the S&P 500 Index

Choosing between weekly and monthly volatility drivers within a Double Asymmetric GARCH-MIDAS model

Volatility in financial markets has both low and high–frequency components which determine its dynamic evolution. Previous modelling efforts in the GARCH context (e.g. the Spline–GARCH) were aimed at estimating the low frequency component as a smooth function of time around which short–term dynamics evolves. Alternatively, recent literature has introduced the possibility of considering data sampled at different frequencies to estimate the influence of macro–variables on volatility. In this paper, we extend a recently developed model, here labelled Double Asymmetric GARCH–MIDAS model, where a market volatility variable (in our context, VIX) is inserted as a daily lagged variable, and monthly variations represent an additional channel through which market volatility can influence individual stocks. We want to convey the idea that such variations (separately) affect the short– and long–run components, possibly having a separate impact according to their sign