An algorithm exploiting episodes of inefficient asset pricing to derive a macro-foundation scaled metric for systemic risk: A time-series Martingale representation

This paper employs an event study, the Global Financial Crisis. Episodes of inefficient pricing, the externality, are exploited as a measure of systemic risk. The theoretical asset pricing model, the martingale representation, is shown to be a valid algorithm to identify episodes of efficient and inefficient pricing in time series. Systemic risk metrics are derived from episodes of inefficient pricing, utilizing a shadow volatility metric. The algorithm is forward looking, deriving macro-foundation metrics from actual agent market behavior. The algorithm provides precise risk metrics for magnitude and diffusion using US and Canadian treasury markets. Given the US dollar’s role as the de-facto world reserve currency, scaled metrics derived from the US treasury market provide a globalized systemic benchmark. The risk metrics signal the crisis buildup and calibrate around the crisis epicenter date of September 2008. The risk metrics are heuristically consistent with the stylized facts of financial crises and support the extraordinary US policy response to the crisis. The algorithm output is validated by time-series analysis.
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An algorithm exploiting episodes of inefficient asset pricing to derive a macro-foundation scaled metric for systemic risk: A time-series Martingale representation