Event schedule

Non-fungible tokens and VizTech

Authors: Wolfgang Karl Härdle

Abstract: TBA

Probabilistic Forecasting with Machine Learning and Big Data

Authors: Lubos Hanus and Jozef Barunik

Abstract: e propose a distributional deep learning approach to probabilistic forecasting of economic time series. Being able to learn complex patterns from large amount of data, deep learning methods are useful for a decision making that depends on uncertainty of possibly large number of economic outcomes. Such predictions are also informative to decision makers facing asymmetric dependence of their loss on outcomes from possibly non-Gaussian and non-linear variables. We show the usefulness of the approach on the three distinct problems. First, we use deep learning to construct data-driven macroeconomic fan charts that reflect information contained by large number of variables. Second, we obtain uncertainty forecasts of irregular traffic data. Third, we illustrate gains in prediction of stock return distributions which are heavy tailed and suffer from low signal-to-noise ratio.

Portfolio optimization using distortion risk measures via linear programming

Authors: Milos Kopa

Abstract: TBA

Tree Models for Data Driven Decision Making and Optimization

Authors: Dolores Romero Morales

Abstract: TBA

Monitoring network changes in social media

Authors: Cathy Yi-Hsuan Chen, Yarema Okhrin, Tengyao Wang

Abstract:

Bankruptcy prediction for privately held SME's with implications on banks profitability

Authors: Florentina Paraschiv

Abstract:

Empirical Risk Minimization for Time Series: Nonparametric Performance Bounds for Prediction

Authors: Christian Brownlees and Jordi Llorens-Terrazas

Abstract: Empirical risk minimization is a standard principle for choosing algorithms in learning theory. In this paper we study the properties of empirical risk minimization for time series. The analysis is carried out in a general framework that covers different types of forecasting applications encountered in the literature. We are concerned with 1-step-ahead prediction of a univariate time series generated by a parameter-driven process. A class of recursive algorithms is available to forecast the time series. The algorithms are recursive in the sense that the forecast produced in a given period is a function of the lagged values of the forecast and of the time series. The relationship between the generating mechanism of the time series and the class of algorithms is unspecified. Our main result establishes that the algorithm chosen by empirical risk minimization achieves asymptotically the optimal predictive performance that is attainable within the class of algorithms.

Recursive multivariate volatility forecasts for large portfolios

Authors: Cipra, T., Hendrych, R.

Abstract: TBA

Testing structural breaks in large dynamic models

Authors: Z.Praskova

Abstract: TBA

Value at Risk Approach to Producer’s Best Response in Electricity Market with Uncertain Demand

Authors: M.Branda

Abstract: TBA

Marine Fuel Hedging Under the Sulphur Cap Regulations

Authors: Frantisek Cech and Michal Zitek

Abstract: We argue that marine fuels consumers and producers can reduce the uncertainty of their portfolios under the environmental regulations aimed at air pollution re- duction. Our results show that uncertainty can be reduced up to 72%. We also identify Gasoil futures as the universal hedging instrument to manage uncertainty.

Monitoring a developing pandemic with available data

Authors: Jens Perch Nielsen

Abstract: TBA

A Machine Learning Approach to Quantifying the Quality of Real Estate Descriptions

Authors: Joakim Olsen, Arild Brandrud Næss, Pierre Lison

Abstract: This paper explores how to automatically measure the quality of human-generated summaries, based on a Norwegian corpus of real estate condition reports and their corresponding summaries. The proposed approach proceeds in two steps. First, the real estate reports and their associated summaries are automatically labelled using a set of heuristic rules gathered from human experts and aggregated using weak supervision. The aggregated labels are then employed to learn a neural model that takes a document and its summary as inputs and outputs a score reflecting the predicted quality of the summary. The neural model maps the document and its summary to a shared summary content space and computes the cosine similarity between the two document embeddings to predict the final summary quality score. The best performance is achieved by a CNN-based model with an accuracy (measured against the aggregated labels obtained via weak supervision) of 89.5%, compared to 72.6% for the best unsupervised model. Manual inspection of examples indicate that the weak supervision labels do capture important indicators of summary quality, but the correlation of those labels with human judgements remains to be validated. Our models of summary quality predict that approximately 30% of the real estate reports in the corpus have a summary of poor quality.

Implied volatility smoothing at COVID-19 times

Authors: Sebastiano Vitali

Abstract: This work aims at studying the impact of the SARS-CoV-2 pandemic on the global financial markets. In particular, such impact is analysed through the changes of the shape of the implied volatility smile of the options written on several equity indexes and on several stocks. The implied volatility function is estimated using the market-based information of liquid options and applying a semi-parametric smoothing technique that exploits a kernel function and no-arbitrage conditions. Such approach is applied to an extensive set of data to study the evolution of the implied volatility functions through the months of the pandemic. We show, in several cases, a sudden and massive change in the shape of the implied volatility functions.

Uniform confidence bands for generalised random forests estimates

Authors: Kainat Khowaja

Abstract: In this paper, we show that the generalised random forest estimate, as function of x, is uniformly converging to true function. Athey et al (2020) show in their paper on Generalised Random Forests(GRF) that an estimate obtained from GRF at the point is asymptotically normal. Given a data set at hand, we find a critical value such for the uniform confidence bands using multiplier bootstrap, since it is well known that the standard approach via extreme value theory have a very slow asymptotic. We also demonstrate the construction of UCB on the same example given in the study of Athey et al (2020).

Sentiment-driven cryptocurrency market analysis

Authors: Anna Shchekina

Abstract:

Quantinar Data - Science - Education

Authors: Julian Winkel

Abstract: Living in the Information Age, the power of data and statistics has never been more prevalent. Academics, architects, medical doctors, journalists, lawyers, programmers and many other professionals nowadays require an accurate application of statistical methods. Instead many branches are subject to a crisis of integrity, which is shown in improper use of statistical models, p-hacking, HARKing or failure to replicate results. We propose the use of a peer-to-peer education network, Quantinar, to spread statistical knowledge embedded with code in the form of Quantlets.

FRM@Asia an ML Approach

Authors: Ruting Wang

Abstract:

Quantile Portfolio Optimization

Authors: Martin Hronec and Jozef Barunik

Abstract: TBA

Distributional Assymetries and Currency Risk

Authors: Josef Kurka

Abstract: TBA

Tail Risks, Investment Horizons, and Asset Prices

Authors: Matej Nevrla

Abstract: TBA

Deep Reinforcement Learning in Asset Pricing

Authors: Lenka Nechvatalova

Abstract: TBA

A Data-driven Explainable Case-based Reasoning for Bankruptcy Prediction

Authors: Wei Li

Abstract: TBA

Crypto volatility and blockchain mechanism

Authors: Min-Bin Lin

Abstract: TBA

Halfspace depth for general measures

Authors: Petra Laketa, Stanislav Nagy and Dusan Pokorny

Abstract: Let $R^d$ be the $d$-dimensional Euclidean space and $mu$ a finite Borel measure on $R^d$. A halfspace depth of a given point $x$ with respect to $mu$ is defined as the infimum of $mu$-masses of all the closed halfspaces that contain $x$. As such, it measures the centrality of $x$ with respect to $mu$ and is used as a multivariate quantile. The notion of the halfspace depth found also application in machine learning. The existing literature on this interesting topic usually imposes restrictive assumptions on measure $mu$. We consider halfspace depth in general setting, for all finite Borel measures, with intention to collect partial results from the literature and give more general theoretical results. We specially focus on 1) when and how is it possible to reconstruct the underlying measure based on its halfspace depth function and 2) extending the so-called ray basis theorem, which gives an interesting characterization of the point with the maximal halfspace depth, called the halfspace median.

Compressed time series text regression

Authors: Daniel Mittendorf

Abstract: We propose a procedure to model the time-varying relationship between a univariate response and a high-dimensional predictor set derived from textual data. In such settings, standard shrinkage approaches such as lasso can encounter computational and numerical issues. While text-specific regression approaches such as multinomial inverse regression and hurdle regression have been developed, these methods allow for neither time-varying parameters nor heteroskedasticity. Our proposed method first performs several random projections of the predictor matrix to a much lower-dimensional linear subspace. For each of these compressed predictor sets, a time-varying parameter state space model is estimated using fast Kalman filter recursions, allowing for heteroskedasticity in the response. These models' predictions are then averaged dynamically using Bayesian posterior model probabilities. The resulting procedure remains stable with hundreds of thousands of predictors and is trivially parallelisable. Intuitive variable importance measures can be computed naturally with little additional computational cost.

Multivariate crypto-portfolio optimization

Authors: Karel Kozmik

Abstract: TBA

State-space moedlling of claims reserves

Authors: Petr Vejmelka

Abstract: TBA

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