arXiv subjects

Maximilian Göbel

Publications and source records attributed to Maximilian Göbel.

6 recordsLinked to original sources

arXiv2024

Unveiling True Talent: The Soccer Factor Model for Skill Evaluation

Evaluating a soccer player's performance can be challenging due to the high costs and small margins involved in recruitment decisions. Raw observational statistics further complicate an accurate individual skill assessment as they do not abstract from the potentially confounding factor of team strength. We introduce the Soccer Factor Model (SFM), which corrects this bias by isolating a player's true skill from the team's influence. We compile a novel data set, web-scraped from publicly available data sources. Our empirical application draws on information of 144 players, playing a total of over 33,000 matches, in seasons 2000/01 through 2023/24. Not only does the SFM allow for a structural interpretation of a player's skill, but also stands out against more reduced-form benchmarks in terms of forecast accuracy. Moreover, we propose Skill- and Performance Above Replacement as metrics for fair cross-player comparisons. These, for example, allow us to settle the discussion about the GOAT of soccer in the first quarter of the twenty-first century.

stat.AP↗

arXiv2022

Zombie-Lending in the United States -- Prevalence versus Relevance

Extraordinary fiscal and monetary interventions in response to the COVID-19 pandemic have revived concerns about zombie prevalence in advanced economies. Within a sample of publicly listed U.S. companies, we find zombie prevalence and zombie-lending not to be a widespread phenomenon per se. Nevertheless, our results reveal negative spillovers of zombie-lending on productivity, capital-growth, and employment-growth of non-zombies as well as on overall business dynamism. It is predominantly the class of healthy small- and medium-sized companies that is sensitive to zombie-lending activities, with financial constraints further amplifying these effects.

econ.GN↗

arXiv2021

On Spurious Causality, CO2, and Global Temperature

Stips, Macias, Coughlan, Garcia-Gorriz, and Liang (2016, Nature Scientific Reports) use information flows (Liang, 2008, 2014) to establish causality from various forcings to global temperature. We show that the formulas being used hinges on a simplifying assumption that is nearly always rejected by the data. We propose an adequate measure of information flow based on Vector Autoregressions, and find that most results in Stips et al. (2016) cannot be corroborated. Then, it is discussed which modeling choices (e.g., the choice of CO2 series and assumptions about simultaneous relationships) may help in extracting credible estimates of causal flows and the transient climate response simply by looking at the joint dynamics of two climatic time series.

stat.AP↗

arXiv2021

Arctic Amplification of Anthropogenic Forcing: A Vector Autoregressive Analysis

On September 15th 2020, Arctic sea ice extent (SIE) ranked second-to-lowest in history and keeps trending downward. The understanding of how feedback loops amplify the effects of external CO2 forcing is still limited. We propose the VARCTIC, which is a Vector Autoregression (VAR) designed to capture and extrapolate Arctic feedback loops. VARs are dynamic simultaneous systems of equations, routinely estimated to predict and understand the interactions of multiple macroeconomic time series. The VARCTIC is a parsimonious compromise between full-blown climate models and purely statistical approaches that usually offer little explanation of the underlying mechanism. Our completely unconditional forecast has SIE hitting 0 in September by the 2060's. Impulse response functions reveal that anthropogenic CO2 emission shocks have an unusually durable effect on SIE -- a property shared by no other shock. We find Albedo- and Thickness-based feedbacks to be the main amplification channels through which CO2 anomalies impact SIE in the short/medium run. Further, conditional forecast analyses reveal that the future path of SIE crucially depends on the evolution of CO2 emissions, with outcomes ranging from recovering SIE to it reaching 0 in the 2050's. Finally, Albedo and Thickness feedbacks are shown to play an important role in accelerating the speed at which predicted SIE is heading towards 0.

econ.EM↗

arXiv2020

Optimal Combination of Arctic Sea Ice Extent Measures: A Dynamic Factor Modeling Approach

The diminishing extent of Arctic sea ice is a key indicator of climate change as well as an accelerant for future global warming. Since 1978, Arctic sea ice has been measured using satellite-based microwave sensing; however, different measures of Arctic sea ice extent have been made available based on differing algorithmic transformations of the raw satellite data. We propose and estimate a dynamic factor model that combines four of these measures in an optimal way that accounts for their differing volatility and cross-correlations. We then use the Kalman smoother to extract an optimal combined measure of Arctic sea ice extent. It turns out that almost all weight is put on the NSIDC Sea Ice Index, confirming and enhancing confidence in the Sea Ice Index and the NASA Team algorithm on which it is based.

stat.AP↗

arXiv2018

Reframing the S\&P500 Network of Stocks along the \nth{21} Century

Since the beginning of the new millennium, stock markets went through every state from long-time troughs, trade suspensions to all-time highs. The literature on asset pricing hence assumes random processes to be underlying the movement of stock returns. Observed procyclicality and time-varying correlation of stock returns tried to give the apparently random behavior some sort of structure. However, common misperceptions about the co-movement of asset prices in the years preceding the \emph{Great Recession} and the \emph{Global Commodity Crisis}, is said to have even fueled the crisis' economic impact. Here we show how a varying macroeconomic environment influences stocks' clustering into communities. From a sample of 296 stocks of the S\&P 500 index, distinct periods in between 2004 and 2011 are used to develop networks of stocks. The Minimal Spanning Tree analysis of those time-varying networks of stocks demonstrates that the crises of 2007-2008 and 2010-2011 drove the market to clustered community structures in both periods, helping to restore the stock market's ceased order of the pre-crises era. However, a comparison of the emergent clusters with the \textit{General Industry Classification Standard} conveys the impression that industry sectors do not play a major role in that order.

q-fin.ST↗