Boeing Professor of Applied Math & Director of the Computational Finance & Risk Management (CFRM) Program at UW; Senior Research Advisor.

Their volatility decay motivates stat arb strategies

Lake Tekapo, New Zealand. Photo by Tobias Keller on Unsplash

Commodities are often used for hedging against inflation and portfolio diversification. The advent of commodity exchange-traded funds (ETFs) has provided both institutional and retail investors with new ways to gain exposure to a wide array of commodities, including precious metals, agricultural products, and oil and gas.

For example, the SPDR…

Algo Trading

Real examples, performance summary, and python package

A powerful pair, ready to pounce. Photo by Geran de Klerk on Unsplash.

Pairs trading is among the most popular trading strategies in many markets, ranging from equities and ETFs to currencies and futures markets. It involves taking simultaneous positions in two correlated assets. The idea is that while typically it is difficult to accurately capture the price evolution of a single asset…

Algo Trading

Automatically identify assets and determine long/short positions

The long and the short. Photo by Heather M. Edwards on Unsplash

Motivated by the industry practice of pairs trading and long/short equity strategies, we study an approach that combines statistical learning and optimization to construct portfolios with mean-reverting price dynamics.

Our main objectives are:

  • Design a portfolio with mean-reverting price dynamics, with parameters estimated by maximum likelihood;
  • Select portfolios with desirable…

Algo Trading

From portfolio construction to optimal execution

A Deep Dive into Pairs Trading. Photo by NOAA on Unsplash

In this new python package called Machine Learning Financial Laboratory (mlfinlab) developed by Hudson & Thames, there is a module that automatically solves for the optimal trading strategies (entry & exit price thresholds) when the underlying assets/portfolios have mean-reverting price dynamics.

It covers a few mean-reverting models, including the Ornstein-Uhlenbeck…

Stochastic model, simulation, and trading strategy

Roosevelt Lake Bridge, AZ. Photo by Tom Gainor on Unsplash

Trading decisions often depend on the trader’s subjective belief of the distribution of the asset price on a given future date. For example, if a trader anticipates a big price movement for a company stock after its earnings announcement, then perhaps a long straddle position makes sense. …

Photo by WrongTog on Unsplash

Asset prices are driven by factors of different timescales, ranging from long-term market regimes to short-term fluctuations, and they often exhibit nonstationary behaviors, such as time-varying volatility and trends.

The empirical mode decomposition (EMD) is designed for analyzing nonstationary time series. This adaptive data-driven method decomposes any time series into…

Playa de la Misericordia, Spain. Photo by Quino Al on Unsplash

Regime-Switching Market

Asset prices are often seen as being dependent on market conditions. Market regimes may change suddenly and persist for a period of time. The unpredictability of the timing of regime changes also means that associated risks are almost impossible to hedge.

In order to capture these crucial properties of market…

Mt. Rainier glowing on a summer evening. Photo by Caleb Riston on Unsplash

Futures are standardized exchange-traded bilateral contracts of agreement to buy or sell an asset at a pre-determined price at a pre-specified time in the future. At the Chicago Mercantile Exchange (CME), futures trading volume averages over 15 million contracts per day.

Managed futures portfolios play an integral role in hedge…

Empirical mode decomposition and Hilbert spectral analysis

“Everything is Energy” — Photo by Darius Bashar on Unsplash

Market observations and empirical studies have shown that asset prices are often driven by multiscale factors, ranging from long-term economic cycles to rapid fluctuations in the short term. This suggests that financial time series are potentially embedded with different timescales.

On the other hand, nonstationary and behaviors and nonlinear dynamics…

Tim Leung, Ph.D.

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