Theme 4: Shock and Risk Measures

This collection of Jupyter notebooks introduces quantamental indicators of financial markets shocks and risk measures.

Shocks refer to daily information states of changes in uncertainty or risk aversion. Risk measures refer to daily information states of the magnitude of uncertainty and risk aversion. Unlike economic trends or balance sheets, these data are mainly based on market prices. However, they have macro implications. Examples include realized return volatility across asset classes, volatility risk premia, and tail risk premia.

For the purpose of building trading factors shocks and risk measures are often complementary building blocks to economic information. For example, currency areas with weak external and financial balance sheets are often more susceptible to rising risk metrics in global credit and commodity markets.

Indicators are organized in categories, i.e. panels of one type of indicator over as many currency areas or markets as are available. Then the categories are grouped by similarity and each group is presented in a notebook.

The notebooks define and document the categories, describe their panels of time series, and provide some examples to illustrate their relevance for trading and algorithmic strategies. Most importantly, the notebooks are downloadable and can be used as a basis for exploring the respective categories interactively and relating them to generic financial returns with a few lines of Python code. All notebooks use the Macrosynergy Python package of standard functions for downloading, plotting, and analyzing data in standard JPMaQS format.