1,721,061 research outputs found
A Relational Unsupervised Approach to Author Identification
No full textIn the last decades speaking and writing habits have changed.
Many works faced the author identification task by exploiting frequencybased
approaches, numeric techniques or writing style analysis. Following
the last approach we propose a technique for author identification
based on First-Order Logic. Specifically, we translate the complex data
represented by natural language text to complex (relational) patterns
that represent the writing style of an author. Then, we model an author
as the result of clustering the relational descriptions associated to the
sentences. The underlying idea is that such a model can express the typical
way in which an author composes the sentences in his writings. So,
if we can map such writing habits from the unknown-author model to
the known-author model, we can conclude that the author is the same.
Preliminary results are promising and the approach seems viable in real
contexts since it does not need a training phase and performs well also
with short texts
Optimal proportional reinsurance and investment for stochastic factor models
Full text linkIn this work we investigate the optimal proportional reinsurance-investment strategy of an insurance company which wishes to maximize the expected exponential utility of its terminal wealth in a finite time horizon. Our goal is to extend the classical Cramér–Lundberg model introducing a stochastic factor which affects the intensity of the claims arrival process, described by a Cox process, as well as the insurance and reinsurance premia. The financial market is supposed not influenced by the stochastic factor, hence it is independent on the insurance market. Using the classical stochastic control approach based on the Hamilton–Jacobi–Bellman equation we characterize the optimal strategy and provide a verification result for the value function via classical solutions to two backward partial differential equations. Existence and uniqueness of these solutions are discussed. Results under various premium calculation principles are illustrated and a new premium calculation rule is proposed in order to get more realistic strategies and to better fit our stochastic factor model. Finally, numerical simulations are performed to obtain sensitivity analyses. © 2019 Elsevier B.V
Optimal proportional reinsurance and investment for stochastic factor models
Full text linkIn this work we investigate the optimal proportional reinsurance-investment strategy of an insurance company which wishes to maximize the expected exponential utility of its terminal wealth in a finite time horizon. Our goal is to extend the classical Cramér–Lundberg model introducing a stochastic factor which affects the intensity of the claims arrival process, described by a Cox process, as well as the insurance and reinsurance premia. The financial market is supposed not influenced by the stochastic factor, hence it is independent on the insurance market. Using the classical stochastic control approach based on the Hamilton–Jacobi–Bellman equation we characterize the optimal strategy and provide a verification result for the value function via classical solutions to two backward partial differential equations. Existence and uniqueness of these solutions are discussed. Results under various premium calculation principles are illustrated and a new premium calculation rule is proposed in order to get more realistic strategies and to better fit our stochastic factor model. Finally, numerical simulations are performed to obtain sensitivity analyses
A BSDE-based approach for the optimal reinsurance problem under partial information
Full text linkWe investigate the optimal reinsurance problem under the criterion of maximizing the expected utility of terminal wealth when the insurance company has restricted information on the loss process. We propose a risk model with claim arrival intensity and claim sizes distribution affected by an unobservable environmental stochastic factor. By filtering techniques (with marked point process observations), we reduce the original problem to an equivalent stochastic control problem under full information. Since the classical Hamilton–Jacobi–Bellman approach does not apply, due to the infinite dimensionality of the filter, we choose an alternative approach based on Backward Stochastic Differential Equations (BSDEs). Precisely, we characterize the value process and the optimal reinsurance strategy in terms of the unique solution to a BSDE driven by a marked point process
Optimal reinsurance problem under fixed cost and exponential preferences
Full text linkWe investigate an optimal reinsurance problem for an insurance company taking into account subscription costs: that is, a constant fixed cost is paid when the reinsurance contract is signed. Differently from the classical reinsurance problem, where the insurer has to choose an optimal retention level according to some given criterion, in this paper, the insurer needs to optimally choose both the starting time of the reinsurance contract and the retention level to apply. The criterion is the maximization of the insurer’s expected utility of terminal wealth. This leads to a mixed optimal control/optimal stopping time problem, which is solved by a two-step procedure: first considering the pure-reinsurance stochastic control problem and next discussing a time-inhomogeneous optimal stopping problem with discontinuous reward. Using the classical Cramér–Lundberg approximation risk model, we prove that the optimal strategy is deterministic and depends on the model parameters. In particular, we show that there exists a maximum fixed cost that the insurer is willing to pay for the contract activation. Finally, we provide some economical interpretations and numerical simulations
Antibody-drug conjugates: resurgent anticancer agents with multi-targeted therapeutic potential
No full textAntibody-drug conjugates (ADCs) constitute a relatively new group of anticancer agents, whose first appearance took place about two decades ago, but a renewed interest occurred in recent years, following the success of anti-cancer immunotherapy with monoclonal antibodies. Indeed, an ADC combines the selectivity of a monoclonal antibody with the cell killing properties of a chemotherapeutic agent (payload), joined together through an appropriate linker. The antibody moiety targets a specific cell surface antigen expressed by tumor cells and/or cells of the tumor microenvironment and acts as a carrier that delivers the cytotoxic payload within the tumor mass. Despite advantages in terms of selectivity and potency, the development of ADCs is not devoid of challenges, due to: i) low tumor selectivity when the target antigens are not exclusively expressed by cancer cells; ii) premature release of the cytotoxic drug into the bloodstream as a consequence of linker instability; iii) development of tumor resistance mechanisms to the payload. All these factors may result in lack of efficacy and/or in no safety improvement compared to unconjugated cytotoxic agents. Nevertheless, the development of antibodies engineered to remain inert until activated in the tumor (e.g., antibodies activated proteolytically after internalization or by the acidic conditions of the tumor microenvironment) together with the discovery of innovative targets and cytotoxic or immunomodulatory payloads, have allowed the design of next-generation ADCs that are expected to possess improved therapeutic properties. This review provides an overview of approved ADCs, with related advantages and limitations, and of novel targets exploited by ADCs that are presently under clinical investigation
Unit-linked life insurance policies: Optimal hedging in partially observable market models
No full textIn this paper we investigate the hedging problem of a unit-linked life insurance contract via the local risk-minimization approach, when the insurer has a restricted information on the market. In particular, we consider an endowment insurance contract, that is a combination of a term insurance policy and a pure endowment, whose final value depends on the trend of a stock market where the premia the policyholder pays are invested. To allow for mutual dependence between the financial and the insurance markets, we use the progressive enlargement of filtration approach. We assume that the stock price process dynamics depends on an exogenous unobservable stochastic factor that also influences the mortality rate of the policyholder. We characterize the optimal hedging strategy in terms of the integrand in the Galtchouk Kunita Watanabe decomposition of the insurance claim with respect to the minimal martingale measure and the available information flow. We provide an explicit formula by means of predictable projection of the corresponding hedging strategy under full information with respect to the natural filtration of the risky asset price and the minimal martingale measure. Finally, we discuss applications in a Markovian setting via filtering. (C) 2017 Elsevier B.V. All rights reserved
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