Impact on trading of defaultable claims of risk aversion and heterogeneity of belief

Investors’ risk aversion may influence asset prices and trading volumes, according to market observations. Investors’ low risk appetite and market frictions generally widen bid-ask spreads and restrict liquidity for assets and derivatives during periods of market turmoil and crises. Credit default swaps (CDS) notional has declined from US$42 trillion in 2008 to US$25 trillion in 2012 in the over-the-counter (OTC) market1. We use utility-indifference valuation methods to establish the pricing rules in order to reflect the investor’s risk preferences and subjective market perception, which are absent in the traditional no-arbitrage pricing framework. As a result, the investor’s valuation is influenced not only by his or her risk aversion and market outlook, but also by credit risk and the hedging instruments deployed. The utility-indifference pricing method has been used to value credit derivatives, among other things. A utility-based trading method for defaultable claims in markets with various pricing rules and agents with varying risk aversions and market perspectives. We obtain formulas for the buyer’s and seller’s pricing rules as well as optimal trading positions for both defaultable bonds and CDS using exponential utility. These findings aid in our understanding of market pricing and risk aversion, as well as market outlook and trade volume . Most notably, belief heterogeneity and zero-risk aversion prices are critical factors in determining whether a buyer will deal with any seller and vice versa.     Other financial derivatives, such as stock options, volatility derivatives, and insurance products, can be studied using our approach. Consider the best time to buy or sell assets or derivatives as an extension of the static trading problem (see [16]). Explicit formulas for the buyer’s and seller’s indifference prices, if provided, can substantially simplify the analysis in these cases. As long as the model is amenable to analysis and numerically tractable, conclusions from risk measures, prospect theory, and other utility maximisation methodologies can be applied to the investor’s portfolio optimization problem to construct alternative pricing rules.