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Cyber risk classifications are widely used in the modeling of cyber event distributions, yet their effectiveness in out of sample forecasting performance remains underexplored. In this paper, we analyse the most commonly used classifications and argue in favour of switching the attention from goodness-of-fit and in-sample predictive performance, to focusing on the out-of sample forecasting performance. We use a rolling window analysis, to compare cyber risk distribution forecasts via threshold weighted scoring functions. Our results indicate that business motivated cyber risk classifications appear to be too restrictive and not flexible enough to capture the heterogeneity of cyber risk events. We investigate how dynamic and impact-based cyber risk classifiers seem to be better suited in forecasting future cyber risk losses than the other considered classifications. These findings suggest that cyber risk types provide limited forecasting ability concerning cyber event severity distribution, and cyber insurance ratemakers should utilize cyber risk types only when modeling the cyber event frequency distribution. Our study offers valuable insights for decision-makers and policymakers alike, contributing to the advancement of scientific knowledge in the field of cyber risk management., Comment: 64 pages, 24 tables, 8 figures

In stochastic multi-factor commodity models, it is often the case that futures prices are explained by two latent state variables which represent the short and long term stochastic factors. In this work, we develop the family of stochastic models using polynomial diffusion to obtain the unobservable spot price to be used for modelling futures curve dynamics. The polynomial family of diffusion models allows one to incorporate a variety of non-linear, higher-order effects, into a multi-factor stochastic model, which is a generalisation of Schwartz and Smith (2000) two-factor model. Two filtering methods are used for the parameter and the latent factor estimation to address the non-linearity. We provide a comparative analysis of the performance of the estimation procedures. We discuss the parameter identification problem present in the polynomial diffusion case, regardless, the futures prices can still be estimated accurately. Moreover, we study the effects of different methods of calculating matrix exponential in the polynomial diffusion model. As the polynomial order increases, accurately and efficiently approximating the high-dimensional matrix exponential becomes essential in the polynomial diffusion model.

PDSim is an R package that enables users to simulate commodity futures prices using the polynomial diffusion model introduced in Filipovic and Larsson (2016) through both a Shiny web application and R scripts. It also provides state variables and contract estimations via the Extended Kalman Filter (EKF) or Unscented Kalman Filter (UKF). With its user-friendly interface, PDSim makes the features of simulations and estimations accessible to all users. To date, it is the only package specifically designed for the simulation and estimation of the polynomial diffusion model. Additionally, the package integrates the Schwartz and Smith two-factor model (Schwartz & Smith, 2000) as an alternative approach. PDSim offers versatile deployment options, including running locally, via the Shiny server, or through Docker.

The mini proceedings of the "Fourth International Workshop on the Extension Project for the J-PARC Hadron Experimental Facility (HEF-ex 2024) [https://kds.kek.jp/event/46965]" held at J-PARC, February 19-21, 2024, are presented. The workshop was devoted to discussing the physics case that connects both the present and the future Hadron Experimental Facility at J-PARC, covering a wide range of topics in flavor, hadron, and nuclear physics related to both experimental and theoretical activities being conducted at the facility.

The Nelson-Siegel model is widely used in fixed income markets to produce yield curve dynamics. The multiple time-dependent parameter model conveniently addresses the level, slope, and curvature dynamics of the yield curves. In this study, we present a novel state-space functional regression model that incorporates a dynamic Nelson-Siegel model and functional regression formulations applied to multi-economy setting. This framework offers distinct advantages in explaining the relative spreads in yields between a reference economy and a response economy. To address the inherent challenges of model calibration, a kernel principal component analysis is employed to transform the representation of functional regression into a finite-dimensional, tractable estimation problem. A comprehensive empirical analysis is conducted to assess the efficacy of the functional regression approach, including an in-sample performance comparison with the dynamic Nelson-Siegel model. We conducted the stress testing analysis of yield curves term-structure within a dual economy framework. The bond ladder portfolio was examined through a case study focused on spread modelling using historical data for US Treasury and UK bonds.

Stochastic versions of recursive integrated climate-economy assessment models are essential for studying and quantifying policy decisions under uncertainty. However, as the number of stochastic shocks increases, solving these models as dynamic programming problems using deterministic grid methods becomes computationally infeasible, and simulation-based methods are needed. The least-squares Monte Carlo (LSMC) method has become popular for solving optimal stochastic control problems in quantitative finance. In this paper, we extend the application of the LSMC method to stochastic climate-economy models. We exemplify this approach using a stochastic version of the DICE model with all five main uncertainties discussed in the literature. To address the complexity and high dimensionality of these models, we incorporate deep neural network approximations in place of standard regression techniques within the LSMC framework. Our results demonstrate that the deep LSMC method can be used to efficiently derive optimal policies for climate-economy models in the presence of uncertainty., Comment: 27 pages with 2 tables and 8 figures

Assessing climate risk and its potential impacts on our cities and economies is of fundamental importance. Extreme weather events, such as hurricanes, floods, and storm surges can lead to catastrophic damages. We propose a flexible approach based on real options analysis and extreme value theory, which enables the selection of optimal adaptation pathways for a portfolio of climate adaptation projects. We model the severity of extreme sea level events using the block maxima approach from extreme value theory, and then develop a real options framework, factoring in climate change, sea level rise uncertainty, and the growth in asset exposure. We then apply the proposed framework to a real-world problem, considering sea level data as well as different adaptation investment options for New York City. Our research can assist governments and policy makers in taking informed decisions about optimal adaptation pathways and more specifically about reducing flood and storm surge risk in a dynamic settings., Comment: 29 pages, 5 figures, and 4 tables

Characteristics of the quasi-bound state in the $K^- pp$ system strongly depend on the model of antikaon-nucleon interaction and weakly - on the nucleon-nucleon potential. In the present paper, dynamically exact Faddeev-type calculations with coupled $\bar{K}NN$ and $\pi \Sigma N$ were performed using different models of the $\Sigma N$ and $\pi N$ interactions to study the influence of these "less important" interaction on the three-body result. In addition, dynamically exact three-body Faddeev-type AGS calculations with three coupled particle channels $\bar{K}NN - \pi \Sigma N - \pi \Lambda N$ were performed., Comment: 10 pages, 4 tables, submitted to the special issue of the Few-Body Physics journal "Advances on the Few-Body Problem in Physics - Selected and refereed papers from the 25th European Conference"

We have been performing daily observations of bright microquasars at 1.2-20~GHz with the Northern sector of RATAN-600 radio telescope for more than ten years. During the 2019--2021 observations we recorded bright flares, which we call giant flares because fluxes reach record levels -- above 20~Jy -- during these events. In this paper we report the results of intraday variations of the Cygnus~X-3} microquasar in multi-azimuth observations made with the "North sector with a flat-sheet reflector" during giant flares of Cygnus X-3. These were the first such observations made simultaneously at several frequencies on a short time scale (10 minutes). Observational data consists of 31 measurement made within +/-2.7 hours of the culmination of the object. We are the first to discover the evolution of the spectrum of the flare emission of Cygnus~X-3 on a time scale comparable to the orbital period of the binary. The measurement data allowed us to determine the temporal and spectral parameters of radio emission, which are typical for synchrotron flare emission in relativistic jets. Evolution of the radio emission of X-ray binaries on short time scales is a key to understanding the formation of jet outbursts in the process of mass accretion of the matter of the donor star onto the relativistic object., Comment: 10 pages, 13 figures, 2 tables, maik.sty

We investigate insurance purchases when bequest motives are age-varying and life insurance and life annuities both carry loads. The existing life cycle literature assumes bequests are normal goods without being either necessities or luxuries. Much of the literature also assumes implicitly that life annuity loads are negative. A key finding of the literature is that the demand for life insurance and the demand for life annuities are symmetrical. It is optimal to buy life-contingent insurance throughout life, even under loads. A life annuity phase backs directly onto a life insurance phase. We find that realistic examples with positive loads on both products reveal up to two distinct periods of non-participation, one in midlife and the other adjoining the maximum age. We highlight examples with necessity bequests during child-rearing years and luxury bequests thereafter. This set of assumptions explains why a substantial demand for life insurance during child-rearing years can co-exist with negligible demand for life annuities later on. A realistic 18% load on both products generates this outcome., Comment: To appear in Journal of Economic Dynamics and Control

The paper is devoted to the $\bar{K}NNN$ system, which is an exotic system consisting of an antikaon and three nucleons. Dynamically exact four-body Faddeev-type equations were solved and characteristics of the quasi-bound state in the system were evaluated. Three antikaon-nucleon and three nucleon-nucleon potentials were used, so the dependence of the four-body pole positions on the two-body interaction models was studied. The resulting binding energies $B^{\rm Chiral}_{K^-ppn} \sim 30.5 - 34.5$ MeV obtained with chirally motivated and $B^{\rm SIDD}_{K^-ppn} \sim 46.4 - 52.0$ MeV obtained with phenomenological antikaon-nucleon potentials are close to those obtained for the $K^- pp$ system with the same $\bar{K}N$ and $NN$ potentials, while the four-body widths $\Gamma_{K^- ppn} \sim 38.2 - 50.9$ MeV are smaller., Comment: 22 pages, 2 figures, 5 tables

We focus on model risk and risk sensitivity when addressing the insurability of cyber risk. The standard statistical approaches to assessment of insurability and potential mispricing are enhanced in several aspects involving consideration of model risk. Model risk can arise from model uncertainty, and parameters uncertainty. We demonstrate how to quantify the effect of model risk in this analysis by incorporating various robust estimators for key model parameter estimates that apply in both marginal and joint cyber risk loss process modelling. We contrast these robust techniques with standard methods previously used in studying insurabilty of cyber risk. This allows us to accurately assess the critical impact that robust estimation can have on tail index estimation for heavy tailed loss models, as well as the effect of robust dependence analysis when quantifying joint loss models and insurance portfolio diversification. We argue that the choice of such methods is akin to a form of model risk and we study the risk sensitivity that arise from choices relating to the class of robust estimation adopted and the impact of the settings associated with such methods on key actuarial tasks such as premium calculation in cyber insurance. Through this analysis we are able to address the question that, to the best of our knowledge, no other study has investigated in the context of cyber risk: is model risk present in cyber risk data, and how does is it translate into premium mispricing? We believe our findings should complement existing studies seeking to explore insurability of cyber losses. In order to ensure our findings are based on realistic industry informed loss data, we have utilised one of the leading industry cyber loss datasets obtained from Advisen, which represents a comprehensive data set on cyber monetary losses, from which we form our analysis and conclusions., Comment: 30 pages, 34 figures

In this study we examine the nature of losses from cyber related events across different risk categories and business sectors. Using a leading industry dataset of cyber events, we evaluate the relationship between the frequency and severity of individual cyber-related events and the number of affected records. We find that the frequency of reported cyber related events has substantially increased between 2008 and 2016. Furthermore, the frequency and severity of losses depend on the business sector and type of cyber threat: the most significant cyber loss event categories, by number of events, were related to data breaches and the unauthorized disclosure of data, while cyber extortion, phishing, spoofing and other social engineering practices showed substantial growth rates. Interestingly, we do not find a distinct pattern between the frequency of events, the loss severity, and the number of affected records as often alluded to in the literature. We also analyse the severity distribution of cyber related events across all risk categories and business sectors. This analysis reveals that cyber risks are heavy-tailed, i.e., cyber risk events have a higher probability to produce extreme losses than events whose severity follows an exponential distribution. Furthermore, we find that the frequency and severity of cyber related losses exhibits a very dynamic and time varying nature., Comment: 24 pages, 10 figures

We study the problem of reducing the variance of Monte Carlo estimators through performing suitable changes of the sampling measure which are induced by feedforward neural networks. To this end, building on the concept of vector stochastic integration, we characterize the Cameron-Martin spaces of a large class of Gaussian measures which are induced by vector-valued continuous local martingales with deterministic covariation. We prove that feedforward neural networks enjoy, up to an isometry, the universal approximation property in these topological spaces. We then prove that sampling measures which are generated by feedforward neural networks can approximate the optimal sampling measure arbitrarily well. We conclude with a comprehensive numerical study pricing path-dependent European options for asset price models that incorporate factors such as changing business activity, knock-out barriers, dynamic correlations, and high-dimensional baskets.

In this study an exploration of insurance risk transfer is undertaken for the cyber insurance industry in the United States of America, based on the leading industry dataset of cyber events provided by Advisen. We seek to address two core unresolved questions. First, what factors are the most significant covariates that may explain the frequency and severity of cyber loss events and are they heterogeneous over cyber risk categories? Second, is cyber risk insurable in regards to the required premiums, risk pool sizes and how would this decision vary with the insured companies industry sector and size? We address these questions through a combination of regression models based on the class of Generalised Additive Models for Location Shape and Scale (GAMLSS) and a class of ordinal regressions. These models will then form the basis for our analysis of frequency and severity of cyber risk loss processes. We investigate the viability of insurance for cyber risk using a utility modelling framework with premium calculated by classical certainty equivalence analysis utilising the developed regression models. Our results provide several new key insights into the nature of insurability of cyber risk and rigorously address the two insurance questions posed in a real data driven case study analysis., Comment: 42 pages, 14 figures

The classical DICE model is a widely accepted integrated assessment model for the joint modeling of economic and climate systems, where all model state variables evolve over time deterministically. We reformulate and solve the DICE model as an optimal control dynamic programming problem with six state variables (related to the carbon concentration, temperature, and economic capital) evolving over time deterministically and affected by two controls (carbon emission mitigation rate and consumption). We then extend the model by adding a discrete stochastic shock variable to model the economy in the stressed and normal regimes as a jump process caused by events such as the COVID-19 pandemic. These shocks reduce the world gross output leading to a reduction in both the world net output and carbon emission. The extended model is solved under several scenarios as an optimal stochastic control problem, assuming that the shock events occur randomly on average once every 100 years and last for 5 years. The results show that, if the world gross output recovers in full after each event, the impact of the COVID-19 events on the temperature and carbon concentration will be immaterial even in the case of a conservative 10\% drop in the annual gross output over a 5-year period. The impact becomes noticeable, although still extremely small (long-term temperature drops by $0.1^\circ \mathrm{C}$), in a presence of persistent shocks of a 5\% output drop propagating to the subsequent time periods through the recursively reduced productivity. If the deterministic DICE model policy is applied in a presence of stochastic shocks (i.e. when this policy is suboptimal), then the drop in temperature is larger (approximately $0.25^\circ \mathrm{C}$), that is, the lower economic activities owing to shocks imply that more ambitious mitigation targets are now feasible at lower costs.

Accuracy of "corrected Deser" and "summed up Deser" formulas was checked for the $K^- p$ and $K^- d$ systems. It was found that the last one is much more accurate and should be used for connection the $1s$ level shift to the corresponding scattering length., Comment: 3 pages, 2 tables; small changes in the text, several references were added

We quantify model risk of a financial portfolio whereby a multi-period mean-standard-deviation criterion is used as a selection criterion. In this work, model risk is defined as the loss due to uncertainty of the underlying distribution of the returns of the assets in the portfolio. The uncertainty is measured by the Kullback-Leibler divergence, i.e., the relative entropy. In the worst case scenario, the optimal robust strategy can be obtained in a semi-analytical form as a solution of a system of nonlinear equations. Several numerical results are presented which allow us to compare the performance of this robust strategy with the optimal non-robust strategy. For illustration, we also quantify the model risk associated with an empirical dataset.