The role will involve designing and developing scalable mid frequency systematic strategies for the long/short option trading team at a family office in New York. The remit it to build predictive strategies based on econometric modeling and statistical techniques for pairs trading, statistical arbitrage, correlation, covariance and calendar spread, trend following, momentum and mean reversion strategies. The role will focus specifically on developing systematic strategies for cross asset volatility trading with a focus on designing and testing strategies.
Requires
- 4-6 years' experience in a quantitative research position
- Masters / PhD in Applied Maths, Econometrics or similar.
- Experience developing forecasting strategies for nonlinear financial instruments, preferably vanilla options.
- Strong understanding of GARCH Models and autoregressive conditional heteroskedasticity.
- Applied research background with a nontrivial understanding of the following:
- Statistical theory Maximum likelihood, Bayes, minimax, Parametric versus Nonparametric Methods, Bayesian versus Non-Bayesian Approaches, classification, regression, density estimation
- Parametric methods (Linear Regression, Model Selection, Generalized Linear Models, Mixture Models, Classification (linear, logistic, support vector machines), Graphical Models, Structured Prediction, Hidden Markov Models)
- Nonparametric methods (Nonparametric Regression and Density Estimation, Nonparametric Classification, Boosting, Clustering and Dimension Reduction, PCA, Manifold Methods, Principal Curves, Spectral Methods, The Bootstrap and Subsampling, Nonparametric Bayes)
- Sparsity (High Dimensional Data and Sparsity, Basis Pursuit and the Lasso Revisited, Sparsistency, Consistency, Persistency, Greedy Algorithms for Sparse Linear Regression, Sparsity in Nonparametric Regression. Sparsity in Graphical Models, Compressed Sensing)
- Kernel methods (Mercel kernels, reproducing kernel Hilbert spaces, relationship to nonparametric statistics, kernel classification, kernel PCA, kernel tests of independence)
- Technical skills and the ability to implement algorithms Core: Python, R, Secondary: C++, or Java
- Knowledge of the following core:
- Data Structures: Arrays, Linked Lists, Dynamic Linked Lists, Binary Tree Search, Balanced Binary Search Trees, Trees, Balanced Trees, Balanced Trees, When to choose balanced tree vs HashMap time complexities, Stacks, Queues, Heap, Disjoint Set.
- Algorithms: Dynamic Programming, Sorting, Strings, Implementation, Constructive Algorithms, Search.
- Smart Pointers: Smart Pointers, Share pointers and unique pointers.
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