6 citations · 15 across the 10 of their papers we have counts for
12 papers
Machine-learning a family of solutions to an optimal pension investment problem
John Armstrong, Cristin Buescu, James Dalby +1
We use a neural network to identify the optimal solutions to a family of pension investment problems, where the parameters determining an investor's risk and consumption preference…
Optimal post-retirement investment under longevity risk in collective funds
John Armstrong, Cristin Buescu, James Dalby
We study the optimal investment problem for a homogeneous collective of individuals investing in a Black-Scholes model subject to longevity risk with Epstein--Zin preferences.…
Analytical valuation of vulnerable derivative claims with bilateral cash flows under credit, funding and wrong-way risk
Juan Jose Francisco Miguelez, Cristin Buescu
We study the problem of valuing and hedging a vulnerable derivative claim with bilateral cash flows between two counterparties in the presence of asymmetric funding costs, defaults…
Asymptotically Optimal Management of Heterogeneous Collectivised Investment Funds
John Armstrong, Cristin Buescu
A collectivised fund is a proposed form of pension investment, in which all investors agree that any funds associated with deceased members should be split among survivors. For thi…
Collectivised Pension Investment with Exponential Kihlstrom--Mirman Preferences
John Armstrong, Cristin Buescu
In a collectivised pension fund, investors agree that any money remaining in the fund when they die can be shared among the survivors. We give a numerical algorithm to compute the…
Collectivised Pension Investment with Homogeneous Epstein-Zin Preferences
John Armstrong, Cristin Buescu
In a collectivised pension fund, investors agree that any money remaining in the fund when they die can be shared among the survivors. We compute analytically the optimal investmen…
Collectivised Post-Retirement Investment
John Armstrong, Cristin Buescu
We quantify the benefit of collectivised investment funds, in which the assets of members who die are shared among the survivors. For our model, with realistic parameter choices, a…
Portfolio Optimization for Cointelated Pairs: SDEs vs. Machine Learning
Babak Mahdavi-Damghani, Konul Mustafayeva, Stephen Roberts +1
With the recent rise of Machine Learning as a candidate to partially replace classic Financial Mathematics methodologies, we investigate the performances of both in solving the pro…
Risk-neutral valuation under differential funding costs, defaults and collateralization
Damiano Brigo, Cristin Buescu, Marco Francischello +2
We develop a unified valuation theory that incorporates credit risk (defaults), collateralization and funding costs, by expanding the replication approach to a generality that has…
Funding, repo and credit inclusive valuation as modified option pricing
Damiano Brigo, Cristin Buescu, Marek Rutkowski
We take the holistic approach of computing an OTC claim value that incorporates credit and funding liquidity risks and their interplays, instead of forcing individual price adjustm…
An application of the method of moments to volatility estimation using daily high, low, opening and closing prices
Cristin Buescu, Michael Taksar, Fatoumata J. Koné
We use the expectation of the range of an arithmetic Brownian motion and the method of moments on the daily high, low, opening and closing prices to estimate the volatility of the…
Impact of the first to default time on Bilateral CVA
Damiano Brigo, Cristin Buescu, Massimo Morini
We compare two different bilateral counterparty valuation adjustment (BVA) formulas. The first formula is an approximation and is based on subtracting the two unilateral Credit Val…