Financial Consulting
My professional background combines front-office trading expertise with a system-level understanding of how derivatives interact with risk, capital, liquidity, and regulation.
At JPMorgan, my trading experience spanned Fixed Income (IR swaps/options, exotics, and ABS), Structured Products, and Credit. Over time, I became particularly focused on the measurement, management, and optimisation of second-order effects — including counterparty risk, liquidity, capital, embedded funding, differential discounting, margin, and the various Basel rules.
Effective optimisation of these drivers can deliver trading efficiencies and cost reductions many multiples of bid-offer spreads. More importantly, these second-order effects can be proactively mined to identify backwardation opportunities across dealers who are at different stages of maturity in their management and pricing of these dynamics.
Value I Bring
- Technical & Systems Expertise
- Extensive experience designing and implementing industry-leading systems to measure, manage, and optimise XVA, funding, and capital effects at scale.
Strategic & Commercial Perspective
I identify and structure opportunities that:
- Enable banks to reduce the cost of regulatory, funding, and capital constraints.
- Help buy-side firms understand and exploit dealer pricing inefficiencies — structuring trades to mitigate costs or enhance value.
Potential Roles & Engagements
Within a hedge fund, asset manager, or clearing member, I could:
- Establish a centralised optimisation framework to distribute trading intelligently — maximising market capacity while minimising XVA and RWA footprints.
- Serve as Chief Risk Officer, Market Risk Head, or Strategic Advisor on derivative risk and capital optimisation.
Within a hedge fund, asset manager, or clearing member, I could:
- Design or enhance frameworks for CVA desks, funding-risk mark-to-market, OIS discounting, and capital valuation adjustments (KVA) — leveraging direct experience implementing these globally.
Career Highlights
- JPMorgan: Led a transformation of the Clearing and F&O business by introducing front-office risk management and pricing discipline to a historically “agency” business — doubling revenue while reducing capital, GSIB, and residual interest exposures.
- Contributed to JPMorgan winning Risk Magazine’s Best OTC Client Clearer award (2019 & 2021).
- Implemented FVA and KVA frameworks for the global derivatives franchise.
- Introduced single and multi-currency OIS discounting across the firm, establishing a centralised funding-risk utility.
Illustrative Trade Concepts
Below are examples of XVA-related and convexity-driven opportunities that illustrate the type of analysis and initiatives I focus on.
1. Collateral Optimisation / CSA Renegotiation / Differential Discounting (DD) Recognition
The idea of capturing value in trades through recognition of differential discounting (DD) and eligible collateral assets — first highlighted during the transition from LIBOR to OIS discounting — has been well understood since around 2009–10, when one major Wall Street bank reportedly generated a substantial portion of its FICC revenue by optimising for this effect. As the market matured, most dealers upgraded their systems to eliminate this arbitrage within their bilateral trading, crystallising significant costs for the slower movers.
However, this efficiency is still not universal. Trades “given-in” under Clearing Agreements are processed through post-trade approval mechanisms that are largely DD-agnostic and therefore remain open to arbitrage. When a client executes a trade, the Executing Broker (EB) typically prices it using the discount curve that reflects its relationship with the Clearing Agent (CA). These curves differ not only between EBs but also from the DD curve the CA applies in its own relationship with the client.
As a result, the transaction can effectively be priced on the “wrong” discount curve, creating systematic valuation mismatches. For trades with large funding deltas, this misalignment can lead to backwardation opportunities, where clients can capture value by recognising and exploiting discrepancies in how discounting conventions are applied across different market participants.
2. CVA Mining and IM Optimisation – Establishing a Central XVA Hub
For any hedge fund, asset manager, or corporate with uncollateralised derivative exposure, there is often considerable latent value embedded in how banks manage their CVA reserves and Initial Margin (IM) requirements. A centralised function — an XVA Hub — can systematically identify and extract this value by managing exposures and counterparty relationships holistically rather than on a trade-by-trade basis.
The concept of CVA mining arises because each dealer maintains credit reserves calibrated to its own exposure profile and counterparty limits. Where a buy-side participant has offsetting credit or risk deltas across multiple banks, it can effectively “mine” these reserves — freeing up or rebalancing exposures to reduce aggregate CVA charges and associated funding costs.
A similar principle applies to IM optimisation under the Non-Cleared Margin Rules (NCMR). The $50 million uncollateralised threshold applies per counterparty group, not in aggregate — meaning that intelligently redistributing trades among multiple dealers can materially lower total IM requirements. For example, with a cost of debt of 5%, distributing risk evenly across five dealers could save around $12.5 million per year in avoided funding drag (5 × $50 million × 5%).
Further efficiencies can be achieved by transforming cleared exposures into bilateral ones through put and call swaptions that replicate the interest-rate delta of cleared swaps. Because IM on these bilateral structures can be bilaterally negotiated and offset, this approach can, in effect, provide free funding for part of the portfolio.
Several large hedge funds have already implemented this concept, establishing centralised hubs to manage their XVA footprint across all dealer relationships — minimising unnecessary credit costs, margin consumption, and funding inefficiencies. For smaller or less systemically connected institutions, adopting a similar hub-and-spoke framework can create substantial and recurring cost advantages while improving transparency and control of balance-sheet usage.
3. X-Gamma DCU Trade
Beyond conventional XVA opportunities, there exists an under-recognised class of convexity effects arising from the interaction between currency basis risk and OIS discounting risk. While the market often treats these as orthogonal, they are in fact correlated through funding behaviour and liquidity stress transmission — creating what might be described as funding wayness, analogous to wrong-way credit risk.
In practice, non-USD discounting curves are a function of both the OIS–LIBOR spread and the currency basis. When a portfolio’s mark-to-market — and therefore its funding delta — depends on either or both of these drivers, a second-order sensitivity emerges that behaves like a form of discounting convexity. This convexity is not properly captured in most market pricing models, meaning that current valuations often omit a significant source of potential P&L asymmetry.
By constructing offsetting exposures across OIS discounting and cross-currency basis, one can create positions where funding deltas move favourably in both market directions, generating a positive annuity flow regardless of the short-term directional outcome. In other words, it is possible to capture value from the non-linear interaction between funding and discounting curves — an opportunity that remains largely unexploited because most participants treat these risk factors as independent.
4. Contingent CCP Trades
In the event of a default of a clearing member, central counterparties (CCPs) auction off the defaulting member’s portfolio to return the CCP to a flat risk position. Typically, CCPs require all non-defaulting members to bid on every contract within the portfolio as a single package so that bids are directly comparable. There is no “menu” of optional bids — and any member that fails to bid risks its default-fund contribution being used to absorb losses.
The problem is that large, complex portfolios invariably contain contracts that some non-defaulting members cannot or will not price or trade. As a result, many bids are incomplete, causing auctions to fail and exposing the CCP to loss.
Contingent Bidder for FCMs
One potential solution is a contingent bidder model, where a third-party trading entity provides conditional bids for those contracts where FCMs lack capacity. These bids could be structured as conservatively priced credit-contingent agreements, providing a valuable defensive tool for clearing members while offering attractive asymmetric trading opportunities for the contingent participant.
CCP Auction Participation
Alternatively, a hedge fund could participate directly in CCP auctions as a liquidity provider. However, the greater opportunity may lie in acting as the “plug” that completes the aggregate bid package of the major dealers — improving auction efficiency while gaining access to the widest bid-offer spreads available in the market. A CCP auction following a large default represents one of the most concentrated trading opportunities that could ever arise — yet the market remains poorly prepared for it.
In Summary
I bring a blend of front-office trading experience, deep risk-infrastructure knowledge, and strategic insight into how regulation and funding mechanics shape market behaviour.
My goal is to help institutions unlock value from complexity — optimising capital, managing XVA exposures, and identifying structural sources of convexity and inefficiency across the derivatives landscape.
If you would like to discuss potential collaborations, advisory roles, or implementation of any of the ideas above, I would welcome the conversation.