The risk–return profile of Lithuanian private pension funds

The introduction of a private pension funds in conjunction with the public social security system is the essence of pension system reform that was implemented in Lithuania. The performance of private funds is mainly presented by fund’s net asset value and few classical risk estimates. Such evaluation shows the management company’s ability to profitably invest funds, but does not give the evidential risk– return evaluation. This paper refers to the overall statistical analysis of 26 private pension funds over a certain time period. The objective of the research is to determine the risk–return profile of pension funds and to answer the question whether the categories specified based on investment strategy in equities reflect fund’s empirical behaviour. Research methodology includes the statistical analysis, risk measuring, performance ratio estimation, and K-means clustering. The conclusions obtained by the research allow determining whether the distinct pension funds have beaten a low risk reference and are adequately assigned to a certain risk categor

Stochastic programming framework for Lithuanian pension payout modelling

The paper provides a scientific approach to the problem of selecting a pension fund by taking into account some specific characteristics of the Lithuanian Republic (LR) pension accumulation system. The decision making model, which can be used to plan a long-term pension accrual of the Lithuanian Republic (LR) citizens, in an optimal way is presented. This model focuses on factors that influence the sustainability of the pension system selection under macroeconomic, social and demographic uncertainty. The model is formalized as a single stage stochastic optimization problem where the long-term optimal strategy can be obtained based on the possible scenarios generated for a particular participant. Stochastic programming methods allow including the pension fund rebalancing moment and direction of investment, and taking into account possible changes of personal income, changes of society and the global financial market. The collection of methods used to generate scenario trees was found useful to solve strategic planning problems

Copula effect on investment portfolio of an insurance company

For an insurance company, the planning has to be carried out under uncertainty. Thus, the decision making model includes the parameters that are not completely known at the current point of time, when the decision has to be taken. These parameters can be named as risk factors. The activity of insurance company is affected by many risk factors, thus the multivariate uncertainty space, where the correlations among these factors are possible, can be constructed. For their dependency structure, the alternative method – copula functions – are employed, which allows to model the non‐linear dependencies between the correlated stochastic variables. The purpose of this work is to explore the copula effect on the investment portfolio of the insurance company. The insurance business is influenced by a large number of stochastic parameters, and decisions concerning the assets that must be invested over time to cover liabilities and to achieve goals subject to various uncertainties and various constraints are considered. Two approaches of making decision models under uncertainty are applied in the integrated dynamic management of insurance company's financial assets and liabilities. One of them allows to evaluate the company's strategy and technically is based on stochastic simulation. The other approach generates a strategy from the stochastic optimization model. Two copula functions – Gaussian copula and Student's t‐copula – concerning the investment performance are employed while generating the set of scenarios for representing the behaviour of risk factors in the multivariate structure. Junginių įtakos draudimo kompanijos investiciniam portfeliui tyrimas Santrauka. Sprendimai, susiję su ilgalaikiu draudimo kompanijos veiklos planavimu, yra dažniausiai veikiamiaplinkos neapibrėžtumų, todėl dalis ar visi modelio parametrai nėra iki galo žinomi tuo momentu, kai sprendimai turi būti priimti. Šie parametrai dar vadinami rizikos veiksniais. Kadangi draudimo kompanijos veiklai įtaką daro tam tikra rizikos faktorių aibė, tai formuojama daugiamatė neapibrėžties erdvė su galimomis priklausomybėms tarp šios erdvės kintamųjų – rizikos veiksnių. Jų priklausomybei modeliuoti taikoma alternatyvi metodika – junginiai: tai funkcijos, leidžiančios aprašyti netiesines priklausomybes tarp stochastinių kintamųjų, nebūtinai pasiskirsčiusių pagal normalųjį dėsnį. Šio darbo tikslas – ištirti junginių įtaką draudimo kompanijos sprendimams, susijusiems su ilgalaikiu investicinės veiklos planavimu. Tokio tipo uždaviniuose egzistuoja gan daug stochastinių parametrų, ir sprendimai turi būti parenkami taip, kad grąža iš investuojamo turto leistų padengti draudimo kompanijos įsipareigojimus laikui einant bei uždirbti kuo daugiau pelno. Tikslui pasiekti taikomos dvi skirtingos sprendimų priėmimo modeliavimo metodikos: tai imitacinis modeliavimas ir stochastinis programavimas (optimizavimas). Šios metodikos daugiausia skiriasi tuo, kaip parenkami sprendimai: stochastinis imitavimas remiasi efektyvaus rinkinio koncepcija, o taikant antrąjį metodą sprendimai gaunami iš daugelio tochastinio optimizavimo modelio etapų. Bendra savybė yra ta, kad stochastinis procesas, aprašantis investicinės veiklos rizikos faktorius, yra esminė įvestis į abu modelius ir yra apibrėžiamas sudarant scenarijų aibes. Eksperimento pavyzdyje taikomos dvi funkcijos – Gauso junginys ir Studento t2 junginys – šių rizikos veiksnių priklausomybėms aprašyti generuojant scenarijų aibes. Reikšminiai žodžiai: stochastinis imitavimas, stochastinis optimizavimas, scenarijų generavimas,sprendimų priėmimas, junginių funkcijos, investicinis portfelis, turto ir įsipareigojimų valdymas, draudimas. First published online: 21 Oct 201

Key Roles of Crypto-Exchanges in Generating Arbitrage Opportunities

The evolving crypto-currency market is seen as dynamic, segmented, and inefficient, coupled with a lack of regulatory oversight, which together becomes conducive to observing the arbitrage. In this context, a crypto-network is designed using bid/ask data among 20 crypto-exchanges over a 2-year period. The graph theory technique is employed to describe the network and, more importantly, to determine the key roles of crypto-exchanges in generating arbitrage opportunities by estimating relevant network centrality measures. Based on the proposed arbitrage ratio, Gatecoin, Coinfloor, and Bitsane are estimated as the best exchanges to initiate arbitrage, while EXMO and DSX are the best places to close it. Furthermore, by means of canonical correlation analysis, we revealed that higher volatility and the decreasing price of dominating crypto-currencies and CRIX index signal bring about a more likely arbitrage appearance in the market. The findings of research include pre-tax and after-tax arbitrage opportunities

Junginių įtakos draudimo kompanijos investiciniam portfeliui tyrimas

For an insurance company, the planning has to be carried out under uncertainty. Thus, the decision making model includes the parameters that are not completely known at the current point of time, when the decision has to be taken. These parameters can be named as risk factors. The activity of insurance company is affected by many risk factors, thus the multivariate uncertainty space, where the correlations among these factors are possible, can be constructed. For their dependency structure, the alternative method - copula functions - are employed, which allows to model the non-linear dependencies between the correlated stochastic variables. The purpose of this work is to explore the copula effect on the investment portfolio of the insurance company. The insurance business is influenced by a large number of stochastic parameters, and decisions concerning the assets that must be invested over time to cover liabilities and to achieve goals subject to various uncertainties and various constraints are considered. Two approaches of making decision models under uncertainty are applied in the integrated dynamic management of insurance company's financial assets and liabilities. One of them allows to evaluate the company's strategy and technically is based on stochastic simulation. The other approach generates a strategy from the stochastic optimization model. Two copula functions - Gaussian copula and Student's t-copula - concerning the investment performance are employed while generating the set of scenarios for representing the behaviour of risk factors in the multivariate structure

Dominance tracking index for measuring pension fund performance with respect to the benchmark

This paper focuses on the performance of Lithuanian life-cycle second-pillar pension funds. Every such fund first specifies its benchmark and then attempts to follow the benchmark in some way. This is a form of regulation, meaning that every such fund is somehow regulated and controlled by the central bank authorities. The goal of this paper is twofold: (i) to analyse the returns of the pension funds with respect to their benchmarks and (ii) to determine whether less strict regulation leads to a better outperformance of the fund with respect to the benchmark. In order to achieve this, we introduced a new performance measure called the dominance-tracking index, which combines the ideas of almost stochastic dominance relations and tracking errors. While the tracking error and its modifications measure the strength of the regulation, almost stochastic dominance provides information about preferences between the funds and their benchmarks. Therefore, the new index was constructed in such a way as to take into account both approaches. The empirical section of the study then presents the results separately for the considered pension managers and participants’ age groups as usual in the life-cycle pension funds analysis. Finally, by taking into account various periods, we studied the effects of the COVID-19 crisis. Keywords: pension fund; management; regulation; life-cycle strategy; benchmark; stochastic dominance; almost stochastic dominance; risk assessment; COVID-19

Real‐options water supply planning: Multistage scenario trees for adaptive and flexible capacity expansion under probabilistic climate change uncertainty

Planning water supply infrastructure includes identifying interventions that cost‐effectively secure an acceptably reliable water supply. Climate change is a source of uncertainty for water supply developments as its impact on source yields is uncertain. Adaptability to changing future conditions is increasingly viewed as a valuable design principle of strategic water planning. Because present decisions impact a system's ability to adapt to future needs, flexibility in activating, delaying, and replacing engineering projects should be considered in least‐cost water supply intervention scheduling. This is a principle of Real Options Analysis, which this paper applies to least‐cost capacity expansion scheduling via multistage stochastic mathematical programming. We apply the proposed model to a real‐world utility with many investment decision stages using a generalized scenario tree construction algorithm to efficiently approximate the probabilistic uncertainty. To evaluate the implementation of Real Options Analysis, the use of two metrics is proposed: the value of the stochastic solution and the expected value of perfect information that quantify the value of adopting adaptive and flexible plans, respectively. An application to London's water system demonstrates the generalized approach. The investment decisions results are a mixture of long‐term and contingency schemes that are optimally chosen considering different futures. The value of the stochastic solution shows that by considering uncertainty, adaptive investment decisions avoid £100 million net present value (NPV) cost, 15% of the total NPV. The expected value of perfect information demonstrates that optimal delay and early decisions have £50 million NPV, 6% of total NPV. Sensitivity of results to the characteristics of the scenario tree and uncertainty set is assessed