Relevant bibliographies by topics / Financial and Insurable Mathematics

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Financial and Insurable Mathematics'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Financial and Insurable Mathematics"

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Hornyák Gregáňová, Radomíra, Miriam Pietriková, and Norbert Kecskés. "Financial and insurance mathematics in practice from students´ point of view." Mathematics in Education, Research and Applications 4, no. 2 (December 2018): 83–87. http://dx.doi.org/10.15414/meraa.2018.04.02.83-87.

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Donnelly, Catherine, and Paul Embrechts. "The Devil is in the Tails: Actuarial Mathematics and the Subprime Mortgage Crisis." ASTIN Bulletin 40, no. 1 (May 2010): 1–33. http://dx.doi.org/10.2143/ast.40.1.2049222.

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AbstractIn the aftermath of the 2007-2008 financial crisis, there has been criticism of mathematics and the mathematical models used by the finance industry. We answer these criticisms through a discussion of some of the actuarial models used in the pricing of credit derivatives. As an example, we focus in particular on the Gaussian copula model and its drawbacks. To put this discussion into its proper context, we give a synopsis of the financial crisis and a brief introduction to some of the common credit derivatives and highlight the difficulties in valuing some of them.We also take a closer look at the risk management issues in part of the insurance industry that came to light during the financial crisis. As a backdrop to this, we recount the events that took place at American International Group during the financial crisis. Finally, through our paper we hope to bring to the attention of a broad actuarial readership some “lessons (to be) learned” or “events not to be forgotten”.
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Karam, E., and F. Planchet. "Operational Risks in Financial Sectors." Advances in Decision Sciences 2012 (December 5, 2012): 1–57. http://dx.doi.org/10.1155/2012/385387.

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A new risk was born in the mid-1990s known as operational risk. Though its application varied by institutions—Basel II for banks and Solvency II for insurance companies—the idea stays the same. Firms are interested in operational risk because exposure can be fatal. Hence, it has become one of the major risks of the financial sector. In this study, we are going to define operational risk in addition to its applications regarding banks and insurance companies. Moreover, we will discuss the different measurement criteria related to some examples and applications that explain how things work in real life.
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Zhou, Min, Kai-yong Wang, and Yue-bao Wang. "Estimates for the finite-time ruin probability with insurance and financial risks." Acta Mathematicae Applicatae Sinica, English Series 28, no. 4 (October 2012): 795–806. http://dx.doi.org/10.1007/s10255-012-0189-8.

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Jing, Peng, Cai Chang, Heng Zhu, and Qiuming Hu. "Financial Imbalance Risk and Its Control Strategy of China’s Pension Insurance Contribution Rate Reduction." Mathematical Problems in Engineering 2021 (February 27, 2021): 1–12. http://dx.doi.org/10.1155/2021/5558757.

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Within the context of China’s Urban Employees’ Basic Pension Insurance (UEBPI), this paper constructs an actuarial model to analyze the financial imbalance risk of contribution rate reduction and to investigate the possibility of further reducing the contribution rate. It is found that the UEBPI fund would show financial imbalance risk in 2024 if the contribution rate is 16%, and no control strategy is introduced. In the case of single strategy (the collection system reform, delay of retirement age, or the introduction of external finance), the financial sustainability of the UEBPI fund could be improved to some extent, whereas the financial imbalance risk remains huge. In the case of a package of control strategies being implemented, the UEBPI fund could be able to continue its operation until 2060, and the contribution rate can be further reduced by 0–4 percentage. Therefore, the implementation of a package of control strategies presents a prerequisite for controlling the financial imbalance risk and further reducing the contribution rate.
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Chen, Yi-qing, and Xiang-sheng Xie. "The Finite Time Ruin Probability with the Same Heavy-tailed Insurance and Financial Risks." Acta Mathematicae Applicatae Sinica, English Series 21, no. 1 (February 2005): 153–56. http://dx.doi.org/10.1007/s10255-005-0226-y.

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Yang, Yang, Jin-guan Lin, and Zhong-quan Tan. "The finite-time ruin probability in the presence of Sarmanov dependent financial and insurance risks." Applied Mathematics-A Journal of Chinese Universities 29, no. 2 (June 2014): 194–204. http://dx.doi.org/10.1007/s11766-014-3209-z.

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Cordoni, Francesco, and Luca Di Persio. "Backward Stochastic Differential Equations Approach to Hedging, Option Pricing, and Insurance Problems." International Journal of Stochastic Analysis 2014 (September 11, 2014): 1–11. http://dx.doi.org/10.1155/2014/152389.

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In the present work we give a self-contained introduction to financial mathematical models characterized by noise of Lévy type in the framework of the backward stochastic differential equations theory. Such techniques will be then used to analyse an innovative model related to insurance and death processes setting.
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Chen, Yiqing. "The Finite-Time Ruin Probability with Dependent Insurance and Financial Risks." Journal of Applied Probability 48, no. 04 (December 2011): 1035–48. http://dx.doi.org/10.1017/s0021900200008603.

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Consider a discrete-time insurance risk model. Within periodi, the net insurance loss is denoted by a real-valued random variableXi. The insurer makes both risk-free and risky investments, leading to an overall stochastic discount factorYifrom timeito timei− 1. Assume that (Xi,Yi),i∈N, form a sequence of independent and identically distributed random pairs following a common bivariate Farlie-Gumbel-Morgenstern distribution with marginal distribution functionsFandG. WhenFis subexponential andGfulfills some constraints in order for the product convolution ofFandGto be subexponential too, we derive a general asymptotic formula for the finite-time ruin probability. Then, for special cases in whichFbelongs to the Fréchet or Weibull maximum domain of attraction, we improve this general formula to be transparent.
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Huang, Liwei, and Arkady Shemyakin. "Empirical comparison of skewed t-copula models for insurance and financial data." Model Assisted Statistics and Applications 15, no. 4 (December 25, 2020): 351–61. http://dx.doi.org/10.3233/mas-200506.

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Skewed t-copulas recently became popular as a modeling tool of non-linear dependence in statistics. In this paper we consider three different versions of skewed t-copulas introduced by Demarta and McNeill; Smith, Gan and Kohn; and Azzalini and Capitanio. Each of these versions represents a generalization of the symmetric t-copula model, allowing for a different treatment of lower and upper tails. Each of them has certain advantages in mathematical construction, inferential tools and interpretability. Our objective is to apply models based on different types of skewed t-copulas to the same financial and insurance applications. We consider comovements of stock index returns and times-to-failure of related vehicle parts under the warranty period. In both cases the treatment of both lower and upper tails of the joint distributions is of a special importance. Skewed t-copula model performance is compared to the benchmark cases of Gaussian and symmetric Student t-copulas. Instruments of comparison include information criteria, goodness-of-fit and tail dependence. A special attention is paid to methods of estimation of copula parameters. Some technical problems with the implementation of maximum likelihood method and the method of moments suggest the use of Bayesian estimation. We discuss the accuracy and computational efficiency of Bayesian estimation versus MLE. Metropolis-Hastings algorithm with block updates was suggested to deal with the problem of intractability of conditionals.
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Dissertations / Theses on the topic "Financial and Insurable Mathematics"

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Gregorová, Jitka. "Návrh metodiky výběru pojišťovacích produktů pro fyzické osoby." Master's thesis, Vysoké učení technické v Brně. Ústav soudního inženýrství, 2012. http://www.nusl.cz/ntk/nusl-232639.

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Diploma Thesis called “Methodology Suggestion for the Choosing of Insurance Products for Individuals” deals with Procedure design of selected insurance products for natural persons with regard for their insurance needs and costs. In order to see the differences between clients' real needs and what insurance companies offer I have compared insurance companies statistics with question-forms' results. The question-forms were focused on the needs of clients in insurance. After that I have designed a procedure and algorithm of choice of suitable insurance products for cost optimization for each assurance
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Parker, Bobby I. Mr. "Assessment of the Sustained Financial Impact of Risk Engineering Service on Insurance Claims Costs." Digital Archive @ GSU, 2011. http://digitalarchive.gsu.edu/math_theses/100.

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This research paper creates a comprehensive statistical model, relating financial impact of risk engineering activity, and insurance claims costs. Specifically, the model shows important statistical relationships among six variables including: types of risk engineering activity, risk engineering dollar cost, duration of risk engineering service, and type of customer by industry classification, dollar premium amounts, and dollar claims costs. We accomplish this by using a large data sample of approximately 15,000 customer-years of insurance coverage, and risk engineering activity. Data sample is from an international casualty/property insurance company and covers four years of operations, 2006-2009. The choice of statistical model is the linear mixed model, as presented in SAS 9.2 software. This method provides essential capabilities, including the flexibility to work with data having missing values, and the ability to reveal time-dependent statistical associations.
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Rasoul, Ryan. "Comparison of Forecasting Models Used by The Swedish Social Insurance Agency." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-49107.

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We will compare two different forecasting models with the forecasting model that was used in March 2014 by The Swedish Social Insurance Agency ("Försäkringskassan" in Swedish or "FK") in this degree project. The models are used for forecasting the number of cases. The two models that will be compared with the model used by FK are the Seasonal Exponential Smoothing model (SES) and Auto-Regressive Integrated Moving Average (ARIMA) model. The models will be used to predict case volumes for two types of benefits: General Child Allowance “Barnbidrag” or (BB_ABB), and Pregnancy Benefit “Graviditetspenning” (GP_ANS). The results compare the forecast errors at the short time horizon (22) months and at the long-time horizon (70) months for the different types of models. Forecast error is the difference between the actual and the forecast value of case numbers received every month. The ARIMA model used in this degree project for GP_ANS had forecast errors on short and long horizons that are lower than the forecasting model that was used by FK in March 2014. However, the absolute forecast error is lower in the actual used model than in the ARIMA and SES models for pregnancy benefit cases. The results also show that for BB_ABB the forecast errors were large in all models, but it was the lowest in the actual used model (even the absolute forecast error). This shows that random error due to laws, rules, and community changes is almost impossible to predict. Therefore, it is not feasible to predict the time series with tested models in the long-term. However, that mainly depends on what FK considers as accepted forecast errors and how those forecasts will be used. It is important to mention that the implementation of ARIMA differs across different software. The best model in the used software in this degree project SAS (Statistical Analysis System) is not necessarily the best in other software.
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Guleroglu, Cigdem. "Portfolio Insurance Strategies." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614809/index.pdf.

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The selection of investment strategies and managing investment funds via employing portfolio insurance methods play an important role in asset liability management. Insurance strategies are designed to limit downside risk of portfolio while allowing some participation in potential gain of upside markets. In this thesis, we provide an extensive overview and investigation, particularly on the two most prominent portfolio insurance strategies: the Constant Proportion Portfolio Insurance (CPPI) and the Option-Based Portfolio Insurance (OBPI). The aim of the thesis is to examine, analyze and compare the portfolio insurance strategies in terms of their performances at maturity, via some of their statistical and dynamical properties, and of their optimality over the maximization of expected utility criterion. This thesis presents the financial market model in continuous-time containing no arbitrage opportunies, the CPPI and OBPI strategies with definitions and properties, and the analysis of these strategies in terms of comparing their performances at maturity, of their statistical properties and of their dynamical behaviour and sensitivities to the key parameters during the investment period as well as at the terminal date, with both formulations and simulations. Therefore, we investigate and compare optimal portfolio strategies which maximize the expected utility criterion. As a contribution on the optimality results existing in the literature, an extended study is provided by proving the existence and uniqueness of the appropriate number of shares invested in the unconstrained allocation in a wider interval.
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Assonken, Tonfack Patrick Armand. "Modeling in Finance and Insurance With Levy-It'o Driven Dynamic Processes under Semi Markov-type Switching Regimes and Time Domains." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/6675.

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Mathematical and statistical modeling have been at the forefront of many significant advances in many disciplines in both the academic and industry sectors. From behavioral sciences to hard core quantum mechanics in physics, mathematical modeling has made a compelling argument for its usefulness and its necessity in advancing the current state of knowledge in the 21rst century. In Finance and Insurance in particular, stochastic modeling has proven to be an effective approach in accomplishing a vast array of tasks: risk management, leveraging of investments, prediction, hedging, pricing, insurance, and so on. However, the magnitude of the damage incurred in recent market crisis of 1929 (the great depression), 1937 (recession triggered by lingering fears emanating from the great depression), 1990 (one year recession following a decade of steady expansion) and 2007 (the great recession triggered by the sub-prime mortgage crisis) has suggested that there are certain aspects of financial markets not accounted for in existing modeling. Explanations have abounded as to why the market underwent such deep crisis and how to account for regime change risk. One such explanation brought forth was the existence of regimes in the financial markets. The basic idea of market regimes underscored the principle that the market was intrinsically subjected to many different states and can switch from one state to another under unknown and uncertain internal and external perturbations. Implementation of such a theory has been done in the simplifying case of Markov regimes. The mathematical simplicity of the Markovian regime model allows for semi-closed or closed form solutions in most financial applications while it also allows for economically interpretable parameters. However, there is a hefty price to be paid for such practical conveniences as many assumptions made on the market behavior are quite unreasonable and restrictive. One assumes for instance that each market regime has a constant propensity of switching to any other state irrespective of the age of the current state. One also assumes that there are no intermediate states as regime changes occur in a discrete manner from one of the finite states to another. There is therefore no telling how meaningful or reliable interpretation of parameters in Markov regime models are. In this thesis, we introduced a sound theoretical and analytic framework for Levy driven linear stochastic models under a semi Markov market regime switching process and derived It\'o formula for a general linear semi Markov switching model generated by a class of Levy It'o processes (1). It'o formula results in two important byproducts, namely semi closed form formulas for the characteristic function of log prices and a linear combination of duration times (2). Unlike Markov markets, the introduction of semi Markov markets allows a time varying propensity of regime change through the conditional intensity matrix. This is more in line with the notion that the market's chances of recovery (respectively, of crisis) are affected by the recession's age (respectively, recovery's age). Such a change is consistent with the notion that for instance, the longer the market is mired into a recession, the more improbable a fast recovery as the the market is more likely to either worsens or undergo a slow recovery. Another interesting consequence of the time dependence of the conditional intensity matrix is the interpretation of semi Markov regimes as a pseudo-infinite market regimes models. Although semi Markov regime assume a finite number of states, we note that while in any give regime, the market does not stay the same but goes through an infinite number of changes through its propensity of switching to other regimes. Each of those separate intermediate states endows the market with a structure of pseudo-infinite regimes which is an answer to the long standing problem of modeling market regime with infinitely many regimes. We developed a version of Girsanov theorem specific to semi Markov regime switching stochastic models, and this is a crucial contribution in relating the risk neutral parameters to the historical parameters (3). Given that Levy driven markets and regime switching markets are incomplete, there are more than one risk neutral measures that one can use for pricing derivative contracts. Although much work has been done about optimal choice of the pricing measure, two of them jump out of the current literature: the minimal martingale measure and the minimum entropy martingale measure. We first presented a general version of Girsanov theorem explicitly accounting for semi Markov regime. Then we presented Siu and Yang pricing kernel. In addition, we developed the conditional and unconditional minimum entropy martingale measure which minimized the dissimilarity between the historical and risk neutral probability measures through a version of Kulbach Leibler distance (4). Estimation of a European option price in a semi Markov market has been attempted before in the restricted case of the Black Scholes model. The problems encountered then were twofold: First, the author employed a Markov chain Monte Carlo methods which relied much on the tractability of the likelihood function of the normal random sequences. This tractability is unavailable for most Levy processes, hence the necessity of alternative pricing methods is essential. Second, the accuracy of the parameter estimates required tens of thousands of simulations as it is often the case with Metropolis Hasting algorithms with considerable CPU time demand. Both above outlined issues are resolved by the development of a semi-closed form expression of the characteristic function of log asset prices, and it opened the door to a Fourier transform method which is derived on the heels of Carr and Madan algorithm and the Fourier time stepping algorithm (5). A round of simulations and calibrations is performed to better capture the performance of the semi Markov model as opposed to Markov regime models. We establish through simulations that semi Markov parameters and the backward recurrence time have a substantial effect on option prices ( 6). Differences between Markov and Semi Markov market calibrations are quantified and the CPU times are reported. More importantly, interpretation of risk neutral semi Markov parameters offer more insight into the dynamic of market regimes than Markov market regime models ( 7). This has been systematically exhibited in this work as calibration results obtained from a set of European vanilla call options led to estimates of the shape and scale parameters of the Weibull distribution considered, offering a deeper view of the current market state as they determine the in-regime dynamic crucial to determining where the market is headed. After introducing semi Markov models through linear Levy driven models, we consider semi Markov markets with nonlinear multidimensional coupled asset price processes (8). We establish that the tractability of linear semi Markov market models carries over to multidimensional nonlinear asset price models. Estimating equations and pricing formula are derived for historical parameters and risk neutral parameters respectively (9). The particular case of basket of commodities is explored and we provide calibration formula of the model parameters to observed historical commodity prices through the LLGMM method. We also study the case of Heston model in a semi Markov switching market where only one parameter is subjected to semi Markov regime changes. Heston model is one the most popular model in option pricing as it reproduces many more stylized facts than Black Scholes model while retaining tractability. However, in addition to having a faster deceasing smiles than observed, one of the most damning shortcomings of most diffusion models such as Heston model, is their inability to accurately reproduce short term options prices. An avenue for solving these issues consists in generalizing Heston to account for semi Markov market regimes. Such a solution is implemented and a semi analytic formula for options is obtained.
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Yan, Yuxing. "Three essays on financial intermediation." Thesis, McGill University, 1998. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=35654.

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This dissertation consists of three essays: (I) Double Liability, Moral Hazard and Deposit Insurance Schemes, (II) Contract Costs, Lender Identity and Bank Loan Pricing, and (III) Bank Capital Structure and Differential Lending Behaviour. The first essay proposes to add double liability to a deposit insurance scheme to induce insurees (depository financial institutions) to reveal their true risk types. The second essay looks at the differential lending patterns of American banks versus Japanese banks. The third essay discusses the relationship between the characteristics of a lender and those of the borrower.
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Li, Jiang. "Financial Mathematics Project." Digital WPI, 2012. https://digitalcommons.wpi.edu/etd-theses/263.

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This project describes the underlying principles of Modern Portfolio Theory, the Capital Asset Pricing Model (CAPM), and multi-factor models in detail, explores the process of constructing optimal portfolios using the Modern Portfolio Theory, estimates the expected return and covariance matrix of assets using CAPM and multi-factor models, and finally, applies these models in real markets to analyze our portfolios and compare their performances.
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Dang, Zhe. "Financial Mathematics Project." Digital WPI, 2012. https://digitalcommons.wpi.edu/etd-theses/262.

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This project describes the underlying principles of Modern Portfolio Theory (MPT), the Capital Asset Pricing Model (CAPM), and multi-factor models in detail. It also explores the process of constructing optimal portfolios using Modern Portfolio Theory, as well as estimates the expected return and covariance matrix of assets using the CAPM and multi-factor models. Finally, the project applies these models in real markets to analyze our portfolios and compare their performances.
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Zhou, Junhua, and 周俊华. "To survive and succeed in the risky financial world: applications of mathematical optimization in finance andinsurance." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B44407579.

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Lindensjö, Kristoffer. "Essays in financial mathematics." Doctoral thesis, Handelshögskolan i Stockholm, Institutionen för Finansiell ekonomi, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:hhs:diva-2145.

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Books on the topic "Financial and Insurable Mathematics"

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Cipra, Tomas. Financial and insurance formulas. Heidelberg: Physica-Verlag, 2010.

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Ottaviani, G. Financial Risk in Insurance. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000.

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Advanced financial modelling. Berlin: Walter de Gruyter, 2009.

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Ruckman, Chris. Financial mathematics: A practical guide for actuaries and other business professionals. Weatogue, CT: BPP Professional Education, 2004.

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1965-, Francis Joe, and BPP Professional Education, eds. Financial mathematics: A practical guide for actuaries and other business professionals. 2nd ed. Weatogue, CT: BPP Professional Education, 2005.

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Wüthrich, Mario V. Financial Modeling, Actuarial Valuation and Solvency in Insurance. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Olivieri, Annamaria. Introduction to insurance mathematics: Technical and financial features of risk transfers. Berlin: Springer Verlag, 2011.

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Ser-Huang, Poon, and Rockinger Michael, eds. Financial Modeling Under Non-Gaussian Distributions. London: Springer London, 2007.

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1946-, Herzog Thomas N., and London Richard L, eds. Models for quantifying risk. 3rd ed. Winsted, Conn: ACTEX Publications, 2008.

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1950-, Duncan Ian G., Camilli Stephen J. 1976-, and Cunningham Robin J. 1965-, eds. Models for quantifying risk. Winsted, CT: ACTEX Publications, 2014.

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Book chapters on the topic "Financial and Insurable Mathematics"

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Olivieri, Annamaria, and Ermanno Pitacco. "Pension plans: technical and financial perspectives." In Introduction to Insurance Mathematics, 373–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16029-5_8.

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Asmussen, Søren, and Mogens Steffensen. "Chapter VI: Financial Mathematics in Life Insurance." In Risk and Insurance, 141–87. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-35176-2_6.

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Cipra, Tomas. "Mathematical Compendium." In Financial and Insurance Formulas, 275–81. Heidelberg: Physica-Verlag HD, 2010. http://dx.doi.org/10.1007/978-3-7908-2593-0_25.

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Cipra, Tomas. "Descriptive and Mathematical Statistics." In Financial and Insurance Formulas, 307–29. Heidelberg: Physica-Verlag HD, 2010. http://dx.doi.org/10.1007/978-3-7908-2593-0_27.

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Wallis, W. D. "Financial Mathematics." In A Beginner's Guide to Finite Mathematics, 389–415. Boston: Birkhäuser Boston, 2012. http://dx.doi.org/10.1007/978-0-8176-8319-1_8.

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Konstantin, Panos, and Margarete Konstantin. "Financial Mathematics." In Power and Energy Systems Engineering Economics, 5–25. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-72383-9_2.

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Gupta, A. K., and T. Varga. "Financial Mathematics." In An Introduction to Actuarial Mathematics, 1–79. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-017-0711-4_1.

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Peren, Franz W. "Financial Mathematics." In Math for Business and Economics, 141–248. Berlin, Heidelberg: Springer Berlin Heidelberg, 2021. http://dx.doi.org/10.1007/978-3-662-63249-9_7.

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Campolieti, Giuseppe, and Roman N. Makarov. "Replication and Pricing in the Binomial Tree Model." In Financial Mathematics, 331–96. 2nd ed. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429503665-7.

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Campolieti, Giuseppe, and Roman N. Makarov. "Introduction to Discrete-Time Stochastic Calculus." In Financial Mathematics, 271–330. 2nd ed. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429503665-6.

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Conference papers on the topic "Financial and Insurable Mathematics"

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Hornyak Greganova, Radomira. "FINANCIAL AND INSURANCE MATHEMATICS IN THE CONTEXT OF ECONOMIC AND MANAGERIAL UNIVERSITY EDUCATION." In 10th annual International Conference of Education, Research and Innovation. IATED, 2017. http://dx.doi.org/10.21125/iceri.2017.1991.

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JORDAN, RICHARD, and CHARLES TIER. "ASYMPTOTIC APPROXIMATIONS IN FINANCIAL MATHEMATICS." In Proceedings of the 2008 Conference on FACM'08. WORLD SCIENTIFIC, 2008. http://dx.doi.org/10.1142/9789812835291_0027.

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Шумилина, Вера, Vera Shumilina, Алина Рощупкина, and Alina Roschupkina. "TAXES AS A FACTOR OF ECONOMIC AND FINANCIAL SECURITY." In Mathematics in Economics. AUS PUBLISHERS, 2018. http://dx.doi.org/10.26526/conferencearticle_5c24b1cf28fc52.31439246.

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The article discusses the state and tax policy in the field of economic and financial security. The essence of the components of national security is considered: economic, financial and tax. The assessment of the effectiveness of tax security factors is presented.
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Paula Lopes, Ana, and Filomena Soares. "The Experience of a Financial Mathematics Flipped Classroom." In 2nd International Scientific Conference »Teaching Methods for Economics and Business Sciences«. University of Maribor Press, 2019. http://dx.doi.org/10.18690/978-961-286-285-5.10.

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Pan, Zhongyu. "Option Pricing Analysis Based on Financial Mathematics Technology." In 2017 2nd International Conference on Education, Sports, Arts and Management Engineering (ICESAME 2017). Paris, France: Atlantis Press, 2017. http://dx.doi.org/10.2991/icesame-17.2017.289.

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Nacher, J. C., T. Ochiai, Theodore E. Simos, George Psihoyios, Ch Tsitouras, and Zacharias Anastassi. "Linking Financial Market Dynamics and the Impact of News." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2011: International Conference on Numerical Analysis and Applied Mathematics. AIP, 2011. http://dx.doi.org/10.1063/1.3636984.

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Soleymani, Fazlollah. "Option pricing under a financial model with stochastic interest rate." In SECOND INTERNATIONAL CONFERENCE OF MATHEMATICS (SICME2019). Author(s), 2019. http://dx.doi.org/10.1063/1.5097822.

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Su, Ya-Ran, Yan-Ping Nie, and Xi-Xian Niu. "Enterprise financial risk evaluation research based on fuzzy mathematics." In 2010 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2010. http://dx.doi.org/10.1109/icmlc.2010.5580548.

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Huang, Yan. "Manifold Learning for Financial Market Visualization." In ICMAI 2020: 2020 5th International Conference on Mathematics and Artificial Intelligence. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3395260.3395297.

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Sun, Wei. "Research on Company's Financial Performance Evaluation Basing on Fuzzy Mathematics." In 2010 International Conference on Management and Service Science (MASS 2010). IEEE, 2010. http://dx.doi.org/10.1109/icmss.2010.5578311.

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Reports on the topic "Financial and Insurable Mathematics"

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Khrunichev, R. V. Distance learning course "Financial mathematics", training direction 38.03.05 " Business Informatics". OFERNIO, June 2018. http://dx.doi.org/10.12731/ofernio.2018.23678.

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