A new approximation for the risk premium with large risks

PurposeThe Arrow–Pratt approximation to the risk premium is only valid for small risks. In this paper we consider a second approximation, based on risk-neutral probabilities and which requires no greater information than the Arrow–Pratt approximation, that works well for both small and large risks.Design/methodology/approachThe paper is theoretical in nature, although it also provides illustrative numerical simulations.FindingsThe new approximation proposed here appears to be significantly superior to Arrow–Pratt for approximating the true value of the risk premium when the risk is large. It may also approximate better even for relatively small risks.Originality/valueAs far as we are aware, there are no other known approximations for the risk premium when the risk involved is large.

Additive logistic processes in option pricing

In option pricing, it is customary to first specify a stochastic underlying model and then extract valuation equations from it. However, it is possible to reverse this paradigm: starting from an arbitrage-free option valuation formula, one could derive a family of risk-neutral probabilities and a corresponding risk-neutral underlying asset process. In this paper, we start from two simple arbitrage-free valuation equations, inspired by the log-sum-exponential function and an $\ell ^{p}$ ℓ p vector norm. Such expressions lead respectively to logistic and Dagum (or “log-skew-logistic”) risk-neutral distributions for the underlying security price. We proceed to exhibit supporting martingale processes of additive type for underlying securities having as time marginals two such distributions. By construction, these processes produce closed-form valuation equations which are even simpler than those of the Bachelier and Samuelson–Black–Scholes models. Additive logistic processes provide parsimonious and simple option pricing models capturing various important stylised facts at the minimum price of a single market observable input.