Price of 5-year credit default swap (coupon=100) at the close of the market on

The credit default swap (CDS) is a financial instrument enabling the transfer of an issuers credit risk between two investors. The CDS provides investors with a very simple, very liquid tool for hedging or investing. The price of a CDS is the market barometer for the risk of a borrower.

Arbitrage is an investment technique that combines purchases and sales of financial instruments having offsetting risks. The net return on the portfolios thus combined is the arbitrage profit. The performance of arbitrage portfolios is not governed by the performance of the financial markets. Arbitrages are investment processes that use leverage such investments are restricted to professional investors.

Hellebore Capital (HC) is a management company specializing in credit derivatives arbitrage. Its technology gives HC the ability to operate in all the credit derivatives markets of Asia, Europe and North America. If you would like to learn more about HC and its products, you cancreate an account online. HC is regulated by the UK Financial Conduct Authority (FCA).

Hellebore Capital, London in association with its FinTech sister company, Hellebore Technologies, Paris has developed a front-to-back real-time solutions platform to invest in over-the-counter credit derivatives markets. Within this context, Hellebore rethinks continuously the way it sources, validates and distributes data and this, in turn, design drives and adoption of new architectures covering all aspects of data handling including sourcing, cleaning, transformation, enrichment, transmission and storage.

The Analyst DATA / IT Front Office position is at the critical center of our business and our dynamic, developmental environment requires innovative strategic thinking and immediate, real-time solutions. Analyst DATA / IT Front Office will be in charge of developing on-the-fly solutions, leveraging the existing Hellebore open architecture. They will be expected to leverage the numerous internal Pythons libraries, the companys large-scale computing capacity and various web portals. Analyst DATA / IT Front Office will also be closely related to a new Django project for data dissemination.

WHO WE ARE LOOKING FOR. The Analyst DATA / IT Front Office will be responsible for developing tools used to streamline investment decisions, risk analysis or operational processes directly interacting with front office traders and leveraging our technology partners. Hellebore is looking for innovators and problem-solvers, providing market risk management solutions, big data and more; the successful candidate will oversee the operation of its platforms and support the continuing automation of processes. Hellebore focuses on automation and tooling both to eliminate unnecessary manual effort and to enhance monitoring capabilities.

Owning the relationship between the portfolio managers and the technology partners

Developing specialist knowledge in systems, sharing knowledge with team members and partners

Managing projects through completion, adhering to project-management best practices

You have knowledge of Trading or Risk Functions – or can adapt your existing experience to the financial markets.

You have experienced the demanding nature of delivering real time solutions.

You are experienced in SQL and have a strong understanding of how relational databases work.

You know Python to an advanced level to automate processes or have a development background

Based in London since 2016, Hellebore Capital is a hedge fund specializing in Credit Default Swaps arbitrage since 2013. The Company has steadily increased its assets under management based on this highly focused investment strategy. Combining new technologies with its over-the-counter dealer relationships, the Company can invest in the global credit markets to deliver specific opportunities for alpha for its investors. The Company is now working on the launch of a new investment vehicle, leveraging past years research efforts. Hellebore Capitals R&D team combines multiple aspects of machine learning technologies to monitor the CDS markets, to detect investment opportunities and to assess risks.

The Quantitative Research position will reinforce Hellebore Capital agile and challenging research team. The Quantitative Research position will be in charge of digging into statistical / machine learning ideas in a constant dialogue with portfolio managers.

WHO WE ARE LOOKING FOR. The Quantitative Research Scientist will be responsible of several research projects. He / She must be able to present innovative and challenging ideas as well as to implement them. He / She must be able to popularize his work to the whole team.

Developing and improving Hellebore Capital statistical / Machine Learning models over OTC transactions and CDS time series.

Updating Hellebore research standards, sharing knowledge with team members and partners.

Being a creative force : translating research efforts into trading ideas.

Contributing to Hellebore Capitals influence & monitoring technology intelligence by publishing research articles and attending top conferences.

You have a Master degree or PhD in Finance / Stochastic Calculus / Statistics / Data Science.

You fit and enjoy both aspects of R & D : Research (Ability to go through state-of the-art, suggest and test innovative ideas) and Development (Ability to present research contributions and implement them into Hellebore systems, to build visualization tools) .

You are resourceful and present a curious mindset.

This position is also opened to students looking for a 6 month internships (end-of-studies internship, gap-year internship etc).

Hellebore Capitals R&D team focuses on credit default swap (CDS) time series modelling, both to define quantitative investment strategies or to design new risk management frameworks. The team has developed an expertise in clustering algorithms, a set of machine-learning techniques which clusters assets demonstrating similar behaviours. These techniques are providing resilient results despite noise and jumps characteristics of CDS time series. More recently, some of the research efforts have concentrated on asynchronous time series description and prediction. Amongst other topics, Natural Language Processing techniques have been explored as well, hand in hand with Hellebore Technologies, a sister company.

The team publishes some of its results and participates in various conferences. We think this is good practice to constantly challenge our ideas.

By hosting interns or monitoring projects with PhD students, Hellebore Capital has established partnerships with universities amongst the best in Europe : theEcole Polytechnique(France),Imperial College(London), etc. Groups of students fromENSAE(France) has also been supervised on Data Science projects.

We are always looking for resourceful and motivated people, do not hesitate to apply to any of our research open positions. If nothing fits your prospects & skills, we do encourage spontaneous applications throughHellebore HR.

Toward a generic representation of random variables for machine learning

This paper introduces a new representation of time series and an associated metric for clustering time series in homogeneous sets. The representation is applied to CDS time series. It essentially refines clusters of correlated assets by taking into account the marginal distribution of their returns. The method provides stable and reliable clusters.

A proposal of a methodological framework with experimental guidelines to investigate clustering stability on financial time series

This paper proposes a methodology to assess the robustness of clusters. Besides, it also aims to understand asset clustering invariants and to answer questions such as: Are clusters the same using daily/weekly/monthly returns, using different maturities in the CDS term structure, both during bear and bull periods?

Clustering Financial Time Series: How Long is Enough?

This paper also tackles the question of clustering stability, but from more of a theoretical point of view. We first show that the clustering methodology can be consistent. This means that the clusters obtained from the algorithm converge to the correct clusters. This also means that there is a minimum value of the time sample T (in practice, a minimum number of returns observed in the historical time series) required so that the clusters obtained are correct. We show that this minimum value T is strongly dependent of the clustering methodology. The challenge for us is then to find the clustering methodology which yields the smallest possible T. The smaller the T, the more relevant the applications of clustering financial time series can be.

Optimal Copula Transport for Clustering Multivariate Time Series

This paper contains several ideas and opens several research directions for us. We focus on (i) Understanding dependence between objects described by several time series; (ii) Defining new dependence coefficients which can robustly target specific dependence patterns; and (iii) Studying various geometries for copulas. W

distance between these objects, empirical ergodicity and the rate of convergence of the clustering algorithms when the objects are described by several time series.

A Review of Two Decades of Correlations, Hierarchies, Networks and Clustering in Financial Markets

This paper is an ongoing review on the state of the art of clustering financial time series and the study of correlation and other interaction networks. It aims at gathering in one place the relevant material that can help the researcher in the field to have a bigger picture, the quantitative researcher to play with this alternative modeling of the financial time series, and the decision maker to leverage the insights obtained from these methods.

We will give and gave talks at these machine learning conferences:

Analyzing credit indices time series: How random are trades arrival times?

Autoregressive Convolutional Neural Networks for Asynchronous Time Series

Putting Self-Supervised Token Embedding on the Tables

Exploring and measuring non-linear correlations: Copulas, Lightspeed Transportation and Clustering

Empirical convergence rates of dependence-based clustering methods illustrated with financial time series

9th International Conference on Computational and Methodological Statistics – CMStatistics 2016

Clustering Financial Time Series: How Long is Enough?

25th International Joint Conference on Artificial Intelligence – IJCAI 2016

, New York Hilton Midtown Hotel, New York City, USA. (print,slides)

Optimal transport vs. Fisher-Rao distance between copulas for clustering multivariate time series

,IEEE Workshop on Statistical Signal Processing – SSP 2016, Palma de Mallorca, Spain. (preprint,slides)

33rd International Conference on Machine Learning – ICML 2016

Optimal Copula Transport for Clustering Multivariate Time Series

,41st IEEE International Conference on Acoustics, Speech and Signal Processing – ICASSP 2016, Shanghai International Convention Center, Shanghai, China. (slides,poster)

A proposal of a methodological framework with experimental guidelines to investigate clustering stability on financial time series

,14th International Conference on Machine Learning and Applications – IEEE ICMLA 2015, Miami, Florida, USA. (print,slides)

,2nd conference on Geometric Science of Information – GSI 2015, Ecole Polytechnique, Palaiseau, France. (print,video,slides)

On clustering financial time series: a need for distances between dependent random variables

,Computational information geometry for image and signal processing – CIGISP 2015, International Centre for Mathematical Sciences, Edinburgh, UK.

Comment partitionner automatiquement des marches alatoires ? Avec application la finance quantitative,

GRETSI 2015, Ecole Normale Suprieure de Lyon, Lyon, France.

32nd International Conference on Machine Learning – ICML 2015, Lille, France (poster)

We also participate to various AI / Machine Learning / Big Data / FinTech meetings in Paris and London:

Data Science Summer School, Ecole Polytechnique, 28 Aug-1 Sept 2017

Summer school on the ethics of Artificial Intelligence, 26-30 September 2016

Paris Machine Learning Meetup 3 Season 4: OPECST, Correlations, Transfer Learning, DL @Amazon, Car Sales, 9 November 2016 (slides)

Paris Machine Learning Meetup 5 Season 2: Time Series and FinTech, Adversarial Algos, 14 January 2015 (slides)

Unsolicited applications from graduate students in math, physics, machine learning, financial engineering with strong computational skills are most welcomed.

Hellebore Capital Ltd – Michelin House 81 Fulham Rd London SW3 6RD, UK

Regulated by the FCA, Firm Reference Number 716140

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