Annals of Actuarial Science最新文献

{"title":"SPLICE: a synthetic paid loss and incurred cost experience simulator","authors":"Benjamin Avanzi, G. Taylor, Melantha Wang","doi":"10.1017/S1748499522000057","DOIUrl":"https://doi.org/10.1017/S1748499522000057","url":null,"abstract":"Abstract In this paper, we first introduce a simulator of cases estimates of incurred losses called SPLICE (Synthetic Paid Loss and Incurred Cost Experience). In three modules, case estimates are simulated in continuous time, and a record is output for each individual claim. Revisions for the case estimates are also simulated as a sequence over the lifetime of the claim in a number of different situations. Furthermore, some dependencies in relation to case estimates of incurred losses are incorporated, particularly recognising certain properties of case estimates that are found in practice. For example, the magnitude of revisions depends on ultimate claim size, as does the distribution of the revisions over time. Some of these revisions occur in response to occurrence of claim payments, and so SPLICE requires input of simulated per-claim payment histories. The claim data can be summarised by accident and payment “periods” whose duration is an arbitrary choice (e.g. month, quarter, etc.) available to the user. SPLICE is built on an existing simulator of individual claim experience called SynthETIC (introduced in Avanzi et al. 2021b, Insurance: Mathematics and Economics, 100, 296–308), which offers flexible modelling of occurrence, notification, as well as the timing and magnitude of individual partial payments. This is in contrast with the incurred losses, which constitute the additional contribution of SPLICE. The inclusion of incurred loss estimates provides a facility that almost no other simulators do. SPLICE is is a fully documented R package that is publicly available and open source (on CRAN). SPLICE, combined with SynthETIC, provides 11 modules (occurrence, notification, etc.), any one or more of which may be re-designed according to the user’s requirements. It comes with a default version that is loosely calibrated to resemble a specific (but anonymous) Auto Bodily Injury portfolio, as well as data generation functionality that outputs alternative data sets under a range of hypothetical scenarios differing in complexity. The general structure is suitable for most lines of business, with some reparameterisation.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":"17 1","pages":"7 - 35"},"PeriodicalIF":1.7,"publicationDate":"2021-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43723090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pseudo-model-free hedging for variable annuities via deep reinforcement learning","authors":"W. F. Chong, Haoen Cui, Yuxuan Li","doi":"10.1017/s1748499523000027","DOIUrl":"https://doi.org/10.1017/s1748499523000027","url":null,"abstract":"This paper proposes a two-phase deep reinforcement learning approach, for hedging variable annuity contracts with both GMMB and GMDB riders, which can address model miscalibration in Black-Scholes financial and constant force of mortality actuarial market environments. In the training phase, an infant reinforcement learning agent interacts with a pre-designed training environment, collects sequential anchor-hedging reward signals, and gradually learns how to hedge the contracts. As expected, after a sufficient number of training steps, the trained reinforcement learning agent hedges, in the training environment, equally well as the correct Delta while outperforms misspecified Deltas. In the online learning phase, the trained reinforcement learning agent interacts with the market environment in real time, collects single terminal reward signals, and self-revises its hedging strategy. The hedging performance of the further trained reinforcement learning agent is demonstrated via an illustrative example on a rolling basis to reveal the self-revision capability on the hedging strategy by online learning.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2021-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47461700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial","authors":"Katrien Antonio, Christophe Dutang, A. Tsanakas","doi":"10.1017/S174849952100018X","DOIUrl":"https://doi.org/10.1017/S174849952100018X","url":null,"abstract":"The proceedings contains 344 papers. The topics discussed include: Chronic coronary syndrome patients with polyarterial disease are a high risk but heterogenous subset of patients. Insights from the CLARIFY registry;Additional prognostic value of vasodilator stress cardiovascular magnetic resonance in patients with inconclusive stress test to detect coronary artery disease;Applicability of the REDUCE-IT trial to the FAST-MI registry. Are the results of randomized trials relevant in routine clinical practice?;Medical therapy at discharge in patients admitted for acute coronary syndrome: Data of the French MONICA population registers;Post-infectious myocardial Infarction: Does percutaneous coronary intervention improve outcomes? A propensity-score matched analysis;Prevalence and prognosis of iron deficiency in acute myocardial infarction;Myocardial infarction rates overview during COVID-19 pandemic In France: Results of the MODIF registry;Short-term air pollution concentration variations and ST-elevation myocardial infarction: A case-crossover study from the SCALIM registry;Predictive value of early cardiac mri functional and geometric indexes on adverse left ventricular remodelling in anterior STEMI patients. A report from the CIRCUS study;High levels of lipoprotein(a) are associated with the severity of coronary disease in patients with acute myocardial infarction. Data from the RICO survey;More than 30% of symptomatic patients at one year in “infarct like” acute myocarditis;Impact of use stent with a polyethylene terephthalate micro-net covering on coronary microvascular dysfunction in patients with acute myocardial infarction;Impact of Covid-19 infection in high-risk coronary patients;Coronary artery lesions in Kawasaki Disease;Activity of a catheterization laboratory in Tunisia: A comparative study before and during confinement (COVID-19);Acute coronary syndromes in the era of SARS-CoV-2 Infection: A registry of the French group of acute cardiac care","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":"15 1","pages":"205 - 206"},"PeriodicalIF":1.7,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45608321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Conditional mean risk sharing in the individual model with graphical dependencies","authors":"M. Denuit, C. Robert","doi":"10.1017/S1748499521000166","DOIUrl":"https://doi.org/10.1017/S1748499521000166","url":null,"abstract":"Abstract Conditional mean risk sharing appears to be effective to distribute total losses amongst participants within an insurance pool. This paper develops analytical results for this allocation rule in the individual risk model with dependence induced by the respective position within a graph. Precisely, losses are modelled by zero-augmented random variables whose joint occurrence distribution and individual claim amount distributions are based on network structures and can be characterised by graphical models. The Ising model is adopted for occurrences and loss amounts obey decomposable graphical models that are specific to each participant. Two graphical structures are thus used: the first one to describe the contagion amongst member units within the insurance pool and the second one to model the spread of losses inside each participating unit. The proposed individual risk model is typically useful for modelling operational risks, catastrophic risks or cybersecurity risks.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":"16 1","pages":"183 - 209"},"PeriodicalIF":1.7,"publicationDate":"2021-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/S1748499521000166","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48007018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of the choice of risk assessment time horizons on defined benefit pension schemes","authors":"Doug Andrews, S. Bonnar, L. Curtis, Jaideep S. Oberoi, Aniketh Pittea, Pradip Tapadar","doi":"10.1017/S1748499521000178","DOIUrl":"https://doi.org/10.1017/S1748499521000178","url":null,"abstract":"Abstract We examine the impact of asset allocation and contribution rates on the risk of defined benefit (DB) pension schemes, using both a run-off and a shorter 3-year time horizon. Using the 3-year horizon, which is typically preferred by regulators, a high bond allocation reduces the spread of the distribution of surplus. However, this result is reversed when examined on a run-off basis. Furthermore, under both the 3-year horizon and the run-off, the higher bond allocation reduces the median level of surplus. Pressure on the affordability of DB schemes has led to widespread implementation of the so-called de-risking strategies, such as moving away from predominantly equity investments to greater bond investments. If the incentives produced by shorter term risk assessments are contributing to this shift, they might be harming the long-term financial health of the schemes. Contribution rates have relatively lower impact on the risk.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":"16 1","pages":"214 - 242"},"PeriodicalIF":1.7,"publicationDate":"2021-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/S1748499521000178","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49433364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A robust random coefficient regression representation of the chain-ladder method","authors":"Ioannis Badounas, Apostolos Bozikas, G. Pitselis","doi":"10.1017/S1748499521000154","DOIUrl":"https://doi.org/10.1017/S1748499521000154","url":null,"abstract":"Abstract It is well known that the presence of outliers can mis-estimate (underestimate or overestimate) the overall reserve in the chain-ladder method, when we consider a linear regression model, based on the assumption that the coefficients are fixed and identical from one observation to another. By relaxing the usual regression assumptions and applying a regression with randomly varying coefficients, we have a similar phenomenon, i.e., mis-estimation of the overall reserves. The lack of robustness of loss reserving regression with random coefficients on incremental payment estimators leads to the development of this paper, aiming to apply robust statistical procedures to the loss reserving estimation when regression coefficients are random. Numerical results of the proposed method are illustrated and compared with the results that were obtained by linear regression with fixed coefficients.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":"16 1","pages":"151 - 182"},"PeriodicalIF":1.7,"publicationDate":"2021-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/S1748499521000154","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45088917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scenario Weights for Importance Measurement (SWIM) – an R package for sensitivity analysis","authors":"Silvana M. Pesenti, Alberto Bettini, Pietro Millossovich, Andreas Tsanakas","doi":"10.1017/s1748499521000130","DOIUrl":"https://doi.org/10.1017/s1748499521000130","url":null,"abstract":"The Scenario Weights for Importance Measurement (<jats:bold>SWIM</jats:bold>) package implements a flexible sensitivity analysis framework, based primarily on results and tools developed by Pesenti <jats:italic>et al</jats:italic>. (2019). <jats:bold>SWIM</jats:bold> provides a stressed version of a stochastic model, subject to model components (random variables) fulfilling given probabilistic constraints (stresses). Possible stresses can be applied on moments, probabilities of given events, and risk measures such as Value-At-Risk and Expected Shortfall. <jats:bold>SWIM</jats:bold> operates upon a single set of simulated scenarios from a stochastic model, returning scenario weights, which encode the required stress and allow monitoring the impact of the stress on all model components. The scenario weights are calculated to minimise the relative entropy with respect to the baseline model, subject to the stress applied. As well as calculating scenario weights, the package provides tools for the analysis of stressed models, including plotting facilities and evaluation of sensitivity measures. <jats:bold>SWIM</jats:bold> does not require additional evaluations of the simulation model or explicit knowledge of its underlying statistical and functional relations; hence, it is suitable for the analysis of black box models. The capabilities of <jats:bold>SWIM</jats:bold> are demonstrated through a case study of a credit portfolio model.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":"875 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2021-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138529748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Statistical features of persistence and long memory in mortality data","authors":"G. Peters, Hongxuan Yan, J. Chan","doi":"10.1017/S1748499521000129","DOIUrl":"https://doi.org/10.1017/S1748499521000129","url":null,"abstract":"Abstract Understanding core statistical properties and data features in mortality data are fundamental to the development of machine learning methods for demographic and actuarial applications of mortality projection. The study of statistical features in such data forms the basis for classification, regression and forecasting tasks. In particular, the understanding of key statistical structure in such data can aid in improving accuracy in undertaking mortality projection and forecasting when constructing life tables. The ability to accurately forecast mortality is a critical aspect for the study of demography, life insurance product design and pricing, pension planning and insurance-based decision risk management. Though many stylised facts of mortality data have been discussed in the literature, we provide evidence for a novel statistical feature that is pervasive in mortality data at a national level that is as yet unexplored. In this regard, we demonstrate in this work a strong evidence for the existence of long memory features in mortality data, and second that such long memory structures display multifractality as a statistical feature that can act as a discriminator of mortality dynamics by age, gender and country. To achieve this, we first outline the way in which we choose to represent the persistence of long memory from an estimator perspective. We make a natural link between a class of long memory features and an attribute of stochastic processes based on fractional Brownian motion. This allows us to use well established estimators for the Hurst exponent to then robustly and accurately study the long memory features of mortality data. We then introduce to mortality analysis the notion from data science known as multifractality. This allows us to study the long memory persistence features of mortality data on different timescales. We demonstrate its accuracy for sample sizes commensurate with national-level age term structure historical mortality records. A series of synthetic studies as well a comprehensive analysis of real mortality death count data are studied in order to demonstrate the pervasiveness of long memory structures in mortality data, both mono-fractal and multifractal functional features are verified to be present as stylised facts of national-level mortality data for most countries and most age groups by gender. We conclude by demonstrating how such features can be used in kernel clustering and mortality model forecasting to improve these actuarial applications.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":"15 1","pages":"291 - 317"},"PeriodicalIF":1.7,"publicationDate":"2021-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/S1748499521000129","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44726005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint modelling of male and female mortality rates using adaptive P-splines","authors":"Kai Hon Tang, Erengul Dodd, J. Forster","doi":"10.1017/S1748499521000105","DOIUrl":"https://doi.org/10.1017/S1748499521000105","url":null,"abstract":"Abstract Raw mortality data often exhibit irregular patterns due to randomness. Graduation refers to the act of smoothing crude mortality rates. In this paper, we propose a flexible and robust methodology for graduating mortality rates using adaptive P-splines. Since the observed data at high ages are often sparse and unreliable, we use an exponentially increasing penalty. We use mortality data of England and Wales and model male and female mortality rates jointly by means of penalties, achieving borrowing of information between the two sexes.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":"16 1","pages":"119 - 135"},"PeriodicalIF":1.7,"publicationDate":"2021-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/S1748499521000105","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42655306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of mortality shocks on modelling and insurance valuation as exemplified by COVID-19","authors":"Simon Schnürch, T. Kleinow, R. Korn, A. Wagner","doi":"10.2139/ssrn.3835907","DOIUrl":"https://doi.org/10.2139/ssrn.3835907","url":null,"abstract":"Abstract The COVID-19 pandemic interrupts the relatively steady trend of improving longevity observed in many countries over the last decades. We claim that this needs to be addressed explicitly in many mortality modelling applications, for example, in the life insurance industry. To support this position, we provide a descriptive analysis of the mortality development of several countries up to and including the year 2020. Furthermore, we perform an empirical and theoretical investigation of the impact a mortality jump has on the parameters, forecasts and implied present values of the popular Lee–Carter mortality model. We find that COVID-19 has resulted in substantial mortality shocks in many countries. We show that such shocks have a large impact on point and interval forecasts of death rates and, consequently, on the valuation of mortality-related insurance products. We obtain similar findings under the Cairns–Blake–Dowd mortality model, which demonstrates that the effects caused by COVID-19 show up in a variety of models. Finally, we provide an overview of approaches to handle extreme mortality events such as the COVID-19 pandemic in mortality modelling.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":"16 1","pages":"498 - 526"},"PeriodicalIF":1.7,"publicationDate":"2021-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41673455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}