Search arXivSearch

arXiv · 1510.07199

Coherent CVA and FVA with Liability Side Pricing of Derivatives

Abstract

This article presents FVA and CVA of a bilateral derivative in a coherent manner, based on recent developments in fair value accounting and ISDA standards. We argue that a derivative liability, after primary risk factors being hedged, resembles in economics an issued variable funding note, and should be priced at the market rate of the issuer's debt. For the purpose of determining the fair value, the party on the liability side is economically neutral to make a deposit to the other party, which earns his current debt rate and effectively provides funding and hedging for the party holding the derivative asset. The newly derived partial differential equation for an option discounts the derivative's receivable part with counterparty's curve and payable part with own financing curve. The price difference from the counterparty risk free price, or total counterparty risk adjustment, is precisely defined by discounting the product of the risk free price and the credit spread at the local liability curve. Subsequently the adjustment can be broken into a default risk component -- CVA and a funding component -- FVA, consistent with a simple note's fair value treatment and in accordance with the usual understanding of a bond's credit spread consisting of a CDS spread and a basis. As for FVA, we define a cost -- credit funding adjustment (CFA) and a benefit -- debit funding adjustment (DFA), in parallel to CVA and DVA and attributed to counterparty's and own funding basis. This resolves a number of outstanding FVA debate issues, such as double counting, violation of the law of one price, misuse of cash flow discounting, and controversial hedging of own default risk. It also allows an integrated implementation strategy and reuse of existing CVA infrastructure.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Wujiang Lou. 2015-10-25. Coherent CVA and FVA with Liability Side Pricing of Derivatives. https://arxiv.org/abs/1510.07199

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Design and pricing of a transparent parametric-modeled loss CAT bond: application to German windstorm

Catastrophe (cat) bonds overcome some lack of reinsurance by sourcing capacity from the wider capital markets. We present a new type of cat bond addressing the known trade-off between moral hazard and basis risk. As our main contributions we propose a trigger mechanism which is entirely transparent and simpler to evaluate compared to indemnity modeling techniques, as well as a methodology to price this cat bond. This is relevant for insurers and public authorities in a world where natural disasters are occurring with increasing frequency and severity due to climate change, but also for players willing to enter the cat bond market for whom the lack of transparency of this asset class has been a significant obstacle. Our trigger is derived from a cost random field which separates the physical hazard, a vulnerability function and the exposure. This allows the trigger to take a flexible form between parametric and modeled loss, in case exposure is taken into account. We present a case study based on historical windstorm events impacting Germany. Using wind speed data from historical storms, we fit a max-stable random field on a resolution which is standard in the reinsurance industry. The availability of industry loss and exposure data allows us to calibrate the vulnerability component to historical observations. Besides measuring the basis risk associated with our trigger, we perform a full model assessment and discuss numerical results.

q-fin.PR

Fundamentals of Perpetual Futures

Perpetual futures are the most popular cryptocurrency derivatives. Perpetuals offer leveraged exposure to their underlying without rollover or direct ownership. Unlike fixed-maturity futures, perpetuals are not guaranteed to converge to the spot price. To minimize the gap between perpetual and spot prices, long investors periodically pay shorts a funding rate proportional to this difference. We derive no-arbitrage prices for perpetual futures in frictionless markets and bounds in markets with trading costs. Empirically, deviations from these prices in crypto are larger than in traditional currency markets, comove across currencies, and diminish over time. An implied arbitrage strategy yields high Sharpe ratios.

q-fin.PR

A deep learning approach for pricing convertible bonds with path-dependent reset and call provisions

This paper develops a deep learning framework for pricing convertible bonds with path-dependent downward reset and issuer call provisions governed by rolling-window triggers. We formulate the valuation problem as a path-dependent partial differential equation (PPDE) that captures both the historical stock-price path and the evolution of the conversion price. Model-specific PPDEs are derived under GBM, CEV, and Heston dynamics. Under suitable conditions, we establish the existence and uniqueness of a piecewise viscosity solution linked by contractual transmission conditions at monitoring dates. For computation, we construct a fixed-grid backward dynamic programming scheme and approximate its conditional expectations using neural networks, with $L^2$ convergence to the exact fixed-grid recursion as the approximation errors vanish. An application to the China CITIC Bank Convertible Bond produces stable prices across the three models and close agreement with the LSMC benchmark, but outperforms in dimensional scaling. The results show that contractual provisions have a greater valuation effect than the choice of underlying dynamics. The call provision reduces the bond value by truncating upside gains, whereas the downward reset provision increases it under the benchmark specification because improved conversion terms dominate the effect of earlier redemption. Delta and Gamma obtained by automatic differentiation of smooth local network approximations closely agree with central finite-difference estimates. The framework provides a flexible approach to pricing and sensitivity analysis for convertible bonds with complex path-dependent provisions.

q-fin.PR