deutsche bank qp1 internships
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Deutsche Bank Global Markets Equity
Equity Quantitative Products
Quantitative Products Analytics (equity derivatives quants) Quantitative Products Engineering (pricing) Quantitative Products Laboratory (research lab in Berlin) Quantitative Products One (equity quants)
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Deutsche Bank Global Markets Equity
The team
Head of all Quantitative Products: Dr.2 Marcus Overhaus (differential geometry + theoretical physics)
„Analytics“ quant team consists currently of 10 members, to grow to 13 by end of March– 12 in London
– 1 senior US quant
– Headed by Hans Buehler, PhD in Finance
Research institute „QP Laboratory“ in cooperation with two Berlin universities (three professors with MS, PhD and PostDoc students)– Research on fundamental questions
– PhD projects together with the quant teams
„Engineering“ team is 36 people
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Deutsche Bank Global Markets Equity
The team, continued
„One“ (=delta one) quant team consists currently of 6 members– 4 in London (3 of them NES graduates)
– 2 in US
– Headed by Andy Ferraris, PhD in Physics
10 quant developers across regions 3 more are joining in 2 months
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Deutsche Bank Global Markets Equity
What exactly is QP One doing?
Algorithmic Trading – automated execution of single stock and portfolio transactions– New and fast-paced field
Driven by shrinking margins in cash and program trading (commissions are in single digits bps, 10mln euro trade can be only 5K euro in commissions)
Expensive traders are being substituted by quants + computers
Pioneered in the late 90s by ITG and CSFB, picked up by all major players
Currently majority of flow in large caps goes through automated execution
– No fundamental starting points like “Black-Scholes”, yet
Few research papers (notably, a series of papers by R. Almgren et al. (market impact and optimal execution); Obizhaeva-Wang (market impact and optimal execution); Lo-MacKinlay-Zhang (time-to-fill modeling); Nevmyvaka (optimal order placement))
Industry generally well ahead of academic research
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Deutsche Bank Global Markets Equity
What exactly is QP One doing?, continued
Primary research topics– Transaction costs analysis, pre- and post- trade analysis
– Optimal execution of equity transactions Macro level: equity basket pricing and optimal liquidation Intermediate level: “trajectory follower” Micro level: optimal single order placement
– Modeling of intraday dynamics of literally everything (volumes, spreads, volatilities, durations, correlations, etc)!
Data specifics– Terabytes of market and proprietary high frequency (transaction level) data
– Need specialized DBMS to handle such volumes
Development environment– Perl + Matlab for research
– C++ for production
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Deutsche Bank Global Markets Equity
Transaction cost modeling
Some banks have heuristic “models” provided by traders R. Almgren et al. estimated transaction cost model on Citigroup data
– Separated permanent (due to information revelation) and temporary (due to liquidity demand) components
– Made some statistical mistakes along the way…
– …yet became the industry standard... sort of
We can do better than that (after Econometrics 3, 4)! We used the model with exponential decay of temporary impact
– independently appeared in Obizhaeva-Wang, and justified theoretically
Also include spread effect
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Deutsche Bank Global Markets Equity
Algorithmic Trading, macro level
Providing best execution of a portfolio transaction with respect to a certain benchmark, minimizing risk and transaction costs
Single stock case pioneered by R. Almgren, yet little practical value– Optimal execution horizon does not depend on the order size, go tell that to a
trader Selection of a proper optimization target is a big theoretical and practical
issue, mean-variance optimization produces dynamically inconsistent trajectories
Very computationally intensive (sizes of involved [dense, quadratic] optimization problems are up to 50000 variables)
Exogenously (by ) constrained in terms of computation time Lots of other constraints (trading speed, factor exposures, self financing, etc) Yet possible to do, although only approximate solutions are available
– 6 seconds for 500 stocks
– 60 seconds for 2000 stocks
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Deutsche Bank Global Markets Equity
Algorithmic Trading, “intermediate” level
First generation single stock strategies– VWAP [value-weighted average price as a benchmark]
– PctVol [constant “participation” rate as a % of market volume]
Can be improved indefinitely, but marginal benefit is decreasing– VWAP - 2bps is a market standard
Most general form: “Follow a trading schedule defined in terms of physical and business time, taking into account market conditions, limits, volume caps, etc”
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Deutsche Bank Global Markets Equity
Algorithmic Trading, micro level:Execution of a single small order through limit order book
Rule-based systems (non-portable, hard to support/modify and prone to errors)
vs. Optimal order placement
– Simulation - based estimation
– Stochastic dynamic programming
– Stock by stock
– Must be robust to misspecification of micro-structural parameters
– Computationally intensive...
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Deutsche Bank Global Markets Equity
Internships programs
2-3 for QP One– Strong math, including optimal control
– Very strong econometrics High frequency time series analysis Survival analysis Nonparametric methods
– Strong Matlab programming skills, Perl is a plus
– One of the positions is in Japan, so should have strong preference to continue full-time there (please indicate in the cover letter)
1 for QP Analytics– Strong math, in particular
Stochastic calculus PDE Monte-Carlo methods
– Strong C++
2 for QP Engineering– Good math
– Understanding of derivative products and their pricing
– Strong VB/Excel
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Deutsche Bank Global Markets Equity
Quantitative Products One – Internship projects
Modeling of intraday dynamics of various characteristics of the order flow
Time-to-fill modeling Optimal portfolio execution using conic optimization techniques Adapting current models to new markets (HK, Tokyo, Australia)
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Deutsche Bank Global Markets Equity
Quantitative Products Analytics – Internship projects
Project suggestion 1:Practical implementation of hedging algorithms in the presence of jumps– Problem:
In the presence of jumps, plain delta-hedging is neither theoretically not practically justified.
– If traded options are available, how can the jump risk to our portfolio be mitigated.
– Broad theory on the subject but few (published) workable results.
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Deutsche Bank Global Markets Equity
Quantitative Products Analytics – Internship projects
Project suggestion 2:Hedging of barrier risk if the market impact function is known.– Problem:
When hedging barriers, we are subject to substantial „gap“ risk when unwinding our delta-position.
– How can the knowledge of a particular market impact function (cost of trading a certain quantity of stock) be used efficiently to incorporate gap risk into the pricing of barriers.
– FD/MC solution.
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Deutsche Bank Global Markets Equity
Quantitative Products Engineering
Deals, rather than projects.
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Strictly Private and Confidential Click and Insert the Date
Equitech:DB Equity Proprietary Trading
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Deutsche Bank Global Markets Equity
About Equitech
Equitech is in its fourteenth year of operation, managing proprietary capital on behalf of Deutsche Bank.
The group has a consistent history of profitability The culture is very tech friendly and analytically oriented. Members of our group
were formerly researchers or faculty in theoretical computer science or physics at places like Bell Labs, MIT, Stanford, NYU and Berkeley.
The team is based in New York, London and Tokyo. Currently has 28 employees Currently managing a global portfolio of several billion dollars. Equitech employs a number of strategies, ranging in products from its core non
black-black box based trading strategy that adapts to market conditions, to fully automated strategies, including ultra high-frequency trading in equities and listed options.
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Deutsche Bank Global Markets Equity
Typical quant work conducted at Equitech Trade execution
– Our large daily trading volume introduces large transaction costs inherent to most of our strategies
– We are constantly working to improve the algorithms we use to execute portfolio trades - even a single basis point improvement in the execution algorithm translates into millions of dollars of additional pnl.
Development work on automated trading systems– Continually improve current strategies
– Systematically search for new ideas that can be automated into trading systems to take advantage of market inefficiencies.
Portfolio return attribution – De-composing portfolio returns into factors, this allows us to quantify
our exposure to market factors (various types of beta) and better isolate the alpha our strategies generate.
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Deutsche Bank Global Markets Equity
Opportunities
We are looking to hire several quant analysts for our London office. In addition, opportunities exist for summer internships.
What we look for:– Evidence of academic excellence in a highly mathematical degree
– Strong programming ability.
– Ability and desire to work independently as well as part of a team
– Knowledge of finance/mathematical finance/statistics is of course desirable
How to apply:
Send CV to [email protected]
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Deutsche Bank Global Markets Equity
Why Finance industry is better than Finance academia
Real problems, not artificial ones You can see which models work and which do not You can talk directly to informed agents from the papers on market
microstructure You can still publish, if you want to (true for DB, not true for most
other banks) You can still do teaching, if you want to (internal seminars for
traders/graduates) £,¥