International Journal of Theoretical and Applied Finance

The estimation of the multiplier parameter of portfolio insurance strategies is crucial for its implementation because it determines the risk exposure to the performance-seeking asset (PSA) at each point in time. Studies that address the estimation of the multiplier’s upper bound have been limited to strategies that use as the safe asset a short-term bank account, in which case the co-movements of the safe and the PSA become irrelevant. However, in several relevant applications, portfolio insurance strategies use stochastic reference assets different from cash, such as the control of active-risk relative to a benchmark, or insuring a minimum level of retirement income. We find that the implications of taking into account the assets’ co-movements in the multiplier estimation can be crucial. In Monte Carlo simulations the multiplier doubles in size across scenarios, and the strategy using the proposed approach presents stochastic dominance over the strategy that ignores the asset dependency structure.

This paper presents the Runge–Kutta–Legendre (RKL) finite difference scheme, allowing for an additional shift in its polynomial representation. A short presentation of the stability region, comparatively to the Runge–Kutta–Chebyshev scheme follows. We then explore the problem of pricing American options with the RKL scheme under the one factor Black–Scholes and the two factor Heston stochastic volatility models, as well as the pricing of butterfly spread and digital options under the uncertain volatility model, where a Hamilton–Jacobi–Bellman partial differential equation needs to be solved. We explore the order of convergence in these problems, as well as the option greeks stability, compared to the literature and popular schemes such as Crank–Nicolson, with Rannacher time-stepping.

This paper proposes an efficient method for calculating European option prices under local, stochastic, and fractional volatility models. Instead of directly calculating the density function of a target underlying asset, we replicate it from a simpler diffusion process with a known analytical solution for the European option. For this purpose, we derive six functions that characterize the density function of a diffusion process, for both the original and simpler processes and match these functions so that the latter mimics the former. Using the analytical formula, we then approximate the option price of the target asset. By comparison with previous works and numerical experiments, we show that the accuracy of our approximation is high, and the calculation is fast enough for practical purposes; hence, it is suitable for calibration purposes.

We consider the representation of forward entropic risk measures using the theory of ergodic backward stochastic differential equations in a jump-diffusion framework. Our paper can be viewed as an extension of the work considered by Chong et al. (2019) in the diffusion case. We also study the behavior of a forward entropic risk measure under jumps when a financial position is held for a longer maturity.

Risk-neutral default probabilities can be implied from credit default swap (CDS) market quotes. In practice, mid-CDS quotes are used as inputs, as their risk-neutral counterparts are not observable. We show how to imply risk-neutral default probabilities from bid and ask quotes directly by means of formulating the CDS calibration problem to bid and ask market quotes within the conic finance framework. Assuming the risk-neutral distribution of the default time to be driven by a Poisson process we prove, under mild liquidity-related assumptions, that the calibration problem admits a unique solution that also allows to jointly calculate the implied liquidity of the market.

We develop a contingent claim model to examine the interaction between financing and investment where equity holders decide when to default and debt holders decide when to liquidate as well as maximize the liquidation value. We show that if the debt holders maximize the residual value at liquidation, an increase in liquidation value increases the amount of debt issuance and investment quantity ex ante, delaying corporate investment. This relationship is based on the fact that an increase in the liquidation value decreases the credit spread of debt holders. These results fit well with those of existing empirical studies.