Paris - Long term internship - Global Markets - AMM Trading

BNP Paribas Corporate & Institutional Banking (CIB) is a leading European investment bank with global leadership in many of our businesses. We are part of the BNP Paribas Group, a financial institution with solid foundations and a proven ability to adapt to change. If you are thinking about a career in investment banking, there is no better place to begin your journey than with BNP Paribas CIB. With nearly 20,000 employees in over 50 countries, we can offer you an exciting start to your career.

Global Markets is BNP Paribas’ capital markets business, delivering investment solutions across a wide range of asset classes and industry-leading services.

As world leader in derivatives, BNP Paribas Global Markets offers clients tailored products, state-of-the-art trading, processing systems, research and strategic advising from its teams of experts, while also maintaining a sustainable economic model.

The team and its projets :

The Automated Market Making team (AMM) is one of the strongest teams in market-making trading. The team includes over 60 people world- wide with a physical present in Paris, London, New York and Hong Kong. The business trades every liquid asset especially equities and derivatives.

Since the last few years, we’ve become one of the leaders in electronic market makers, contributing for 5% to 10% of market share. The success of such activity results in a high computer science level and a good quantitative development in every field from macros view (portfolio optimization, market risk model, future price prediction …) to microstructure view (tick to tick data, microstructure effect, lead lag …).

Interning in the team, you will be in charge of pursuing a full study of a topic subjected to our problematic and you will also responsible for proposing solutions and its implementations. As a member of our team, you will be in permanent discussion with traders in order to have a direct contact with the market, a key component to be successful in quantitative trading.

The following pages show several topics proposed by our team:

Lead Lag

Within a market making framework, you’ll have to search patterns and links between several asset classes. You’ll be working with the most liquid electronic assets in order to find new strategies. Your role will consist in implementing and testing new ideas mixing microstructure and price dynamics.

Valuation of Microstructure Alpha

Your goal for this internship is to study performance of existing alpha sources and value them under several market constraints. You’ll use our back tester and testing framework in order to develop new trading ideas. You’ll also have to find an efficient way to combine several alphas to obtain stable market models.

Short Term Dispersion

Dispersion signal is one of the most classic alphas in statistical arbitrage. You’ll work on several existing models available in literature and adapt them into intraday framework. You’ll have to focus on tick-to-tick data which are highly discontinuous. You’ll need to rethink models in order to be able to apply these models within this framework.

Market Dynamics / Market impact modeling

Within a market making framework, one needs to study his impact on the market in order to reduce it at the minimum. Therefore, your role is to model a response function of several kinds of orders to study their dynamics.

Market Information Asymmetry

Thanks to MiFID, several exchanges have been created which implied a split of available liquidity. Your role will be a deep study of interaction between these exchanges (microstructure, lead lag …).

Alpha Horizon Mixture

In statistical arbitrage, we have developed many “alphas” which predict the future price move. Each alpha is well studied but we lack information about the cross-alpha behavior, especially between alpha from different data sources and different time horizons. The main idea here is to figure out the best way to combine these alphas.

Extreme Events Detection and Prediction

Market-making strategies suppose a normal behavior of products. In some cases, rare event will occur and avoidable losses usually happen. You will use data mining, statistics and pattern detection in order to detect and model extreme events.

Portfolio Optimization / Risk Metrics / Clustering

A market maker need to value his intraday and extraday risk. With research papers about random matrix theory, multi-factor models and regularization you’ll compare several risk proxies for portfolio construction.

Profile

• We are looking for outstanding, high academic background students
• Showing interest in financial modeling, applied mathematics and computer science