Nowcasting for financial markets
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,...
Predicting volatility with heterogeneous autoregressive models
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
The predictive power score is a summary metric for predictive relations between data series. Like correlation, it is suitable for quick data exploration. Unlike...
Reward-risk timing
Reward-risk timing refers to methods for allocating between a risky market index and a risk-free asset. It is a combination of reward timing, based...
Machine learning and macro trading strategies
Machine learning can improve macro trading strategies, mainly because it makes them more flexible and adaptable, and generalizes knowledge better than fixed rules or...
The predictive superiority of ensemble methods for CDS spreads
Through 'R' and 'Python' one can apply a wide range of methods for predicting financial market variables. Key concepts include penalized regression, such as...
RECENT ARTICLES
R tidyverse for macro trading research
The tidyverse is a collection of packages that facilitate data science with R. It is particularly powerful for macro trading research because it...
Nowcasting with MIDAS regressions
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...
Market-implied macro shocks
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’...