Stochastic Volatility for Lévy Processes

Three processes reflecting persistence of volatility are initially formulated by evaluating three Lévy processes at a time change given by the integral of a mean-reverting square root process. The model for the mean-reverting time change is then generalized to include non-Gaussian models that are solutions to Ornstein-Uhlenbeck equations driven by one-sided discontinuous Lévy processes permitting correlation with the stock. Positive stock price processes are obtained by exponentiating and mean correcting these processes, or alternatively by stochastically exponentiating these processes. The characteristic functions for the log price can be used to yield option prices via the fast Fourier transform. In general mean-corrected exponentiation performs better than employing the stochastic exponential. It is observed that the mean-corrected exponential model is not a martingale in the filtration in which it is originally defined. This leads us to formulate and investigate the important property of martingale marginals where we seek martingales in altered filtrations consistent with the one-dimensional marginal distributions of the level of the process at each future date.     

Inference Methods for Discretely Observed Continuous-Time Stochastic Volatility Models: A Commented Overview

In this paper an overview of inference methods for continuous-time stochastic volatility models observed at discrete times is presented. It includes estimation methods for both parametric and nonparametric models that are completely or partially observed in a variety of situations where the data might be nonlinear functions of the components of the model and/or contaminated with observation noise. In each case, the main reported methods are presented, making emphasis on underlying ideas, theoretical properties of the estimators (bias, consistency, efficient, etc.), and the viability of their implementation to solve actual problems in finance.     

American options: the EPV pricing model

Summary We explicitly solve the pricing problem for perpetual American puts and calls, and provide an efficient semi-explicit pricing procedure for options with finite time horizon. Contrary to the standard approach, which uses the price process as a primitive, we model the price process as the expected present value of a stream, which is a monotone function of a Lévy process. Certain processes exhibiting mean-reverting, stochastic volatility and/or switching features can be modeled this way. This specification allows us to consider assets that pay no dividends at all when the level of the underlying stochastic factor is too low, assets that pay dividends at a fixed rate when the underlying stochastic process remains in some range, or capped dividends.The authors are grateful to the anonymous referees for valuable comments and suggestions.     

云计算环境下的审计业务模式变革研究

云计算环境下的审计业务模式是未来审计信息化发展的必然趋势。在归纳了IT环境下审计业务模式演变并从IT和审计两个角度诠释“云审计”及其国内外研究应用现状的基础上,以审计业务流程为主线,从云服务与审计结合的技术场景、云计算环境下的审计业务流程实现步骤以及云计算应用所引起的审计业务风险变化三个方面出发,分析云计算环境下的审计业务模式变革,为掌握云计算应用所带来的审计业务流程变革、迎接云审计时代的到来提供理论基础。     

Business models and the managerial sensemaking process

This study examines the managerial sensemaking process around business models. Drawing on fieldwork, this study introduces a model to describe how managerial sensemaking occurs around business model development and use. This study shows that managerial sensemaking around business models occurs through a mutually co‐constituted process, a separate yet shared process, or a combination of them resulting from an interplay between sensemaking and sensegiving activities. To facilitate their sensemaking around business models, managers draw on several schemas. Over time, some schemas underlying business models remain unchanged, while others change in varying degrees.     

网络组织流程协同效应的生成机理研究

文章主要对网络组织流程协同效应的生成机理进行深入研究。鉴于学术界对网络组织流程没有统一的认识，文章在文献分析和逻辑推理的基础上，首先明确界定了网络组织流程的内涵，进而构建了网络组织流程协同效应的概念模型，最后应用多层次回归分析对所采集的数据进行实证分析，验证网络组织流程协同效应的研究假设。结果表明，网络中结点企业间的关系强度正向影响流程协同效应，而网络组织的治理机制在关系强度与流程协同之间起着调节作用，跨组织信息系统起着部分调节作用。该结论对于网络组织的治理实践具有一定的理论和现实意义。     

Computing the finite-time expected discounted penalty function for a family of Lévy risk processes

Ever since the first introduction of the expected discounted penalty function (EDPF), it has been widely acknowledged that it contains information that is relevant from a risk management perspective. Expressions for the EDPF are now available for a wide range of models, in particular for a general class of Lévy risk processes. Yet, in order to capitalize on this potential for applications, these expressions must be computationally tractable enough as to allow for the evaluation of associated risk measures such as Value at Risk (VaR) or Conditional Value at Risk (CVaR). Most of the models studied so far offer few interesting examples for which computation of the associated EDPF can be carried out to the last instances where evaluation of risk measures is possible. Another drawback of existing examples is that the expressions are available for an infinite-time horizon EDPF only. Yet, realistic applications would require the computation of an EDPF over a finite-time horizon. In this paper we address these two issues by studying examples of risk processes for which numerical evaluation of the EDPF can be readily implemented. These examples are based on the recently introduced meromorphic processes, including the beta and theta families of Lévy processes, whose construction is tailor-made for computational ease. We provide expressions for the EDPF associated with these processes and we discuss in detail how a finite-time horizon EDPF can be computed for these families. We also provide numerical examples for different choices of parameters in order to illustrate how ruin-based risk measures can be computed for these families of Lévy risk processes.     

Catastrophe futures and reinsurance contracts: An incomplete markets approach

We present a theoretical methodology for the pricing of catastrophe (CAT) derivatives with event‐dependent and non‐convex payoffs given the price of a CAT indexed futures contract. We do not assume a fully diversifiable CAT event risk, nor do we assume knowledge of the martingale probability measure beyond the futures price. We derive tight bounds on the contract value and present trading strategies exploiting the mispricing whenever the bounds are violated. We estimate the bounds of the reinsurance contract with data from hurricane landings in Florida. Our method is also applicable when there is no futures market but the price of a CAT‐indexed bond is available.