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In this note, we revisit the innovative transform approach introduced by Cai et al. [Cai, N., Song, Y., Kou, S., 2015. A general framework for pricing Asian options under Markov processes] for accurately approximating the probability distribution of a weighted stochastic sum or time integral under general one-dimensional Markov processes. Since then, Song et al. [Song, Y., Cai, N., Kou, S., 2018. Computable error bounds of Laplace inversion for pricing Asian options] and Cui et al. [Cui, Z., Lee, C., Liu, Y., 2018. Single-transform formulas for pricing Asian options in a general approximation framework under Markov processes] have achieved an efficient reduction of the original double to a single transform approach. We move one step further by approaching the problem from a new angle and, by dealing with the main obstacle relating to the differentiation of the exponential of a matrix, we bypass the transform inversion. We highlight the benefit from the new result by means of some numerical examples.

In this paper, we aim to detect whether the instability of financial industry will restrict the global green evolution. To this end, by applying a sample dataset of 47 countries for the period 1996–2018, this study empirically examines the financial risk-green growth nexus, and also checks their regional heterogeneity as well as the impact of the financial crisis. We find that: (i) increased financial risks play an important role in restricting green growth across the globe; by implication, the instability of financial markets is an effective determinant hindering the global green evolution; (ii) significant regional heterogeneity exists in the financial risk-green growth nexus; only in regions with low green growth and high financial risks can financial risks affect green growth negatively; and (iii) the outbreak of the financial crisis in 2008 becomes a watershed in the impact of financial risks on green growth; after the crisis, the effective control of financial risks can facilitate the green economic recovery. Accordingly, some policy implications are put forward concerning the reform of the financial system and the establishment of early warning mechanism.

In this short communication, we present a new, simple control-variate Monte Carlo procedure for enhancing the evaluation accuracy of alternative reinsurance strategies that an insurance company might adopt.

In this paper, we recall actuarial and financial applications of sums of dependent random variables that follow a non-Gaussian mean-reverting process and contemplate distribution approximations. Our work complements previous related studies restricted to lognormal random variables; we revisit previous approximations and suggest new ones. We then derive moment-based distribution approximations for random sums attuned to Asian option pricing and computation of risk measures of random annuities. Various numerical experiments highlight the speed-accuracy benefits of the proposed methods.

International audience; Our understanding of radiation-induced cellular damage has greatly improved over the past few decades. Despite this progress, there are still many obstacles to fully understand how radiation interacts with biologically relevant cellular components, such as DNA, to cause observable end points such as cell killing. Damage in DNA is identified as a major route of cell killing. One hurdle when modeling biological effects is the difficulty in directly comparing results generated by members of different research groups. Multiple Monte Carlo codes have been developed to simulate damage induction at the DNA scale, while at the same time various groups have developed models that describe DNA repair processes with varying levels of detail. These repair models are intrinsically linked to the damage model employed in their development, making it difficult to disentangle systematic effects in either part of the modeling chain. These modeling chains typically consist of track-structure Monte Carlo simulations of the physical interactions creating direct damages to DNA, followed by simulations of the production and initial reactions of chemical species causing so-called “indirect” damages. After the induction of DNA damage, DNA repair models combine the simulated damage patterns with biological models to determine the biological consequences of the damage. To date, the effect of the environment, such as molecular oxygen (normoxic vs. hypoxic), has been poorly considered. We propose a new standard DNA damage (SDD) data format to unify the interface between the simulation of damage induction in DNA and the biological modeling of DNA repair processes, and introduce the effect of the environment (molecular oxygen or other compounds) as a flexible parameter. Such a standard greatly facilitates inter-model comparisons, providing an ideal environment to tease out model assumptions and identify persistent, underlying mechanisms. Through inter-model comparisons, this unified standard has the potential to greatly advance our understanding of the underlying mechanisms of radiation-induced DNA damage and the resulting observable biological effects when radiation parameters and/or environmental conditions change.

It is our pleasure to prologue the special issue on “Machine Learning in Insurance”, which represents a compilation of ten high-quality articles discussing avant-garde developments or introducing new theoretical or practical advances in this field [...]

This paper investigates the dynamics of stock price volatility for different vessel-type segments of the U. S, water transportation industry . We measure market exposure by a portfolio of tanker, dry bulk, container, and gas stocks to examine tail behavior and tail risk dependence. The role of mixture distributions in predicting future volatility is studied from both statistical and economic perspectives. We further test for predictability in co-movements in the tails of sectors returns . Findings indicate that large losses are strongly correlated, supporting asymmetric transmission processes for financial contagion. Finally, using a non-parametric approach, we extend the model to the multivariate case and assess the value of volatility and correlation timing in optimal portfolio selection. The results can help to improve the understanding of time-varying volatility, correlation and tail systemic risk of shipping stock markets, and consequently, have implications for risk management and asset allocation practices, as well as regulatory policies.

We consider a seasonal mean-reverting model for energy commodity prices with jumps and Heston-type stochastic volatility, and three nested models for comparison. By exploiting the affine form of the log-spot models, we develop a general valuation framework for futures and discrete arithmetic Asian options. We investigate five major petroleum commodities from Europe (Brent crude oil, gasoil) and US (light sweet crude oil, gasoline, heating oil) and analyse the effects of the competing fitted spot models in futures pricing, Asian options pricing and hedging. We find evidence that price jumps and stochastic volatility are important features of the petroleum price dynamics.

We present a joint Monte Carlo-Fourier transform sampling scheme for pricing derivative products under a Carr–Geman–Madan–Yor (CGMY) model (Carr et al. [Journal of Business, 75, 305–332, 2002]) exhibiting jumps of infinite activity and finite or infinite variation. The approach relies on numerical transform inversion with computable error estimates, which allow generating the unknown cumulative distribution function of the CGMY process increments at the desired accuracy level. We use this to generate samples and simulate the entire trajectory of the process without need of truncating the process small jumps. We illustrate the computational efficiency of the proposed method by comparing it to the existing methods in the literature on pricing a wide range of option contracts, including path-dependent univariate and multivariate products.

The main objective of this thesis is to provide effective means for the valuation of popular financial derivative contracts with path-dependence and/or early-exercisable provisions. Starting from the risk-neutral valuation formula, the approach we propose is to sequentially compute convolutions of the value function of the contract at a monitoring date with the transition density between two dates, to provide the value function at the previous monitoring date, until the present date. A rigorous computational algorithm for the convolutions is then developed based on transformations to the Fourier domain. In the first part of the thesis, we deal with arithmetic Asian options, which, due to the growing popularity they enjoy in the financial marketplace, have been researched signicantly over the last two decades. Although few remarkable approaches have been proposed so far, these are restricted to the market assumptions imposed by the standard Black-Scholes-Merton paradigm. Others, although in theory applicable to Lévy models, are shown to suffer a non-monotone convergence when implemented numerically. To solve the Asian option pricing problem, we initially propose a flexible framework for independently distributed log-returns on the underlying asset. This allows us to generalize firstly in calculating the price sensitivities. Secondly, we consider an extension to non-Lévy stochastic volatility models. We highlight the benefits of the new scheme and, where relevant, benchmark its performance against an analytical approximation, control variate Monte Carlo strategies and existing forward convolution algorithms for the recovery of the density of the underlying average price. In the second part of the thesis, we carry out an analysis on the rapidly growing market of convertible bonds (CBs). Despite the vast amount of research which has been undertaken yet. This is due to the need for proper modelling of the CBs composite payout structure and the multi factor modelling arising in the CB valuation. Given the dimensional capacity of the convolution algorithm, we are now able to introduce a new jump diffusion structural approach in the CB literature, towards more realistic modelling of the default risk, and further include correlated stochastic interest rates. This aims at fixing dimensionality and convergence limitations which previously have been restricting the range of applicability of popular grid- based, lattice and Monte Carlo methods. The convolution scheme further permits flexible handling of real-world CB specications; this allows us to properly model the call policy and investigate its impact on the computed CB prices. We illustrate the performance of the numerical scheme and highlight the effects originated by the inclusion of jumps.

We develop a novel, workable switching option model approach to component redesign and replacement projects that are divisible into sequential phases. The component manufacturer has the option to retain the current product position and abandon the project, or switch to a redesigned product position. Additional uncertainty remains as to whether the redesigned product can meet the newly set production efficiency criteria. Depending on the viability of a prototype, the firm can finally decide whether or not to move to final production. Our framework incorporates a potentially valuable option which aims to reduce the cost of the project. It is generally applicable to fields where investments in components’ replacement have to be correctly timed due to large costs and uncertainties about the outcome. We illustrate this by means of a case study application in aeronautical engineering using real data, where we show via a sensitivity analysis based on the key variables that the flexibility of component switching offered via options during the development of the project can be significantly valuable.

Radiation therapy along with chemotherapy and surgery remain the main cancer treatments. Radiotherapy can be applied to patients externally (external beam radiotherapy) or internally (brachytherapy and radioisotope therapy). Previously, nanoencapsulation of radioactive crystals within carbon nanotubes, followed by end-closing, resulted in the formation of nanocapsules that allowed ultrasensitive imaging in healthy mice. Herein we report on the preparation of nanocapsules initially sealing “cold” isotopically enriched samarium (152Sm), which can then be activated on demand to their “hot” radioactive form (153Sm) by neutron irradiation. The use of “cold” isotopes avoids the need for radioactive facilities during the preparation of the nanocapsules, reduces radiation exposure to personnel, prevents the generation of nuclear waste, and evades the time constraints imposed by the decay of radionuclides. A very high specific radioactivity is achieved by neutron irradiation (up to 11.37 GBq/mg), making the “hot” nanocapsules useful not only for in vivo imaging but also therapeutically effective against lung cancer metastases after intravenous injection. The high in vivo stability of the radioactive payload, selective toxicity to cancerous tissues, and the elegant preparation method offer a paradigm for application of nanomaterials in radiotherapy., This work received funding from the European Union’s Seventh Framework Programme (FP7-ITN Marie-Curie Actions, RADDEL, 290023). K.T.A.-J. acknowledges funding from Worldwide Cancer Research (12-1054), M.K. from MEYS (LTC18039), and G.T. from Agaur (2017 SGR 581). ICMAB and ICN2 acknowledge financial support from the Spanish Ministry of Economy and Competitiveness, through the “Severo Ochoa” Programme for Centres of Excellence in R&D (SEV-2015-0496 and SEV-2017-0706). This work was partly supported by the Centre National de la Recherche Scientifique (CNRS), by the Agence Nationale de la Recherche (ANR) through the LabEx project Chemistry of Complex Systems (ANR-10-LABX-0026_CSC), and by the International Center for Frontier Research in Chemistry (icFRC). We acknowledge support by MEYS CR and EU -ESIF in the frame of Operational Programme Research Development and Education - project Pro-NanoEnviCz (Project No. CZ.02.1.01/0.0/0.0/16_013/0001821). We thank Thomas Swan Co. Ltd. for supplying CNT Elicarb samples. We are grateful to S. Sandoval (ICMAB) and C. Ramos (IQS-ICMAB) for assessing the length and diameter distribution of CNTs.

The relationship between the prices of crude oil and its refined products is at the heart of the oil industry. Crude oil and refined products volatilities and correlations have been modelled extensively using short-memory multivariate GARCH models. This paper investigates the potential benefits from using fractionally integrated multivariate GARCH models from a forecasting and a risk management perspective. Several models for the spot returns on three major oil-related markets are compared. In-sample results show significant evidence of long-memory decay and leverage effects in volatilities and of time-varying autocorrelations. The forecasting performance of the models is assessed by means of three approaches: the Superior Predictive Ability test, the Model Confidence Set and the Value-at-Risk. The results indicate that the multivariate models incorporating long-memory outperform the short-memory benchmarks in forecasting the conditional covariance matrix and associated risk magnitudes. The paper makes an innovative contribution to the analysis of the relationship between crude oil and its refined products providing refiners, physical oil traders, non-commercial oil traders and other energy markets agents with significant insights for hedging and risk management operations.