About Me
I’m an economics and finance researcher at Universita’ di Pisa with research interests spanning Cryptoeconomics, Asset Pricing, Banking and Computational Economics.
Until December 2022 I was a research associate in the Finance Department of Imperial College Business School.
In 2022 I was a consultant for the European Central Bank in the Directorate General Macroprudential Policy and Financial Stability.
I obtained my Ph.D. in Mathematics at Imperial College.
Contacts
Twitter: @marcofrancisc
Linkedin: Marco Francischello
Selected Works
For a complete list of my publications see my Google Scholar
Work In Progress
Restaking is not Rehypotecation with Tarun Chitra
📄 Poster
Abstract
Restaking describes a set of mechanisms for using assets within one decentralized network to bootstrap another network.
Such networks have grown to have over $15 billion in assets since January 2024.
Another means for bootstrapping networks is rehypothecation, i.e. borrowing against assets locked in an old network and locking the borrowed assets within the new network.
Is restaking equivalent to rehypothecation?
In this paper, we describe the similarities and differences between restaking and leverage and show that their financial payoffs are distinct.
We study this difference via mean-variance portfolio theory and demonstrate that in positive risk-free rate environments, one cannot use leverage to replicate restaking payoffs. Our results demonstrate that restaking performs better when economic payoffs are less correlated and rehypothecation performs better when payoffs are more correlated.
Working Papers in Economics and Finance
Estimation and Application of Random Vector Fields: A Local Polynomial Regression Approach with Davide Fiaschi, Angela Parenti and Cristiano RIcci
🛠️ Draft available upon request
Abstract
This paper introduces a methodology for estimating a Random Vector Field (RVF), which characterises the local direction of movement for a sample of observational units in a d-dimensional space. We develop an estimator for RVF based on Local Polynomial Regression (LPR) and establish its asymptotic normality. Additionally, we propose a local significance test and introduce a methodology for optimal bandwidth selection. To address small sample bias, we explore the effectiveness of the Adaptive Kernel (AK) approach and inference via bootstrap. We illustrate the properties of our estimator by analysing the joint dynamics of GDP per capita and life expectancy — the so-called Preston Curve — for a sample of 105 countries from 1960 to 2015. Finally, we generate a forecast for 2045 based on the estimated RVF, demonstrating one of its key applications in economic and demographic analysis.
Wealth Inequality, Aggregate Risk, and the Equity Term Structure with Harjoat Bhamra and Clara Martínez-Toledano
🛠️ Draft available upon request
Abstract
This paper studies the feedback between stock market fluctuations and wealth inequality dynamics. We do so by means of a dynamic consumption-based general equilibrium model with endogenous asset returns and a non-degenerate wealth distribution for a continuum of households. Households are heterogeneous in risk aversion and thus choose different expected portfolio returns and portfolio return volatilities, generating time-varying wealth inequality. We show how to solve the model analytically in terms of a cumulant generating function, which encodes information about all the moments of the distribution of risk aversion.
With this result, we recover the unobservable distribution of risk aversion using time variation in the slope of the observable equity term structure. We also confront the model with US data on the wealth distribution to recover a second estimate of the distribution of risk aversion. By comparing the two estimates, we show quantitatively that there is significant feedback between stock price dynamics and wealth distribution dynamics.
Optimal Ramsey Taxation with Social Security with Francesco Del Prato and Matteo Paradisi
🛠️ Draft available upon request
Abstract
We develop an OLG model with heterogeneous agents and aggregate uncertainty to study optimal Ramsey taxation when the government can use a credible set of social security instruments. Social security mitigates the income effect in optimal labor tax smoothing and, together with heterogeneity, adds new redistributive motives to both labor and capital taxes while crowding out others. We calibrate the model on three different economies: the US, Netherlands, and Italy. We argue that the three countries would experience heterogeneous gains, in redistributive and efficiency terms, by moving from the status-quo allocations to those prescribed by a utilitarian Ramsey planner. Our simulations show that retirement benefits in the current economies are higher than their Ramsey-optimal level while we argue that the use of funded social security schemes, neglected in current actual policies, could be welfare improving.
Mathematical Finance Articles
Nonlinear Valuation under Credit, Funding, and Margins: Existence, Uniqueness, Invariance, and Disentanglement with Damiano Brigo and Andrea Pallavicini
Abstract
Since the 2008 global financial crisis, the banking industry has been using valuation adjustments to account for default risk and funding costs. These adjustments are computed separately and added together by practitioners as if the valuation equations were linear. This assumption is too strong and does not allow to model market features such as different borrowing and lending rates and replacement default closeout. Hence we argue that the full valuation equations are nonlinear, and this paper is devoted to studying the nonlinear valuation equations introduced in Pallavicini et al (2011).
We illustrate all the cash flows exchanged by the parties involved in a derivative contract, in presence of default risk, collateralisation with re-hypothecation and funding costs. Then we show how to obtain semi-linear PDEs or Forward Backward Stochastic Differential Equations (FBSDEs) from present-valuing said cash flows in an arbitrage-free setup, and we study the well-posedness of these PDEs and FBSDEs in a viscosity and classical sense.
Moreover, from a financial perspective, we discuss cases where classical valuation adjustments (XVA) can be disentangled. We show how funding costs are offset by treasury valuation adjustments when one takes a whole-bank perspective in the valuation, while the same costs are not offset by such adjustments when taking a shareholder perspective. We show that although we use a risk-neutral valuation framework based on a locally risk-free bank account, our final valuation equations do not depend on the risk-free rate. Finally, we show how to consistently derive a netting set valuation from a portfolio level one.
Impact of multiple curve dynamics in credit valuation adjustments under collateralization with Giacomo Bormetti, Damiano Brigo, and Andrea Pallavicini
Abstract
We present a detailed analysis of interest rate derivatives valuation under credit risk and collateral modeling. We show how the credit and
collateral extended valuation framework presented in Pallavicini et al. 2011, and the related collateralized valuation measure, can be helpful in defining the key market rates underlying the multiple interest rate curves that characterize current interest rate markets. A key point is that spot Libor rates are to be treated as market primitives rather than being defined by no-arbitrage relationships. We formulate a consistent realistic dynamics for the different rates emerging from our analysis and compare the resulting model performances to simpler models used in the industry. We include the often neglected margin period of risk, showing how this feature may increase the impact of different rates dynamics on valuation. We point out limitations of multiple curve models with deterministic basis considering valuation of particularly sensitive products such as basis swaps. We stress that a proper wrong way risk analysis for such products requires a model with a stochastic basis and we show numerical results confirming this fact.