HY-A-PARCH: A Stationary A-PARCH Model with Long Memory

The FI-A-PARCH process has been developed by Tse (1998) to model essential characteristics of financial market returns. However, due to the nonstationarity described by Níguez (2002) the process exhibits infinite conditional second moments and no statements about the autocovariance function can be derived. Thus, the new Hyperbolic A-PARCH model is considered, first introduced in Schoffer (2003). Subsequently the characteristics of this extension of the FI-A-PARCH process are inspected. It can be shown, that under certain parameter restrictions the intrinsic process as well as the process of conditional volatilities is stationary. Furthermore, for an asymmetric transformation of the conditional volatilities the presence of long memory is proven. Thus, the introduced model is able to reproduce the main characteristics of financial market returns such as volatility clustering, leptokurtosis, asymmetry and long memory

Ist die Hebelwirkung der Grund für Asymmetrie in ARCH- und GARCH-Modellen?

Moderne Varianten von ARCH- und GARCH-Modellen für Kapitalmarktdaten (z.B. EGARCH, GJR-GARCH, A-PARCH) berücksichtigen außer den zweiten Momenten auch die Beziehung zwischen ersten und zweiten Momenten (Asymmetrie). Eine Erklärung dieser Asymmetrie ist die Hebelwirkungshypothese. Die Hebelwirkung (leverage effect) ist jedoch nur für bestimmte Arten von Kapitalmarktdaten relevant und damit als Erklärung der Asymmetrie heranziehbar. Diese Arbeit weist empirisch nach, daß asymmetrische GARCH-Modelle in der Tat vor allem für Aktienrenditen und weniger für Änderungsraten von Wechselkursen den Erklärungsgehalt erhöhen

HY-A-PARCH: A stationary A-PARCH model with long memory

The FI-A-PARCH process has been developed by Tse (1998) to model essential characteristics of financial market returns. However, due to the nonstationarity described by Níguez (2002) the process exhibits infinite conditional second moments and no statements about the autocovariance function can be derived. Thus, the new Hyperbolic A-PARCH model is considered, first introduced in Schoffer (2003). Subsequently the characteristics of this extension of the FI-A-PARCH process are inspected. It can be shown, that under certain parameter restrictions the intrinsic process as well as the process of conditional volatilities is stationary. Furthermore, for an asymmetric transformation of the conditional volatilities the presence of long memory is proven. Thus, the introduced model is able to reproduce the main characteristics of financial market returns such as volatility clustering, leptokurtosis, asymmetry and long memory. --

Exploring relationships between in-hospital mortality and hospital case volume using random forest: results of a cohort study based on a nationwide sample of German hospitals, 2016–2018

Background Relationships between in-hospital mortality and case volume were investigated for various patient groups in many empirical studies with mixed results. Typically, those studies relied on (semi-)parametric statistical models like logistic regression. Those models impose strong assumptions on the functional form of the relationship between outcome and case volume. The aim of this study was to determine associations between in-hospital mortality and hospital case volume using random forest as a flexible, nonparametric machine learning method. Methods We analyzed a sample of 753,895 hospital cases with stroke, myocardial infarction, ventilation > 24 h, COPD, pneumonia, and colorectal cancer undergoing colorectal resection treated in 233 German hospitals over the period 2016–2018. We derived partial dependence functions from random forest estimates capturing the relationship between the patient-specific probability of in-hospital death and hospital case volume for each of the six considered patient groups. Results Across all patient groups, the smallest hospital volumes were consistently related to the highest predicted probabilities of in-hospital death. We found strong relationships between in-hospital mortality and hospital case volume for hospitals treating a (very) small number of cases. Slightly higher case volumes were associated with substantially lower mortality. The estimated relationships between in-hospital mortality and case volume were nonlinear and nonmonotonic. Conclusion Our analysis revealed strong relationships between in-hospital mortality and hospital case volume in hospitals treating a small number of cases. The nonlinearity and nonmonotonicity of the estimated relationships indicate that studies applying conventional statistical approaches like logistic regression should consider these relationships adequately

Effect of clinical peer review on mortality in patients ventilated for more than 24 hours: a cluster randomised controlled trial

Background Although clinical peer review is a well-established instrument for improving quality of care, clinical effectiveness is unclear. Methods In a pragmatic cluster randomised controlled trial, we randomly assigned 60 German Initiative Qualitätsmedizin member hospitals with the highest mortality rates in ventilated patients in 2016 to intervention and control groups. The primary outcome was hospital mortality rate in patients ventilated fore more than 24 hours. Clinical peer review was conducted in intervention group hospitals only. We assessed the impact of clinical peer review on mortality using a difference-in-difference approach by applying weighted least squares (WLS) regression to changes in age-adjusted and sex-adjusted standardised mortality ratios (SMRs) 1 year before and 1 year after treatment. Recommendations for improvement from clinical peer review and hospital survey data were used for impact and process analysis. Results We analysed 12 058 and 13 016 patients ventilated fore more than 24 hours in the intervention and control hospitals within the 1-year observation period. In-hospital mortality rates and SMRs were 40.6% and 1.23 in intervention group and 41.9% and 1.28 in control group hospitals in the preintervention period, respectively. The groups showed similar hospital (bed size, ownership) and patient (age, sex, mortality, main indications) characteristics. WLS regression did not yield a significant difference between intervention and control groups regarding changes in SMRs (estimate=0.04, 95% CI= −0.05 to 0.13, p=0.38). Mortality remained high in both groups (intervention: 41.8%, control: 42.1%). Impact and process analysis indicated few perceived outcome improvements or implemented process improvements following the introduction of clinical peer review. Conclusions This study did not provide evidence for reductions in mortality in patients ventilated for more than 24 hours due to clinical peer review. A stronger focus on identification of structures and care processes related to mortality is required to improve the effectiveness of clinical peer review

Spirituality in general practice

In a pluraIist and secular society, as well as in a medical world which is becoming increasingly evidence-based, making a case for consideration of spirituaIity in general practice may seem futile and irrelevant. Notwithstanding such an apparent paradoxical proposal, developments occurring in other specialties 1 as well as in general practice abroad reveal that it is high time that this theme is addressed academically and impIications appIied in local practice.peer-reviewe

Is treatment in certified cancer centers related to better survival in patients with pancreatic cancer?: Evidence from a large German cohort study

Background Treatment of cancer patients in certified cancer centers, that meet specific quality standards in term of structures and procedures of medical care, is a national treatment goal in Germany. However, convincing evidence that treatment in certified cancer centers is associated with better outcomes in patients with pancreatic cancer is still missing. Methods We used patient-specific information (demographic characteristics, diagnoses, treatments) from German statutory health insurance data covering the period 2009–2017 and hospital characteristics from the German Standardized Quality Reports. We investigated differences in survival between patients treated in hospitals with and without pancreatic cancer center certification by the German Cancer Society (GCS) using the Kaplan–Meier estimator and Cox regression with shared frailty. Results The final sample included 45,318 patients with pancreatic cancer treated in 1,051 hospitals (96 GCS-certified, 955 not GCS-certified). 5,426 (12.0%) of the patients were treated in GCS-certified pancreatic cancer centers. Patients treated in certified and non-certified hospitals had similar distributions of age, sex, and comorbidities. Median survival was 8.0 months in GCS-certified pancreatic cancer centers and 4.4 months in non-certified hospitals. Cox regression adjusting for multiple patient and hospital characteristics yielded a significantly lower hazard of long-term, all-cause mortality in patients treated in GCS-certified pancreatic centers (Hazard ratio = 0.89; 95%-CI = 0.85–0.93). This result remained robust in multiple sensitivity analyses, including stratified estimations for subgroups of patients and hospitals. Conclusion This robust observational evidence suggests that patients with pancreatic cancer benefit from treatment in a certified cancer center in terms of survival. Therefore, the certification of hospitals appears to be a powerful strategy to improve patient outcomes in pancreatic cancer care

Health related Quality of Life over time in German sarcoma patients. An analysis of associated factors - results of the PROSa study

Introduction Sarcomas are rare cancers and very heterogeneous in their location, histological subtype, and treatment. Health-Related Quality of Life (HRQoL) of sarcoma patients has rarely been investigated in longitudinal studies. Methods Here, we assessed adult sarcoma patients and survivors between September 2017 and February 2020, and followed-up for one year in 39 study centers in Germany. Follow-up time points were 6 (t1) and 12 months (t2) after inclusion. We used a standardized, validated questionnaire (the European Organisation for Research and Treatment of Cancer Quality of Life Core Instrument (EORTC QLQ-C30) and explored predictors of HRQoL in two populations (all patients (Analysis 1), patients in ongoing complete remission (Analysis 2)) using generalized linear mixed models. Results In total we included up to 1111 patients at baseline (915 at t1, and 847 at t2), thereof 387 participants were in complete remission at baseline (334 at t1, and 200 at t2). When analyzing all patients, HRQoL differed with regard to tumor locations: patients with sarcoma in lower extremities reported lower HRQoL values than patients with sarcomas in the upper extremities. Treatment which included radiotherapy and/or systemic therapy was associated with lower HRQoL. For patients in complete remission, smoking was associated with worse HRQoL-outcomes. In both analyses, bone sarcomas were associated with the worst HRQoL values. Being female, in the age group 55-&lt;65 years, having lower socioeconomic status, and comorbidities were all associated with a lower HRQoL, in both analyses. Discussion HRQoL increased partially over time since treatment and with sporting activities. HRQoL improved with time since treatment, although not in all domains, and was associated with lifestyle and socioeconomic factors. Bone sarcomas were the most affected subgroup. Methods to preserve and improve HRQoL should be developed for sarcoma patients. </sec

Modellierung von Kapitalmarktrenditen mittels asymmetrischer GARCH-Modelle

In der vorliegenden Arbeit wird die Modellierung von Kapitalmarktrenditen mittels asymmetrischer GARCH-Prozesse betrachtet. Insbesondere werden die Eigenschaften des A-PARCH-Modells sowie seiner Erweiterungen untersucht. Dieses Modell ermöglicht nicht allein die Modellierung von Volatilitätsschwankungen und Hochgipfligkeit wie das GARCH-Modell. Es ist ebenso geeignet, das Charakteristikum der Asymmetrie nachzubilden. Ferner können bedingte Volatilitäten als nichtganzzahlige Potenz vom Absolutbetrag der Beobachtungen beschrieben werden. Langes Gedächtnis in den bedingten Volatilitäten kann durch einen A-PARCH-Prozeß jedoch nicht modelliert werden, da ihre Autokorrelationsfunktion exponentiell und nicht hyperbolisch abfällt.Zunächst wird ein Vergleich von Modellanpassungen für Renditen von Aktienkursdaten durchgeführt. Dabei weist der A-PARCH-Prozeß mit bedingter t-Verteilung gegenüber anderen symmetrischen und asymmetrischen GARCH-Prozessen die kleinsten Werte für das Informationskriterium SBC (Schwarz-Bayes-Informationskriterium) sowie für das Maß für die Vorhersagegüte MSPE (mittlerer quadratischer Prognosefehler) auf. Er wird somit bei der Modellierung von Strukturen dieser Kapitalmarktdaten bevorzugt.Weiter wird die Hebelwirkungshypothese als mögliche Ursache für Asymmetrie in Kapitalmarktdaten mittels symmetrischer und asymmetrischer GARCH-Modelle untersucht. Unter anderem wird dabei für Modellanpassungen an Renditen von Aktien- und Wechselkursdaten sowie Edelmetallpreisen und Zinssätzen der Wert für SBC betrachtet. Die jeweilige Bevorzugung von symmetrischen bzw. asymmetrischen Modellen für die Anpassung an die beschriebenen Kapitalmarktrenditen stützt die Plausibilität der Hebelwirkungshypothese. Obwohl es auch weiterhin keine endgültige Aussage über die Gültigkeit dieser Hypothese gibt, sollte ihr bei der Modellauswahl, d.h. symmetrischer gegenüber asymmetrischem Ansatz, Rechnung getragen werden. Asymmetrische Modelle sollten demnach nur für Daten verwendet werden, für die das Zugrundeliegen einer Hebelwirkung sinnvoll ist.Um langes Gedächtnis als Eigenschaft von Kapitalmarktrenditen nachbilden zu können, gibt es verschiedene Erweiterungen des ursprünglichen GARCH-Ansatzes. In der vorliegenden Arbeit wird das FI-A-PARCH-Modell sowie sein Spezialfall FIGARCH betrachtet. Diese ergeben sich aus A-PARCH- bzw. GARCH-Modellen durch geeignetes Hinzufügen des fraktionalen Differenzenoperators (1 - B)d. Zur praktischen Umsetzung dieser Modelle ist jedoch eine Abschätzung dieses Operators mittels binomischer Reihe notwendig. Über die in dieser Abschätzung zu verwendende Anzahl von Beobachtungen gibt es jedoch bisher kaum konkrete Aussagen. Daher werden Parameterschätzungen in FI-A-PARCH-Modellen in Abhängigkeit von dieser Beobachtungszahl betrachtet. Es kann festgestellt werden, daß sich die Werte der Schätzungen für die vorliegenden Modellanpassungen etwa ab einem Wert von 300 Beobachtungen stabilisieren. Basierend auf diesem Ergebnis wird empfohlen, für ähnliche Probleme eine Anzahl von ca. 300 Beobachtungen für die Abschätzung von (1 - B)d zu verwenden.Obwohl der FI-A-PARCH-Prozeß zur Modellierung von langem Gedächtnis entwickelt wurde, besitzen seine bedingten Volatilitäten nicht die Eigenschaft langen Gedächtnisses in dem Sinne, daß die Autokorrelationsfunktion hyperbolisch fällt. Da die zweiten Momente analog zu denen des FIGARCH-Prozesses unendlich sind, existiert die betrachtete Autokorrelationsfunktion nicht. In einem weiteren Vergleich von Modellanpassungen nach MSPE wird der FI-A-PARCH-Prozeß mit bedingter t-Verteilung zwar nicht in jedem Fall bevorzugt, er erzielt jedoch jeweils die kleinsten Werte für SBC und kann somit als sinnvolle Ergänzung der zuvor vorgestellten Modelle angesehen werden.Ein Modell, welches die Nachbildung von langem Gedächtnis in den bedingten Volatilitäten ermöglicht, ist das Hyperbolische GARCH-Modell. Jedoch kann damit keine Asymmetrie beschrieben werden. Analog zur Herleitung des HYGARCH-Modells wird daher in der vorliegenden Arbeit das A-PARCH-Modell zum Hyperbolischen A-PARCH-Modell erweitert. Eigenschaften dieses Modells können unter anderem mittels Volterra-Reihenentwicklung von Asymmetrischen Power-GARCH-Modellen hergeleitet werden. Die Existenz der zweiten Momente wird gezeigt sowie die dazu notwendigen Bedingungen hergeleitet. Weiterhin kann das Vorliegen von langem Gedächtnis in der Transformation {(|yt| - yt)d} des Hyperbolischen A-PARCH-Prozesses {yt} nachgewiesen werden. Für den Prozeß {|yt|d}, welcher die bedingten Volatilitäten repräsentiert, kann daraus zunächst jedoch keine Aussage über die Autokorrelationsstruktur abgeleitet werden. Das HY-A-PARCH-Modell ermöglicht also die Beschreibung der Charakteristika Volatilitätsschwankungen, Hochgipfligkeit und Asymmetrie sowie das Vorliegen von langem Gedächtnis zumindest für eine Transformation der bedingten Volatilitäten. Der Nachweis von langem Gedächtnis in der Reihe {|yt|d} von HY-A-PARCH-Prozessen bleibt somit zunächst der künftigen Forschung vorbehalten. Als Ansatz zur Lösung dieses Problems wird die Methode der Appell-Polynome vorgeschlagen.The available thesis contemplates the modeling of returns of financial markets using asymmetric GARCH processes. In particular the characteristics of the A-PARCH model as well as its extensions are examined. This model not only allows for modeling volatility clustering and leptocurtosis like the GARCH model. It is suitable to reproduce the characteristic of asymmetry as well. Furthermore, conditional volatilities can be described as none-integer-numbered power by the absolute value of the observations. However, long memory in the conditional volatilities cannot be modeled by an A-PARCH process since their autocorrelation function decays exponentially but not hyperbolically.First a comparison of model fits for returns of asset prices is accomplished. In relation to other symmetrical and asymmetrical GARCH processes the A-PARCH process with conditional t-distribution exhibits the smallest values for SBC (Schwarz-Bayes information criterion) as well as for the measure for the forecast quality MSPE (mean square prediction error). Thus, it is preferred by modeling structures of these capital market data.Moreover, the leverage hypothesis is examined as a possible cause for asymmetry in financial market data by symmetrical and asymmetrical GARCH models. Thereby, the value for SBC is inspected for model fits of asset returns and exchange rates as well as for precious metal prices and interest rates. The respective preference of symmetrical or asymmetrical models for the described data supports the plausibility of the leverage hypothesis. This effect should be accounted for model selection, i.e. for symmetrical vs. asymmetrical approach, even though there is still no final statement about the validity of this hypothesis. Therefore, asymmetrical models should be used only for data, for which it is reasonable to imply a leverage effect.In order to be able to reproduce long memory as characteristic of financial market returns, there are several extensions of the original GARCH approach. In the available work the FI-A-PARCH model as well as its special case FIGARCH are considered. These results by suitable adding of the fractional difference operator (1 - B)d from A-PARCH and GARCH models, respectevly. However, an approximation of this operator is necessary for practical implementation of these models using binomial series. But there are hardly concrete statements about the number of observations, which have to be used in this approximation, up to now. Therefore, the parameter estimations in FI-A-PARCH models are examined as a function of this observation number. It can be observed that the values of the estimations for these model fits stabilize from a value of approx. 300 observations. Based on this result, it is recommended to use a number of 300 observations for the estimation of (1 - B)d at similar problems.Although the FI-A-PARCH process was developed to model long term dependence, its conditional volatilities does not possess the characteristic of long memory in the sense that the autocorrelation function decays hyperbolically. Since the second moments are infinite similarly to those of the FIGARCH process, the appropriate autocorrelation function does not exist. In a further comparison of model fits using MSPE the FI-A-PARCH process with conditional t-distribution is not preferred in every case. However, the smallest value for SBC were obtained using this model. Thus it can be regarded as a reasonable addition of the models presented before.A model, which allows for reproduction of long memory in the conditional volatilities, is the Hyperbolic GARCH model. However, it cannot describe asymmetry. Thus, in this work the A-PARCH model is extended to the Hyperbolic A-PARCH model analogous to the derivation of the HYGARCH model. Characteristics of this model can be derived among other things using Volterra series expansion with Asymmetric Power GARCH models. The existence of the second moments is shown as well as the appropriate necessary condition is derived. Further the presence of long memory in the transformation {(|yt| - yt)d} of the hyperbolic A-PARCH-process {yt} is proved. However, for the process {|yt|d}, which represents the conditional volatilities, up to now no conclusion can be derived about the autocorrelation structure. Thus, the HY-A-PARCH model allows for description of the characteristics for volatility clustering, leptocurtosis and asymmetry as well as the presence of long memory at least for a transformation of the conditional volatilities. The proof of long memory in the series {|yt|d} of HY-A-PARCH processes at first remains for future research. The method of Appell polynomials is suggested as approach for solution of this problem