We present a new dynamic bank run model for liquidity risk where a financial institution finances its risky assets by a mixture of short- and long-term debt. The financial institution is exposed to insolvency risk at any time until maturity and to illiquidity risk at a finite number of rollover dates. We compute both insolvency and illiquidity default probabilities in this multiperiod setting using a structural credit risk model approach. Firesale rates can be determined endogenously as expected debt value over current asset value. Numerical results illustrate the impact of various input parameters on the default probabilities.

We propose a general, accurate and fast econometric approach for the estimation of affine option pricing models. The algorithm belongs to the class of Laplace-Type Estimation (LTE) techniques and exploits Sequential Monte Carlo (SMC) methods. We employ functions of the risk-neutral cumulants given in closed form to marginalize latent states, and we address parameter estimation by designing a density tempered SMC sampler. We test our algorithm on simulated data by tackling the challenging inference problem of estimating an option pricing model which displays two stochastic volatility factors, allows for co-jumps between price and volatility, and stochastic jump intensity. Furthermore, we consider real data and estimate the model on a large panel of option prices. Numerical studies confirm the accuracy of our estimates and the superiority of the proposed approach compared to its natural benchmark.