We propose an extension of the -OU Barndorff-Nielsen and Shephard model taking into account jump clustering phenomena. We assume that the intensity process of the Hawkes driver coincides, up to a constant, with the variance process. By applying the theory of continuous-state branching processes with immigration, we prove existence and uniqueness of strong solutions of the SDE governing the asset price dynamics. We propose a measure change of self-exciting Esscher type in order to describe the relation between the risk-neutral and the historical dynamics, showing that the -OU Hawkes framework is stable under this probability change. By exploiting the affine features of the model we provide an explicit form for the Laplace transform of the asset log-return, for its quadratic variation and for the ergodic distribution of the variance process. We show that the proposed model exhibits a larger flexibility in comparison with the -OU model, in spite of the same number of parameters required. We calibrate the model on market vanilla option prices via characteristic function inversion techniques, we study the price sensitivities and propose an exact simulation scheme. The main financial achievement is that implied volatility of options written on VIX is upward shaped due to the self-exciting property of Hawkes processes, in contrast with the usual downward slope exhibited by the -OU Barndorff-Nielsen and Shephard model.

We study a financial market where the risky asset is modelled by a geometric Itô-Lévy process, with a singular drift term. This can for example model a situation where the asset price is partially controlled by a company which intervenes when the price is reaching a certain lower barrier. See e.g. Jarrow and Protter (J Bank Finan 29:2803-2820, 2005) for an explanation and discussion of this model in the Brownian motion case. As already pointed out by Karatzas and Shreve (Methods of Mathematical Finance, Springer, Berlin, 1998) (in the continuous setting), this allows for arbitrages in the market. However, the situation in the case of jumps is not clear. Moreover, it is not clear what happens if there is a delay in the system. In this paper we consider a jump diffusion market model with a singular drift term modelled as the local time of a given process, and with a delay in the information flow available for the trader. We allow the stock price dynamics to depend on both a continuous process (Brownian motion) and a jump process (Poisson random measure). We believe that jumps and delays are essential in order to get more realistic financial market models. Using white noise calculus we compute explicitly the optimal consumption rate and portfolio in this case and we show that the maximal value is finite as long as . This implies that there is no arbitrage in the market in that case. However, when goes to 0, the value goes to infinity. This is in agreement with the above result that is an arbitrage when there is no delay. Our model is also relevant for high frequency trading issues. This is because high frequency trading often leads to intensive trading taking place on close to infinitesimal lengths of time, which in the limit corresponds to trading on time sets of measure 0. This may in turn lead to a singular drift in the pricing dynamics. See e.g. Lachapelle et al. (Math Finan Econom 10(3):223-262, 2016) and the references therein.

This paper aims to apply the real options game theoretic to study the impact of sudden events on the optimal investment timing and capacity choice in a duopoly market. We model the market demand and investment cost as the geometric Brownian motions with jumps driven by the Poisson processes. A new computing method independent on specific distribution functions is proposed for the real option models with jump processes of random frequency and amplitude. Based on this method, we find that both firms delay investment with a larger capacity as uncertainties of demand and investment cost increase. We also demonstrate that two firms both invest later and the optimal capacity relationship between them is ambiguous in the presence of two sources of uncertainty. Numerical simulation reveals that upward (downward) jump in demand and downward (upward) jump in investment cost cause the firms to invest earlier (later) with a larger (smaller) capacity. Finally, in a duopoly with symmetric firms, the first investor invests earlier than in an asymmetric duopoly due to the threat of preemption.

In a continuous-time market with a safe rate and a risky asset that pays a dividend stream depending on a latent state of the economy, several agents make consumption and investment decisions based on public information-prices and dividends-and private signals. If each investor has constant absolute risk aversion, equilibrium prices do not reveal all the private signals, but lead to the same estimate of the state of the economy that one would hypothetically obtain from the knowledge of all private signals. Accurate information leads to low volatility, ostensibly improving market efficiency, but also reduces each agent's consumption through a decrease in the price of risk. Thus, informational efficiency is reached at the expense of agents' welfare.

This article presents a model of the financial system as an inhomogeneous random financial network (IRFN) with nodes that represent different types of institutions such as banks or funds and directed weighted edges that signify counterparty relationships between nodes. The onset of a systemic crisis is triggered by a large exogenous shock to banks' balance sheets. Their behavioural response is modelled by a cascade mechanism that tracks the propagation of damaging shocks and possible amplification of the crisis, and leads the system to a cascade equilibrium. The mathematical properties of the stochastic framework are investigated for the first time in a generalization of the Eisenberg-Noe solvency cascade mechanism that accounts for fractional bankruptcy charges. New results include verification of a "tree independent cascade property" of the solvency cascade mechanism, and culminate in an explicit recursive stochastic solvency cascade mapping conjectured to hold in the limit as the number of banks goes to infinity. It is shown how this cascade mapping can be computed numerically, leading to a rich picture of the systemic crisis as it evolves toward the cascade equilibrium.

We develop a model for contagion risks and optimal security investment in a directed network of interconnected agents with heterogeneous degrees, loss functions, and security profiles. Our model generalizes several contagion models in the literature, particularly the independent cascade model and the linear threshold model. We state various limit theorems on the final size of infected agents in the case of random networks with given vertex degrees for finite and infinite-variance degree distributions. The results allow us to derive a resilience condition for the network in response to the infection of a large group of agents and quantify how contagion amplifies small shocks to the network. We show that when the degree distribution has infinite variance and highly correlated in- and out-degrees, even when agents have high thresholds, a sub-linear fraction of initially infected agents is enough to trigger the infection of a positive fraction of nodes. We also demonstrate how these results are sensitive to vertex and edge percolation (intervention). We then study the asymptotic Nash equilibrium and socially optimal security investment. In the asymptotic limit, agents' risk depends on all other agents' investments through an aggregate quantity that we call network vulnerability. The limit theorems enable us to capture the impact of one class of agents' decisions on the overall network vulnerability. Based on our results, the vulnerability is semi-analytic, allowing for a tractable Nash equilibrium. We provide sufficient conditions for investment in equilibrium to be monotone in network vulnerability. When investment is monotone, we demonstrate that the (asymptotic) Nash equilibrium is unique. In the specific example of two types of core-periphery agents, we illustrate the strong effect of cost heterogeneity on network vulnerability and the non-monotonous investment as a function of costs.

In a Mean Field Game (MFG) each decision maker cares about the cross sectional distribution of the state and the dynamics of the distribution is generated by the agents' optimal decisions. We prove the uniqueness of the equilibrium in a class of MFG where the decision maker controls the state at optimally chosen times. This setup accommodates several problems featuring non-convex adjustment costs, and complements the well known drift-control case studied by Lasry-Lions. Examples of such problems are described by Caballero and Engel in several papers, which introduce the concept of the generalized hazard function of adjustment. We extend the analysis to a general "impulse control problem" by introducing the concept of the "Impulse Hamiltonian". Under the monotonicity assumption (a form of strategic substitutability), we establish the uniqueness of equilibrium. In this context, the Impulse Hamiltonian and its derivative play a similar role to the classical Hamiltonian that arises in the drift-control case.