Time: 13:00-15:00 (UK Time), Wednesday, 30 November 2022
Presenter: Dr. G. Charles-Cadogan, University of Leicester
Chair: Prof. Kemi Yekini, SOAS University of London
Abstract
We conduct a natural experiment in which US Federal Reserve money supply M1SL (``M1SL") response to the Great Recession of 2008 is a control, and its M1SL response to the exogenous COVID19 pandemic event is a treatment for the Great Lockdown of 2020. Both recession periods are matched on market crash from rare disaster, almost identical VIX scores, similar jumps in risk aversion, similar U-shape pattern in US Treasury yield curves, and similar intertemporal marginal rate of substitution (IMRS) behaviour. The main difference is the Fed's unprecedented M1SL treatment of the Great Lockdown compared to the Great Recession of 2008 (and other rare disasters). We introduce a novel time varying stochastic discount factor (SDF) which admits explosions based on fear of catastrophic loss in financial markets and it disentangles risk aversion from fear of loss. We find that even though both rare disasters were matched on risk attitudes and consumption behaviour, the unprecedented increase in M1SL treatment during the Great Lockdown is aliasing for fear of loss like that observed during the Great Recession. The treatment is tantamount to latent risk substitution that attenuated the SDF during the V-shaped Great Lockdown recession. By comparison, there was little risk substitution in M1SL response to the explosive SDF during the control period of prolonged U-shaped Great Recession. We find that time varying consumption beta and time varying implied risk aversion each exhibit heterogenous responses to rare disasters. In contrast, each exhibited homogenous response to quantitative easing (QE). We priced rare disasters and found that the causal effect of QE during the Great Lockdown induced speculators to demand a 2% increase in the equity premium (ERP). In contrast, QE failed to stop a net loss of -1% in the ERP during the Great Recession. 70\% of the variation in credit spreads are explained by rare disasters--each of which contributes equally to the spread. Pre-pandemic rare disasters accounted for 7% of the variability in M1SL. Inclusion of pandemic disaster caused a 78% jump to 85% for the variation in M1SL. Loss aversion was 7 times greater during the Great Recession QE window compared to the Great Lockdown QE window.
Presenter
Dr. Charles-Cadogan is a lecturer in the Division of Finance, School of Business, University of Leicester. Charles-Cadogan research interests include behavioural decision theory, and probability and stochastic processes with applications to finance. His risk torsion concept paper was voted by Money Science as one of the top innovative papers; and his modified Sharpe Ratio statistic for high frequency traders’ performance evaluation was recommended for practitioners. He was awarded a “Best Paper Prize” at the International Risk Management Conference sponsored by NYU Stern and EU Joint Research Centre for his innovations in estimating risk attitudes during unconventional monetary policy response to rare disasters. He is a referee for top journals in mathematics, finance and economics, and he has been invited to present his work globally. His current research activities include construction of a credit risk index with myopic loss aversion to credit default; perceptions of risk, penalized reference dependent utility functions, representation theory of risk attitudes, Lie group structure of risk attitudes, econometric tests for factor timing portfolios, machine learning with big data in finance, compensating risk premium for banks in underserved communities, managerial compensation contracts in revolving doors between government and industry; large deviation probability estimates for irrational exuberance in behavioural dynamical systems and financial market instability. He is lead editor for Frontier in Applied Mathematics and Statistics – Mathematical Finance special issue on Machine Learning and Factor Pricing in Finance. He holds a PhD in Statistical Economics, and he is a Fellow of the Higher Education Academy (FHEA) and Fellow of the Royal Statistical Society (FRSS). Further details are available on his website.