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Pricing Model for Data Assets in Investment-Consumption Framework with Ambiguity (with Xiaoshan Chen and Zhou Yang).
- Working Paper. [Abstract|SSRN]
+ submitted. [Abstract|SSRN]
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+ Data assets are data commodities that have been processed, produced, priced, and traded based on actual demand. Reasonable pricing mechanism for data assets is essential for developing the data market and realizing their value. Most existing literature approaches data asset pricing from the seller's perspective, focusing on data properties and collection costs, however, research from the buyer's perspective remains scarce. This gap stems from the nature of data assets: their value lies not in direct revenue generation but in providing informational advantages that enable enhanced decision-making and excess returns. This paper addresses this gap by developing a pricing model based on the informational value of data assets from the buyer's perspective. We determine data asset prices through an implicit function derived from the value functions in two robust investment-consumption problems under ambiguity markets via indifference pricing principle. By the existing research results, we simplify the value function, using mathematical analysis and differential equation theory, we derive general expressions for data assets price and explore their properties under various conditions. Furthermore, we derive the explicit pricing formulas for specific scenarios and provide numerical illustration to describe how to use our pricing model.
-
+
Arbitrage in Perpetual Contracts (with Min Dai and Linfeng Li).
- Working Paper. [Abstract|SSRN]
+ submitted. [Abstract|SSRN]
Perpetual contracts, designed to track the underlying price through a funding swap mechanism, have gained significant popularity in cryptocurrency markets. However, observed price discrepancies between perpetual contracts and the underlying asset cannot be explained solely by transaction fees. By examining the impact of the clamping function inherent in the funding swap mechanism -- an overlooked aspect in existing literature -- we derive model-free no-arbitrage bounds for perpetual contracts. Our findings reveal that these bounds persist as intervals even without transaction fees, due to the clamping function. Empirical analysis using two years of Binance data supports the validity of our proposed bounds.