A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Lognormal distribution used for predicting team rankings in an orienteering relay race.
A new model predicts race places using changeover-times and log-normal distributions.
German FinBERT improves financial text analysis performance.
Proposes a regularization approach to model German power derivative market, identifying significant risk spillovers.
Quantum computing improves fault diagnosis in industrial processes.
Study reveals stylized facts in German bond futures markets.
Deep learning has enabled traditional reinforcement learning methods to deal with high-dimensional problems. However, one of the disadvantages of deep reinforcement learning methods is the limited exploration capacity of learning agents. In this paper, we introduce an approach that integrates human strategies to increa…
This paper has been submitted to the Proceedings of the Australian-German Workshop on Differential Geometry in the Large held at the mathematical research institute MATRIX in Creswick, Victoria, Australia, Feb.2-Feb.14, 2019. We describe and discuss 2 important open problems in Sasaki geometry.
How does dynamic price information flow among Northern European electricity spot prices and prices of major electricity generation fuel sources? We use time series models combined with new advances in causal inference to answer these questions. Applying our methods to weekly Nordic and German electricity prices, and oi…
Paper tackles adapting multiple domains to a target domain using distillation and dictionary learning.
We present a relatively detailed analysis of the persistence probability distributions in financial dynamics. Compared with the auto-correlation function, the persistence probability distributions describe dynamic correlations non-local in time. Universal and non-universal behaviors of the German DAX and Shanghai Index…
Short-term probabilistic forecasting of German electricity imbalance prices.
Detects out-of-distribution sentences in Neural Machine Translation.
Research optimizes a small RES utility's portfolio by dynamically trading in German electricity markets.
Classical (Itô diffusions) stochastic volatility models are not able to capture the steepness of small-maturity implied volatility smiles. Jumps, in particular exponential Lévy and affine models, which exhibit small-maturity exploding smiles, have historically been proposed to remedy this (see \cite{Tank} for an overvi…
Investigates if adding cryptocurrencies to German portfolios diversifies better, finding mixed results.
We determine the distribution of size and growthrates of German business firms in 1987-1997. We find a log-normal size distribution. The distribution of growth rates has fat tails. It can be fitted to an exponential in a narrow central region and is dominated by finite-sample-size effects far in its wings. We study the…
Paper introduces new risk measures that unify two existing types.
The grid integration of intermittent Renewable Energy Sources (RES) causes costs for grid operators due to forecast uncertainty and the resulting production schedule mismatches. These so-called profile service costs are marginal cost components and can be understood as an insurance fee against RES production schedule u…
In the following paper, we analyse the ID-Price in the German Intraday Continuous electricity market using an econometric time series model. A multivariate approach is conducted for hourly and quarter-hourly products separately. We estimate the model using lasso and elastic net techniques and perform an out-of-samp…
Low redispatch prices boost green hydrogen production cost, encouraging electrolyzer siting.
Based on the tick-by-tick stock prices from the German and American stock markets, we study the statistical properties of the distribution of the individual stocks and the index returns in highly collective and noisy intervals of trading, separately. We show that periods characterized by the strong inter-stock coupling…
Study on excess mortality in Germany during 2020-21.
For the first time, we apply the wavelet coherence methodology on biofuels (ethanol and biodiesel) and a wide range of related commodities (gasoline, diesel, crude oil, corn, wheat, soybeans, sugarcane and rapeseed oil). This way, we are able to investigate dynamics of correlations in time and across scales (frequencie…
Recent works have highlighted the strength of the Transformer architecture on sequence tasks while, at the same time, neural architecture search (NAS) has begun to outperform human-designed models. Our goal is to apply NAS to search for a better alternative to the Transformer. We first construct a large search space in…
We investigate the large-volatility dynamics in financial markets, based on the minute-to-minute and daily data of the Chinese Indices and German DAX. The dynamic relaxation both before and after large volatilities is characterized by a power law, and the exponents usually vary with the strength of the large vo…
We investigate the large-fluctuation dynamics in financial markets, based on the minute-to-minute and daily data of the Chinese Indices and German DAX. The dynamic relaxation both before and after the large fluctuations is characterized by a power law, and the exponents usually vary with the strength of the lar…
Multi-task learning uses auxiliary data or knowledge from relevant tasks to facilitate the learning in a new task. Multi-task optimization applies multi-task learning to optimization to study how to effectively and efficiently tackle multiple optimization problems simultaneously. Evolutionary multi-tasking, or multi-fa…
Deep neural networks improve chemical reactor control using MPC.
In this paper, we present Neural Phrase-based Machine Translation (NPMT). Our method explicitly models the phrase structures in output sequences using Sleep-WAke Networks (SWAN), a recently proposed segmentation-based sequence modeling method. To mitigate the monotonic alignment requirement of SWAN, we introduce a new …
One of the findings of the recent literature is that the 2008 financial crisis caused reduction in international diversification benefits. To fully understand the possible potential from diversification, we build an empirical model which combines generalised autoregressive score copula functions with high frequency dat…
We examine the novel problem of the estimation of transaction arrival processes in the intraday electricity markets. We model the inter-arrivals using multiple time-varying parametric densities based on the generalized F distribution estimated by maximum likelihood. We analyse both the in-sample characteristics and the…
These are notes of a talk I gave in a seminar at the University of Pennsylvania summarizing results in the Habilitation by Jost Eschenburg on "Freie isometrische Aktionen auf kompakten Lie-Gruppen mit positiv gekruemmten Orbitraeumen". Due to the fact that it is published in a not easily accesible journal (and is writt…
Paper models and forecasts intra-day electricity price spreads.
The study uses neural networks to classify and predict coronavirus data.
THieF improves day-ahead electricity price prediction accuracy by reconciling hourly and block forecasts.
The article prices exchange options using variance gamma-like models.
Paper proposes a new method for probabilistic electricity price forecasting.
This paper contributes to the literature on international stock market comovements and contagion. The novelty of our approach lies in application of wavelet tools to high-frequency financial market data, which allows us to understand the relationship between stock markets in a time-frequency domain. While major part of…
Based on the tick-by-tick price changes of the companies from the U.S. and from the German stock markets over the period 1998-99 we reanalyse several characteristics established by the Boston Group for the U.S. market in the period 1994-95, which serves to verify their space and time-translational invariance. By increa…
A quantitative check of weak efficiency in US dollar/German mark exchange rates is developed using high frequency data. We show the existence of long term return anomalies. We introduce a technique to measure the available information and show it can be profitable following a particular trading rule.
Optimal energy trading strategy for intraday markets using Hawkes processes.
New method recovers classical cut and paste groups for manifolds.
Rebellion Research's AI strategy outperformed the S&P 500 for 14 years.
The goal of this modern presentation, followed by an English translation from the German, is to make available some parts of Lie's very systematic mathematical thought which deserve to join the contemporary literature, and above all also, to be read.
This paper improves bidding price prediction for ancillary services markets, boosting revenues.
Optimizes intraday electricity trading to minimize costs.
Recently, substantial progress has been made in language modeling by using deep neural networks. However, in practice, large scale neural language models have been shown to be prone to overfitting. In this paper, we present a simple yet highly effective adversarial training mechanism for regularizing neural language mo…