719 research outputs found
Size and Value Anomalies under Regime Shifts
No full textThis paper finds strong evidence of time-variations in the joint distribution of returns on a stock market portfolio and portfolios tracking size- and value effects. Mean returns, volatilities and correlations between these equity portfolios are found to be driven by underlying regimes that introduce short-run market timing opportunities for investors. The magnitude of the premia on the size and value portfolios and their hedging properties are found to vary across regimes. Regimes are shown to have a large impact both on the optimal asset allocation--especially under rebalancing--and on investors' utility. Regimes also have a considerable impact on hedging demands, which are positive when the investor starts from more favorable regimes and negative when starting from bad states. Recursive out-of-sample forecasting experiments show that portfolio strategies based on models that account for regimes dominate single-state benchmarks. Copyright The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected], Oxford University Press.
International asset allocation under regime switching, skew, and kurtosis preferences
No full textThis paper investigates the international asset allocation effects of time-variations in higher-order moments of stock returns such as skewness and kurtosis. In the context of a four-moment International Capital Asset Pricing Model (ICAPM) specification that relates stock returns in five regions to returns on a global market portfolio and allows for time-varying prices of covariance, co-skewness, and co-kurtosis risk, we find evidence of distinct bull and bear regimes. Ignoring such regimes, an unhedged US investor's optimal portfolio is strongly diversified internationally. The presence of regimes in the return distribution leads to a substantial increase in the investor's optimal holdings of US stocks, as does the introduction of skewness and kurtosis preferences. The Author 2008. Published by Oxford University Press on behalf of the Society for Financial Studies. All rights reserved. For permissions, please e-mail: [email protected]., Oxford University Press.
Least angle regression for time series forecasting with many predictors.
No full textLeast Angle Regression(LARS)is a variable selection method with proven performance for cross-sectional data. In this paper, it is extended to time series forecasting with many predictors. The new method builds parsimonious forecast models,taking the time series dynamics into account. It is a exible method that allows for ranking the different predictors according to their predictive content. The time series LARS shows good forecast performance, as illustrated in a simulation study and two real data applications, where it is compared with the standard LARS algorithm and forecasting using diffusion indices.macro-econometrics; model selection; penalized regression; variable ranking;
Quantifying the potential causes of Neanderthal extinction: Abrupt climate change versus competition and interbreeding
No full text© 2020 The Author(s). Anatomically Modern Humans are the sole survivor of a group of hominins that inhabited our planet during the last ice age and that included, among others, Homo neanderthalensis, Homo denisova, and Homo erectus. Whether previous hominin extinctions were triggered by external factors, such as abrupt climate change, volcanic eruptions or whether competition and interbreeding played major roles in their demise still remains unresolved. Here I present a spatially resolved numerical hominin dispersal model (HDM) with empirically constrained key parameters that simulates the migration and interaction of Anatomically Modern Humans and Neanderthals in the rapidly varying climatic environment of the last ice age. The model simulations document that rapid temperature and vegetation changes associated with Dansgaard-Oeschger events were not major drivers of global Neanderthal extinction between 50 and 35 thousand years ago, but played important roles regionally, in particular over northern Europe. According to a series of parameter sensitivity experiments conducted with the HDM, a realistic extinction of the Neanderthal population can only be simulated when Homo sapiens is chosen to be considerably more effective in exploiting scarce glacial food resources as compared to Neanderthals11sci
Establishment and application of nonlinear methods to characterize pathological neuronal activity
No full textMit etwa 100 Milliarden Neuronen und bis zu 1014 Verbindungen gehört das menschliche Gehirn zu den komplexesten bekannten Strukturen. Komplexe Systeme weisen häufig eine intrinsische Nichtlinearität in ihrem Verhalten auf. Das bedeutet, dass der Input in das System in keinem einfachen (linearen) Verhältnis zum Output steht. Diese Nichtlinearität ist auf allen Betrachtungsebenen des Gehirns zu finden, beginnend mit der Aktivierung einzelner Nervenzellen nach dem „Alles oder Nichts“-Prinzip.
Klassischerweise werden zur Aufklärung pathologischer, neurophysiologischer Veränderungen bis heute vornehmlich lineare Ansätze und Methoden gewählt. Trotz ihres Potentials halten nichtlineare Methoden nur langsam Einzug in neurobiologische Forschung. Der vorliegenden Dissertation liegen drei Studien zugrunde deren übergeordnetes Ziel die Etablierung und Anwendung nichtlinearer Methoden für neurobiologische Fragestellungen war.
Im Rahmen der ersten Studie habe ich mit NoLiTiA (Nonlinear Time Series Analysis) eine umfassende, frei verfügbare Matlab-Toolbox entwickelt, um neurobiologische Fragestellungen mittels Methoden der nichtlinearen Zeitreihenanalyse zu beantworten. Die etwa 50 implementierten Routinen und Methoden sind drei großen thematischen Schwerpunkten nichtlinearer Analyse entlehnt: der nichtlinearen Dynamik, der Informationstheorie, sowie der Rekurrenzanalyse. Neben klassischen Methoden aus der Chaosforschung sind auch neueste Verfahren aus der Informationstheorie, wie die jüngst etablierte „Active Information Storage“, implementiert. Gleiches gilt für das relativ junge Feld der Rekurrenzanalyse, dessen methodische Bandbreite ich mit konsequenten Weiterentwicklungen, wie dem Rekurrenz-Perioden-Spektrum, unter Betrachtung neurobiologischer Fragestellungen, ergänzt habe. Eine große Herausforderung bei der Etablierung komplexer Methoden stellt die Zugänglichkeit in der Anwendung dar. Um gleichermaßen programmiererfahrene und unerfahrene Anwender anzusprechen, bietet die Toolbox drei verschiedene Interfaces für die Bedienung: eine intuitive graphische Nutzeroberfläche, einen Batch-Editor für die Bearbeitung umfangreicher Datensätze, sowie die Möglichkeit, eigene anwendungsangepasste Skripte zu programmieren. Mittels einer separaten Nutzeroberfläche lassen sich die gewonnenen Ergebnisse graphisch darstellen. Unterstützt wird hier unter anderem die topographische Darstellung von elektroenzephalographischen Daten. Die Grundfunktionalität der basalen Funktionen wird anhand von simulierten Daten validiert und beispielhaft anhand eines elektromyographischen Datensatzes eines Parkinson-Patienten demonstriert.
Wie jüngste Studien zeigen konnten, kann die, für elektrophysiologische Studien typische, Datenvorverarbeitung zur Bereinigung von Störgrößen erheblichen Einfluss auf bestimmte lineare Methoden aus dem Bereich der Kausalitätsanalysen haben. Die Transfer Entropie stellt eine generalisierte, modellfreie Interpretation der klassischen Wiener-Granger-Kausalität aus dem Bereich der Informationstheorie dar und kann unter anderem zur Analyse direktionalen Informationsflusses zwischen unterschiedlichen Hirnarealen genutzt werden. Das Ziel der zweiten Studie war es, den Einfluss unterschiedlicher Vorverarbeitungspraktiken auf die Schätzung der Transfer Entropie zu ergründen. Hierzu wurden im Rahmen einer Simulationsstudie verschiedene digitale Filter- und „Downsampling“ (Heruntertaktungs)-Optionen mittels eines etablierten linearen und zwei eigens designten nichtlinearen Kopplungsmodellen getestet. Unter Verwendung sukzessiv niedriger Tiefpass-Filter-Frequenzen konnten bei den nichtlinearen Kopplungsmodellen bis zu 72 % falsch negative direkte Verbindungen und bis zu 26 % falsch positive Verbindungen detektiert werden. Beim linearen Modell konnte unter gleichen Bedingungen lediglich ein Anstieg der falsch negativen indirekten Verbindungen beobachtet werden (bis zu 86 %). Die Anwendung eines Hoch-Pass-Filters hatte keinen Einfluss auf die Schätzung der Transfer Entropie. „Downsampling“ führte mit 67 % bis 100 % falsch negativen direkten Verbindungen zu massiven Fehlschätzungen. Insgesamt sollten übliche elektrophysiologische Vorverarbeitungspraktiken nur unter größtem Vorbehalt bei Schätzung der Transfer Entropie angewendet werden.
Im Rahmen der dritten Studie sollte der Informationsgehalt von intraoperativ gemessener elektrophysiologischer Aktivität während einer Ruhe- und einer Haltekondition im subthalamischen Areal von Parkinson-Patienten mit der klinischen Symptomatik korreliert werden. Hierbei zeigte sich in Ruhe eine signifikant positive Korrelation von klinischer Symptomatik, sowohl mit dem Informationsgehalt des Nucleus subthalamicus als auch der Zona incerta. In einer zweiten Analyse sollte die Informationsspeicherkapazität des Nucleus subthalamicus quantifiziert und mit der Zona incerta verglichen werden. Unter Verwendung der „Active Information Storage“ konnte in der Zona incerta eine signifikant größere Speicherkapazität als im Nucleus subthalamicus festgestellt werden. Dieses Ergebnis deckt sich mit der Beobachtung, dass nur relativ hohe Stimulationsfrequenzen im Nucleus subthalamicus therapeutisch wirksam sind. Schließlich sollte der Informationstransfer zwischen subthalamischen Areal und Oberarmmuskeln analysiert werden. Zunächst wurden insgesamt mehr bidirektionale Verbindungen zwischen Nucleus subthalamicus und Muskeln detektiert als zwischen Zona incerta und Muskeln. Allerdings zeigte sich nur bei den bidirektionalen Verbindungen zwischen Zona incerta und Muskeln eine bewegungsabhängige Modulation. Hierbei konnte im Gegensatz zum Nucleus subthalamicus ein Anstieg der Kopplungen während der Haltebedingung beobachtet werden. Die Ergebnisse werden unter Einbeziehung neuester Studienergebnisse zur Wirksamkeit der Tiefen Hirnstimulation in der Zona incerta diskutiert.
Zusammenfassend konnte ich mit der ersten Studie eine umfangreiche, leicht zugängliche Software-Bibliothek entwickeln, mit der zweiten Studie die notwendigen Voraussetzungen für die Anwendung bestimmter nichtlinearer Methoden in der Neurobiologie schaffen und mit der dritten Studie unter Beantwortung einer relevanten neurobiologischen Fragestellung die Anwendung und Anwendbarkeit neuester nichtlinearer Methoden demonstrieren.With approximately 100 billion neurons and 1014 synaptic connections, the human brain is among the most complex structures in nature. Complex systems are often characterized by an intrinsic nonlinearity leading to seemingly random behavior. This means that the input to the system does not relate to its output in a simple linear or proportional way. This nonlinearity can be observed on every scale of observation beginning from the generation of single cell action potentials according to the all-or-nothing principle up to the organization of functional related brain areas to hubs. Despite the brain’s high complexity, humans are normally able to respond to internal and external cues in a reproducible and adequate manner. In the pathological state however, many neurological diseases, like the idiopathic Parkinson’s disease lead to an inability to adequately respond to internal cues, which might lead to a wide array of symptoms like e. g. slowing of movements.
Given their broad distribution, most commonly linear methods are chosen in order to study pathological neurophysiological changes. Among classic linear methods are simple (auto)correlations, Fourier-based frequency analyses or model-based causality analyses. However, these linear methods only capture a small proportion of the brain’s temporal dynamics as they are insensitive to nonlinear behavior. Despite their great potential, nonlinear methods are just slowly adopted in the neuroscientific community. Possible reasons for this are their high computational demands, a low availability of easy to use analysis tools and few studies on caveats, stability of results and interactions with typical neurobiological preprocessing routines.
The present dissertation is based on three studies, which overall aim was to establish a broad array of nonlinear methods in neuroscientific research.
With the first study, I developed NoLiTiA, a free, easy to use Matlab-toolbox with a wide array of nonlinear methods and routines to analyze neuroscientific datasets. The approximately 50 nonlinear methods and routines originate mostly from three distinct topics: nonlinear dynamics, information theory and recurrence analysis. Beside classical approaches from chaos theory like correlation dimension for complexity analysis or estimation of Lyapunov exponents for stability analysis, I also implemented very recent methods like active information storage from information theory and even developed new methods like the time resolved recurrence period spectrum with neurobiological research questions in mind. A big challenge in establishing new complex methods is making them easy and intuitive to use for programming unexperienced researchers. In order to be accessible for programming experienced and unexperienced researchers, the toolbox offers three analysis pathways: an intuitive graphical user interface, a batch-editor for large datasets and the option to develop individualized custom-made scripts. An additional interface offers the possibility to plot analysis results and even supports topographical representations of electroencephalographic data. All basic functions are validated using simulated data with known ground truths and exemplified using electromyographic data of a single Parkinson’s disease patient. The toolbox and all its functions were completely designed and implemented by me. The example dataset of one Parkinson’s disease patient was kindly provided by Prof. Dr. Esther Florin.
As recent studies could demonstrate, typical electrophysiological preprocessing routines can have tremendous effects on the estimation of certain linear, model-based, directional coupling methods based on Wiener-Granger causality. Transfer entropy is a model-free generalization of classic Wiener-Granger causality derived from information theory. Due to its nonparametric character, it can make inferences on linear as well as on nonlinear systems and is thus especially suited to study directional information flow between brain regions. The aim of the second study was to analyze the influence of different electrophysiological preprocessing routines, including different digital filter and downsampling option, on the estimation of Transfer entropy. We tested the different preprocessing options in a simulation framework using one established linear and two self-designed nonlinear coupling models. For successively lower low-pass filter frequencies we observed up to 72 % false negative direct connections and up to 26 % false positive connections when analyzing the nonlinear models. When conducting the same analysis using the linear model, only up to 86 % false negative indirect connections could be detected. Using a high-pass filter had no influence on the estimation of transfer entropy. Downsampling should be avoided if the sampling factor is greater than the assumed interaction delay of the information transmission. In our simulations we observed 67 % up to 100 % false negative direct connections. In conclusion, preprocessing should be avoided when estimating Transfer entropy or should at least be performed with great care as it may lead to a high number of spurious or missed connections. The study was designed in cooperation with Prof. Dr. Esther Florin, Prof. Dr. Lars Timmermann and Dr. Michael von Papen. All analyses and implementations of established and new models were done by me.
Incorporating the results from study 1 and 2, the aim of the third study was to characterize information processing in the basal ganglia of Parkinson’s disease patients. The idiopathic Parkinson’s disease is among the most common neurodegenerative diseases. Besides medical treatment, deep brain stimulation is an efficient and well-established treatment option. Despite its wide-spread application not much is known regarding its mechanism of action. A recent theory suggests that the electrical stimulation overrides pathological brain activity with a more physiological signal. A direct implication of this informational lesion hypothesis is a possible correlation of pathological neural information content of the basal ganglia with clinical symptoms, which has already been validated for the globus pallidus internus in animal studies. Thus, for the third study the aim was to correlate information content of intraoperatively recorded electrophysiological activity from the subthalamic area of Parkinson’s disease patients, during a rest and a hold condition, with their clinical symptoms. In accordance with animal studies I could demonstrate a significant positive correlation between information content and clinical symptoms at rest, both in the zona incerta as well as in the subthalamic nucleus. In a second analysis I studied the information storage capabilities of the subthalamic area, using active information storage implemented in NoLiTiA. I could demonstrate a significant larger processing memory in the zona incerta than in the subthalamic nucleus, which might explain the need of clinical effective stimulation being high frequency. Finally, I analysed the information transfer between the subthalamic area and three forearm muscles during the rest and hold condition. I detected more bidirectional couplings between the subthalamic nucleus and muscles than between the zona incerta and muscles. However, in contrast to the subthalamic nucleus, I observed a movement dependent increase of couplings from the zona incerta to muscles. These results are subsequently discussed with respect to recent studies claiming that the zona incerta might be a better target for deep brain stimulation than the subthalamic nucleus. The study was designed in cooperation with Prof. Dr. Esther Florin, Prof. Dr. Lars Timmermann and Dr. Michael von Papen. All analyses and implementations of established and new methods were done by me. Intraoperative data was recorded by Prof. Dr. Esther Florin at the university hospitals Düsseldorf and Cologne.
In conclusion, I developed an intuitive, open-source toolbox for nonlinear time series analysis with the aim of increasing the visibility, availability and accessibility of nonlinear methods in the neuroscientific community. With the second study I demonstrated the effects of using classic electrophysiological preprocessing routines on nonlinear coupling techniques, thus providing guidance on how to use these techniques. Finally, with the third study exemplified usage of nonlinear methods by validating a recent relevant hypothesis on the relation of pathological information processing in the basal ganglia and clinical symptoms of Parkinson’s disease patients
Challenges in Translating Beauvoir
No full textAlthough the translator is given nothing but words and produces nothing but words, the translation process itself truly involves “navigating in a world of pure thought,” independent of words. It challenges the translator to not only study the author’s words, but internalize the very meaning behind those words. This fascinating process becomes all the more challenging when translating an author as thought-provoking and influential as Simone de Beauvoir. Translating Beauvoir presents many challenges due to the complexity of her thought, the limits and ambiguities inherent in any language, the connotations of common words, and the differences in linguistic norms between her era and ours. Timmermann discusses challenges such as translating Beauvoir’s use of “la femme” and “féminine” without introducing essentialist connotations and translating Beauvoir’s use of the masculine neutral without modernizing Beauvoir’s original.</p
Fokker–Planck dynamics of the El Niño-Southern Oscillation
No full text© 2020, The Author(s). The asymmetric nature of the El Niño-Southern Oscillation (ENSO) is explored by using a probabilistic model (PROM) for ENSO. Based on a Fokker–Planck Equation (FPE), PROM describes the dynamics of a nonlinear stochastic ENSO recharge oscillator model for eastern equatorial Pacific temperature anomalies and equatorial Pacific basin-averaged thermocline depth changes. Eigen analyses of PROM provide new insights into the stationary and oscillatory solutions of the stochastic dynamical system. The first probabilistic eigenmode represents a stationary mode, which exhibits the asymmetric features of ENSO, in case deterministic nonlinearities or multiplicative noises are included. The second mode is linked to the oscillatory nature of ENSO and represents a cyclic asymmetric probability distribution, which emerges from the key dynamical processes. Other eigenmodes are associated with the temporal evolution of higher order statistical moments of the ENSO system. The model solutions demonstrate that the deterministic nonlinearity plays a stronger role in establishing the observed asymmetry of ENSO as compared to the multiplicative stochastic part.11Nsciescopu
(Un)predictability of strong El Nino events
No full textThe El Niño-Southern Oscillation (ENSO) is a mode of interannual variability in the coupled equa- torial Pacific coupled atmosphere/ocean system. El Niño describes a state in which sea surface temper- atures in the eastern Pacific increase and upwelling of colder, deep waters diminishes. El Niño events typically peak in boreal winter, but their strength varies irregularly on decadal time scales. There were exceptionally strong El Niño events in 1982–83, 1997–98 and 2015–16 that affected weather on a global scale. Widely publicized forecasts in 2014 predicted that the 2015–16 event would occur a year earlier. Predicting the strength of El Niño is a matter of practical concern due to its effects on hydroclimate and agriculture around the world. This paper discusses the frequency and regularity of strong El Niño events in the context of chaotic dynamical systems. We discover a mechanism that limits their predictability in a conceptual “recharge oscillator” model of ENSO. Weak seasonal forcing or noise in this model can induce irregular switching between an oscillatory state that has strong El Niño events and a chaotic state that lacks strong events, In this regime, the timing of strong El Niño events on decadal time scales is unpredictable. (c) The Author(s) 2017. Published by Oxfold University Press
Drivers of future seasonal cycle changes in oceanic pCO2
No full textRecent observation-based results show that the seasonal amplitude of surface ocean partial pressure of CO2 (pCO2) has been increasing on average at a rate of 2–3µatm per decade (Landschützer et al. 2018). Future increases in pCO2 seasonality are expected, as marine CO2 concentration ([CO2]) will increase in response to increasing anthropogenic carbon emissions (McNeil and Sasse 2016). Here we use seven different global coupled atmosphere–ocean–carbon cycle–ecosystem model simulations conducted as part of the Coupled Model Intercomparison Project Phase 5 (CMIP5) to study future projections of the pCO2 annual cycle amplitude and to elucidate the causes of its amplification. We find that for the RCP8.5 emission scenario the seasonal amplitude (climatological maximum minus minimum) of upper ocean pCO2 will increase by a factor of 1.5 to 3 over the next 60–80 years. To understand the drivers and mechanisms that control the pCO2 seasonal amplification we develop a complete analytical Taylor expansion of pCO2 seasonality in terms of its four drivers: dissolved inorganic carbon (DIC), total alkalinity (TA), temperature (T), and salinity (S). Using this linear approximation we show that the DIC and T terms are the dominant contributors to the total change in pCO2 seasonality. To first order, their future intensification can be traced back to a doubling of the annual mean pCO2, which enhances DIC and alters the ocean carbonate chemistry. Regional differences in the projected seasonal cycle amplitude are generated by spatially varying sensitivity terms. The subtropical and equatorial regions (40°S–40°N) will experience a ≈ 30–80µatm increase in seasonal cycle amplitude almost exclusively due to a larger background CO2 concentration that amplifies the T seasonal effect on solubility. This mechanism is further reinforced by an overall increase in the seasonal cycle of T as a result of stronger ocean stratification and a projected shoaling of mean mixed layer depths. The Southern Ocean will experience a seasonal cycle amplification of ≈ 90–120µatm in response to the mean pCO2-driven change in the mean DIC contribution and to a lesser extent to the T contribution. However, a decrease in the DIC seasonal cycle amplitude somewhat counteracts this regional amplification mechanism.© Author(s) 2018
Spurious North Tropical Atlantic precursors to El Niño
No full text© 2021, The Author(s).The El Niño-Southern Oscillation (ENSO), the primary driver of year-to-year global climate variability, is known to influence the North Tropical Atlantic (NTA) sea surface temperature (SST), especially during boreal spring season. Focusing on statistical lead-lag relationships, previous studies have proposed that interannual NTA SST variability can also feed back on ENSO in a predictable manner. However, these studies did not properly account for ENSO’s autocorrelation and the fact that the SST in the Atlantic and Pacific, as well as their interaction are seasonally modulated. This can lead to misinterpretations of causality and the spurious identification of Atlantic precursors for ENSO. Revisiting this issue under consideration of seasonality, time-varying ENSO frequency, and greenhouse warming, we demonstrate that the cross-correlation characteristics between NTA SST and ENSO, are consistent with a one-way Pacific to Atlantic forcing, even though the interpretation of lead-lag relationships may suggest otherwise.11Nsciescopu
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