Quantitative Methods

Unlike market price trends, macroeconomic trends are hard to track in real-time. Conventional econometric models are immutable and not backtestable for algorithmic trading. That...

Dimension reduction methods of machine learning are suited for detecting latent factors of a broad set of asset prices. These factors can then be...

Any asset can use a portfolio of similar assets to hedge against its factor exposure. The factor residual risk of the hedged position is...

Typically, we cannot predict a meaningful portion of daily or higher-frequency market returns. A more realistic approach is classifying the state of the market...

Survey data and asset prices can be combined to estimate high-frequency growth expectations. This is a specific form of nowcasting that implicitly captures all...

Nowcasting macro-financial indicators requires combining low-frequency and high-frequency time series. Mixed data sampling (MIDAS) regressions explain a low-frequency variable based on high-frequency variables and...

Combinations of equity returns and yield-curve changes can be used to classify market-implied underlying macro news. The methodology is structural vector autoregression. Theoretical ‘restrictions’...

Nowcasting is a modern approach to monitoring economic conditions in real-time. It makes financial market trading more efficient because economic dynamics drive corporate profits,...

Heterogeneous autoregressive models of realized volatility have become a popular standard in financial market research. They use high-frequency volatility measures and the assumption that...

The predictive power score is a summary metric for predictive relations between data series. Like correlation, it is suitable for quick data exploration. Unlike...