Public finance risk

Fiscal expansion was the logical response to the 2020 health and economic crisis. Alas, public deficit and debt ratios had already been historically high...

Systemic risk under non-conventional monetary policy

Central bank operations in the form of quantitative easing, qualitative easing, forward guidance and collateral policies wield great influence over market prices of risk....

Risk management shocks and price distortions

Risk management relies on statistical metrics that converge on common standards. These metrics can change drastically alongside market conditions. A risk management shock is...

Unproductive debt

Credit and related interest income have historically been viewed as service and related payment for lending productively. However, in a highly collateralized and risk-averse...

How central banks can take nominal rates deeply negative

The popular view that nominal interest rates have a natural zero lower bound has become obsolete in modern financial systems. It may be more...

Bayesian Risk Forecasting

Portfolio risk forecasting is subject to great parameter uncertainty, particularly for longer forward horizons. This simply reflects that large drawdowns are observed only rarely,...

The duration extraction effect

Under non-conventional monetary policy central banks influence financial markets through the “portfolio rebalancing channel”. The purchase of assets changes the structure of prices. A...

Tiered reserve systems

Negative monetary policy rates can undermine financial transmission, because they encourage cash hoarding and reduce the profitability of traditional banking. This danger increases with...

Signaling systemic risk

Systemic financial crises arise when vulnerable financial systems meet adverse shocks. A systemic risk indicator tracks the vulnerability rather than the shocks (which are...

How to estimate risk in extreme market situations

Estimating portfolio risk in extreme situations means answering two questions: First, has the market entered an extreme state? Second, how are returns likely to...

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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’...

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