Author: Quantifiable

Capital Valuation Adjustment (KVA) adjusts a derivative’s value to reflect the cost of holding capital for regulatory compliance and to cover exposure risk. It captures the ongoing capital commitment throughout the position’s life. Future costs are discounted, accounting for the time value of money.

Margin Valuation Adjustment (MVA) reflects the cost of financing initial and variation margin requirements. Initial margin is the upfront cash covering potential exposure, while variation margin keeps margin levels aligned throughout the trade. As collateral values fluctuate, MVA captures the cost of funding these adjustments.

Collateral Valuation Adjustment (ColVA) adjusts a derivative’s value for counterparty default risk, factoring in the quality and liquidity of collateral. In a perfectly collateralized trade, no adjustment is necessary. The out-of-the-money party posts collateral without earning interest, while the in-the-money party receives collateral and may earn interest.

Funding Valuation Adjustment (FVA) reflects the cost of funding an uncollateralized OTC derivative. For in-the-money positions, the funding cost mirrors the acquirer’s cost of capital. For out-of-the-money positions, the funding benefit arises from reduced exposure, lowering the capital required.

Bilateral Valuation Adjustment (BVA) adjusts a derivative’s value to reflect the credit risk of both counterparties. To ensure fairness, each party’s adjustment must factor in both its own risk and the other party’s. If these adjustments don’t align, the trade is exposed to counterparty risk — a gap that could lead to significant financial loss.

Credit Valuation Adjustment (CVA) captures the potential loss from a counterparty default, adjusting a derivative’s value to account for credit risk. In data asset trading, settlement risk emerges, demanding inclusion in the price. This risk is priced through expected loss, adjusted for time and market conditions. How much is it worth to offload this risk from your portfolio?

Fair pricing of traded data assets hinges on pricing relationships. When derivatives come into play, pricing must factor in the costs of hedging counterparty risk, covering margin requirements, and holding regulatory capital. These essential adjustments, known as xVA, are at the heart of banking operations.

The power of a particular fact is a difference in the validity of outcomes that depends on the fact’s availability for valuation. This difference extends into agenda setting, electioneering, and framing. If a data commons hides a voting paradox, a single fact can make this difference. Our science foundation makes it for you.

Absence of transaction costs can overcome initially allocated access to confidential information. However, presence of transaction costs can make the price of such access diverge from its value. In these conditions, minorities can profit from their exclusion from the access, as you can sell your data assets to a majority.

Proxy, voting, voting trust, and derivative agreements can contractually allocate votes. These agreements rest on fair pricing methods, including dual-class share, synthetic share, controling block, and equity-lending methods. However, since ballot is defined by voting rule, voting power ultimately rests on electoral and agenda-setting methods.