Research articles for the 2020-08-09

COVID-19: What If Immunity Wanes?
M. Alper Çenesiz,Luís Guimarães

Using a simple economic model in which social-distancing reduces contagion, we study the implications of waning immunity for the epidemiological dynamics and social activity. If immunity wanes, we find that COVID-19 likely becomes endemic and that social-distancing is here to stay until the discovery of a vaccine or cure. But waning immunity does not necessarily change optimal actions on the onset of the pandemic. Decentralized equilibria are virtually independent of waning immunity until close to peak infections. For centralized equilibria, the relevance of waning immunity decreases in the probability of finding a vaccine or cure, the costs of infection (e.g., infection-fatality rate), and the presence of other NPIs that lower contagion (e.g., quarantining and mask use). In simulations calibrated to July 2020, our model suggests that waning immunity is virtually unimportant for centralized equilibria until at least 2021. This provides vital time for individuals and policymakers to learn about immunity against SARS-CoV-2 before it becomes critical.

Log-modulated rough stochastic volatility models
Christian Bayer,Fabian Andsem Harang,Paolo Pigato

We propose a new class of rough stochastic volatility models obtained by modulating the power-law kernel defining the fractional Brownian motion (fBm) by a logarithmic term, such that the kernel retains square integrability even in the limit case of vanishing Hurst index $H$. The so-obtained log-modulated fractional Brownian motion (log-fBm) is a continuous Gaussian process even for $H = 0$. As a consequence, the resulting super-rough stochastic volatility models can be analysed over the whole range $0 \le H < 1/2$ without the need of further normalization. We obtain skew asymptotics of the form $\log(1/T)^{-p} T^{H-1/2}$ as $T\to 0$, $H \ge 0$, so no flattening of the skew occurs as $H \to 0$.

New Models of ‘Intelligent Investing’ for the Post-Crisis Economy
Fenwick, Mark,Vermeulen, Erik P. M.
Is coronavirus accelerating the future? Will the crisis provide a tipping point that encourages corporations to promote socially desirable values? Will there be a wider recognition that a sole focus on profits and investors hurts both companies and society? Or, will we simply return to business-as-usual once the memory of the crisis fades?The financially driven corporate world has been losing its appeal over recent years and an anti-corporate sentiment has become more prevalent. There is a greater demand for better standards of corporate behavior and new metrics for judging corporate success. What is ironic is that corporations that embrace a more stakeholder-oriented purpose already outperform their peers when it comes to stock market returns. When thinking about rebuilding the economy post-crisis, this paper argues that investors need to be encouraged to take ‘intelligent risks’ that focus on stakeholder-oriented listed and non-listed companies.

Pricing foreseeable and unforeseeable risks in insurance portfolios
Weihong Ni,Corina Constantinescu,Alfredo Egídio dos Reis,Véronique Maume-Deschamps

In this manuscript we propose a method for pricing insurance products that cover not only traditional risks, but also unforeseen ones. By considering the Poisson process parameter to be a mixed random variable, we capture the heterogeneity of foreseeable and unforeseeable risks. To illustrate, we estimate the weights for the two risk streams for a real dataset from a Portuguese insurer. To calculate the premium, we set the frequency and severity as distributions that belong to the linear exponential family. Under a Bayesian setup , we show that when working with a finite mixture of conjugate priors, the premium can be estimated by a mixture of posterior means, with updated parameters, depending on claim histories. We emphasise the riskiness of the unforeseeable trend, by choosing heavy-tailed distributions. After estimating distribution parameters involved using the Expectation-Maximization algorithm, we found that Bayesian premiums derived are more reactive to claim trends than traditional ones.

Quantum computing for Finance: state of the art and future prospects
Daniel J. Egger,Claudio Gambella,Jakub Marecek,Scott McFaddin,Martin Mevissen,Rudy Raymond,Andrea Simonetto,Stefan Woerner,Elena Yndurain

This paper outlines our point of view regarding the applicability, state of the art, and potential of quantum computing for problems in finance. We provide an introduction to quantum computing as well as a survey on problem classes in finance that are computationally challenging classically and for which quantum computing algorithms are promising. In the main part, we describe in detail quantum algorithms for specific applications arising in financial services, such as those involving simulation, optimization, and machine learning problems. In addition, we include demonstrations of quantum algorithms on IBM Quantum back-ends and discuss the potential benefits of quantum algorithms for problems in financial services. We conclude with a summary of technical challenges and future prospects.

Stochastic leverage effect in high-frequency data: a Fourier based analysis
Imma Valentina Curato,Simona Sanfelici

We analyse the stochastic leverage effect, which is defined as the instantaneous correlation between the returns and their related volatility increments, in a stochastic volatility model set-up. We define a novel estimator of the effect which is based on a pre-estimation of the Fourier coefficients of the return and of the volatility process as defined in Malliavin and Mancino (2002). We establish the consistency of the estimator and we analyse its finite sample performance in the presence of microstructure noise contamination. When applying our methodology to real data, our results provide evidence of the presence of the stochastic leverage effect.

Stress testing and systemic risk measures using multivariate conditional probability
Tomaso Aste

The multivariate conditional probability distribution models the effects of a set of variables onto the statistical properties of another set of variables. In the study of systemic risk in a financial system, the multivariate conditional probability distribution can be used for stress-testing by quantifying the propagation of losses from a set of `stressing' variables to another set of `stressed' variables. In this paper I describe how to compute such conditional probability distributions for the vast family of multivariate elliptical distributions, and in particular for the multivariate Student-t and the multivariate Normal distributions. Measures of stress impact and systemic risk are proposed. An application to the US equity market illustrates the potentials of this approach.

The Short-Term Effect of the Paycheck Protection Program on Unemployment
Barraza, Santiago,Rossi, Martin,Yeager, Timothy J.
We study the short-term causal effect of the Paycheck Protection Program on unemployment. Using the 2019 density of Small Business Administration member bank offices in a county as an instrument for PPP loans originated in that county during April 2020, we find statistically and economically significant effects from the program on unemployment. Our results highlight the importance of this relief policy and the financial system infrastructure in preserving jobs during the COVID-19 crisis.