Systematic Value

The tidyverse is a collection of packages that facilitate data science with R. It is particularly powerful for macro trading research because it...

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’...

In financial markets, risk refers to the probability distribution of future returns. Uncertainty is a broader concept that encompasses ambiguity about the parameters of...

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,...

Non-U.S. financial institutions hold precautionary positions in U.S. dollar assets as protection against financial shocks. This gives rise to a safety premium on the...

Hedge ratios of international investment positions have increased over past decades, spurred by regulation and expanding derivative markets. This has given rise to predictable...

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...

When macroeconomic conditions change rational inattention and cognitive frictions plausibly prevent markets from adjusting expectations for futures interest rates immediately and fully. This is...

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