The Law of Regression to the Tail: How to Mitigate COVID-19, Climate Change, and Other Catastrophic Risks
Bent Flyvbjerg (University of Oxford)
13 May 2020
Regression to the mean is nice and reliable, regression to the tail is reliably scary. We live in the age of regression to the tail. It is only a matter of time until a pandemic worse than Covid-19 will hit us, and climate more extreme than any we have seen so far. What are the basic principles for navigating such risks, for government, business, and the public?

Nowcasting Tail Risks to Economic Activity with Many Indicators
Andrea Carriero (Bocconi University), et al.
11 May 2020
This paper focuses on tail risk nowcasts of economic activity, measured by GDP growth, with a potentially wide array of monthly and weekly information. We consider different models (Bayesian mixed frequency regressions with stochastic volatility, classical and Bayesian quantile regressions, quantile MIDAS regressions) and also different methods for data reduction (either the combination of forecasts from smaller models or forecasts from models that incorporate data reduction). The results show that classical and MIDAS quantile regressions perform very well in-sample but not out-of-sample, where the Bayesian mixed frequency and quantile regressions are generally clearly superior. Such a ranking of methods appears to be driven by substantial variability over time in the recursively estimated parameters in classical quantile regressions, while the use of priors in the Bayesian approaches reduces sampling variability and its effects on forecast accuracy. From an economic point of view, we find that the weekly information flow is quite useful in improving tail nowcasts of economic activity, with initial claims for unemployment insurance, stock prices, a term spread, a credit spread, and the Chicago Fed’s index of financial conditions emerging as particularly relevant indicators. Additional weekly indicators of economic activity do not improve historical forecast accuracy but do not harm it much, either.

Short Term Trading Models – Mean Reversion Trading Strategies and the Black Swan Events
Mark Babayev (WorldQuant University), et al.
15 February 2020
This research analyzed the effectiveness of Black Swan strategies for the Short-Term Mean-Reversion systems, the risks and rewards profiles of such betting systems based on the S&P500 index. In determining the Black Swan events, the research made use of multiple strategies against two portfolios. By utilizing the python notebooks, signals created by the Black Swan and Bollinger Bands trading strategies were compared for performance against the baseline index (buy-and-hold strategy). This was followed by a validation of how risk mitigation techniques like the stop-loss affect the trading performance. The research concluded that it is possible to construct a Mean-Reverse strategy that outperforms the market over time.

Inflation at Risk
J. David López-Salido and Francesca Loria (Federal Reserve System)
February 2020
We investigate how macroeconomic drivers affect the predictive inflation distribution as well as the probability that inflation will run above or below certain thresholds over the near term. This is what we refer to as Inflation-at-Risk–a measure of the tail risks to the inflation outlook. We find that the recent muted response of the conditional mean of inflation to economic conditions does not convey an adequate representation of the overall pattern of inflation dynamics. Analyzing data from the 1970s reveals ample variability in the conditional predictive distribution of inflation that remains even when focusing on the post-2000 period of stable and low mean inflation. We also document that in the United States and in the Euro Area tight financial conditions carry substantial downside inflation risks, a feature overlooked by much of the literature. Our paper offers a new empirical perspective to existing macroeconomic models, showing that changes in credit conditions are also key to understand the dynamics of the inflation tails.

Asymmetry, Tail Risk and Time Series Momentum
Zhenya Liu (Renmin University of China), et al.
11 April 2020
Similar to the cross-sectional momentum crashes, the time series momentum experiences deep and persistent drawdowns in the stressed time of slumps in the upward momentum, rebounds in the downward momentum, and long time sideways market. We measure the upside and downside risk using the upper and lower partial moments, which are derived from the individual asset’s daily return. The time series momentum reversals are partly forecasted by the asymmetric structure of the tail-distributed upside and downside risk. An implementable systematic rule-based decision function is designed to manage the signals given by the time series momentum. Its empirical application on the Chinese commodity futures markets documents improvements in terms of both the Sharpe ratio and the Sortino ratio from 2008 to 2019. These results are robust across the time series momentum with different looking back windows.

Learn To Use R For Portfolio Analysis
Quantitative Investment Portfolio Analytics In R:
An Introduction To R For Modeling Portfolio Risk and Return
By James Picerno