Research Review | 9 August 2024 | Crisis Risk

The CNN Fear and Greed Index as a Predictor of Us Equity Index Returns
Hugh Farrell and Fergal A. O’Connor (University College Cork)
July 2024
We assess whether the CNN “Fear and Greed” Index can be used to predict returns on equity indices and gold using hand-collected data. We find that the Fear and Greed Index Granger causes returns on the S&P 500, Nasdaq Composite and Russell 3000 in the first sample period (2011-2020), but not gold returns. Analysis from 2021–2024 indicates the Fear and Greed index Granger causes S&P 500 and Russell 3000 returns, but the relationship is weaker. No significant relationship is found between the VIX and stock indices, indicating that the Fear and Greed Index is a better predictor of equity returns.

Leverage Dynamics and Learning about Economic Crises
Artur Anschukov (Imperial College Business School), et al.
July 2024
Models of learning about economic crises generate risk premia that rise at the onset of a crisis but then fall as belief uncertainty fades. Yet, empirical risk premia remain elevated during crises. We resolve this tension with leverage dynamics generated by the impact of learning on optimal capital structure decisions within a representative agent consumption-based model. Optimal leverage creates a feedback effect: learning increases risk premia, thereby depressing prices and further raising leverage. We structurally estimate the model and show it closely matches the joint dynamics of consumption, equity risk premia, credit risk, and leverage, especially during crises.

Understanding the Connectedness between Traditional Assets and Green Cryptocurrencies During Crises
Nikolaos Kyriazis (University of Thessaly) and Shaen Corbet (Dublin City University)
June 2024
This research explores the dynamic interaction between conventional financial assets, the US dollar index, S&P 500, gold, and crude oil, and ten major green cryptocurrencies, focusing on spillover linkages and hedging capacities during the COVID-19 pandemic and the Russia-Ukraine conflict. Using daily data and the Quantile-Vector Autoregressive methodology, the study reveals significant spillover effects from green cryptocurrencies to traditional financial instruments. Algorand, Cardano, IOTA, Powerledger, and TRON are notably influential, with Powerledger being a consistent transmitter across both crises. Conventional assets, particularly gold, act as effective hedging tools, especially in bear markets. Crude oil is identified as the largest transmitter of spillover impacts during conflicts. The study highlights that green cryptocurrencies promoting trust, innovation, and renewable energy are better hedged by traditional investments than those focused on financial services, underscoring their risky yet rewarding potential during economic crises.

Central Banking Post Crises
Michael Kiley (Federal Reserve) and Frederic S. Mishkin (Columbia University)
May 2024
The world economy has experienced the largest financial crisis in generations, a global pandemic, and a resurgence in inflation during the first quarter of the 21st century, yielding important insights for central banking. Price stability has important benefits and is the responsibility of a central bank. Achieving price stability in a complex and uncertain environment involves a credible commitment to a nominal anchor with a strong response to inflation and pre-emptive leaning against an overheating economy. Associated challenges imply that central bank communication and transparency are key elements of monetary policy strategies and tactics. Crises have emphasized the role of central banks in promoting financial stability, as financial stability is key to achieving price and economic stability, but this role increases risks to independence. Goals for central banks other than price and economic stability, complemented by financial stability, can make it more difficult for them to stabilize both inflation and economic activity.

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