Portfolio Of Financial Instruments Research Articles

Особенности конструирования портфеля финансовых инструментов на основе оценки бета-коэффициентов

The article focuses on a variant of the development of the classical portfolio theory based on coefficients. The role of beta coefficients in financial decisionmaking is analyzed, and a portfolio optimization option is presented in the form of a linear programming problem with respect to the price shares of financial instruments acting as variables. Special attention is paid to the mathematical approaches underlying portfolio theory. Such approaches include statistical analysis to assess the expected profitability and coefficients of various financial instruments, as well as optimization methods to determine the optimal balance between the profitable and risky characteristics of the portfolio. Due to the choice of various options for the value of the beta coefficient of the portfolio, the fundamental possibility of designing optimal portfolios taking into account investment goals, risk level and desired profitability is demonstrated. Three investment strategies have been formulated based on the inclusion in the portfolio of financial instruments with high coefficient values; inclusion in the portfolio of financial instruments with low coefficient values and inclusion in the portfolio of financial instruments with average coefficient values. Based on real financial data, the differences in financial results resulting from the choice of each of these strategies are demonstrated. From a methodological point of view, the material of the article can be useful for improving the content of mathematical disciplines related to the quantitative justification of financial decisions.

Legal Understanding of Management Decisions in Hydrocarbons Cross-Border Marine Shipping: Digital Hedging Technologies

The paper examines one of the areas of law application that is methodologically based on an individualized approach to the norms of law with their subsequent broad interpretation in relation to specific legal situations. Similarly, the vector of rule-making is determined in relation to the transportation of crude oil and its refined products entirely by sea. The work not only reflects the basic doctrines of legal understanding, but also presents an assessment of their law enforcement potential, the ability to influence the formation of sectoral, intersectoral institutions of law. The author examines financial intermediation and hedging that performs the same dealer functions, but with much lower costs than classical banking institutions. Implementation of the institution of a derivative credit instrument in law enforcement practice is of particular interest to the author. The paper considers such a phenomenon as confrontation between natural and positive law in the context of entrepreneurial activity. Transportation of petroleum products’ tenders is considered as a compensated use of property. The positivist legal understanding is seen by the author as no less important. The paper explains to what extent it can serve as a basis for making management decisions concerning market risks, risks of changes in portfolios of financial instruments, risks of probable losses.Transactions with a digital asset quite reasonably belong to the field of hedging, if there is a causal relationship between the types of the underlying asset and the hedging strategy, using digital examples. Connecting factors to the law of the payment currency, terms and amounts of payments are considered both in line with hedging strategies and utilitarially in the context of making a record of the rights’ transfer. The author draws a conclusion concerning the purpose of short and long positions of net assets hedged against margin trading limits if the security of an oil transportation transaction is found to be insufficient. Merchants receive borrowed capital secured by their own assets.Management solutions ensure the sequence of identification of all relevant risks, facilitate attracting exclusively licensed counterparties for the formation of loan coverage, regularly replenishing the reserves of liquid funds when conducting transactions with virtual currencies and other impersonal (in personal terms) assets. With management decisions being considered as one of the measures of value, hedging is similar to a loan agreement, since they have a common goal — to gain mandatory tranche for the derivative financial instruments that invariably maintain a high level of liquidity and volatility.

Capitalizing on the global financial interest in blue carbon

Natural climate solutions are crucial interventions to help countries and companies achieve their net-zero carbon emissions ambitions. Blue carbon ecosystems such as mangroves, seagrasses, and tidal marshes have attracted particular attention for their ability to sequester and store carbon at densities that can far exceed other ecosystems. The science of blue carbon is now clear, and there is substantial interest from companies and individuals who wish to offset greenhouse gas emissions that they cannot otherwise reduce. We characterise the rapid recent rise in interest in blue carbon ecosystems from the corporate sector and highlight the huge scale of demand (potentially $10 billion or more) from companies and investors. We discuss why, despite this interest and demand, the supply of blue carbon credits remains small. Several market-related challenges currently limit the implementation of blue carbon projects and the sale of resulting credits, including the cost and burden of verification of blue carbon compared to verifying carbon credits in other ecosystems, the general small scale of current blue carbon projects, and double counting of credits between commercial and national institutions. To overcome these challenges, we discuss other supplementary financial instruments beyond carbon credit trading that may also be viable to fund the conservation and restoration of coastal habitats, such as bonds and ecosystem service insurance. Ultimately, a portfolio of financial instruments will be needed in order to generate funding streams that are substantial and reliable enough to realise the potential of blue carbon ecosystems as a natural climate solution.

Static hedging of weather and price risks in electricity markets

We present the closed-form solution to the problem of hedging price and quantity risks for energy retailers (ER), using financial instruments based on electricity price and weather indexes. Our model considers an ER who is intermediary in a regulated electricity market. ERs buy a fixed quantity of electricity at a variable cost and must serve a variable demand at a fixed cost. Thus ERs are subject to both price and quantity risks. To hedge such risks, an ER could construct a portfolio of financial instruments based on price and weather indexes. We construct the closed form solution for the optimal portfolio for the mean-VaR model in the discrete setting. Our model does not make any distributional assumption.

A utility adjusted newsvendor model with stochastic demand

We develop a continuous time Newsvendor model to determine the optimal inventory level for commodities in an established financial market. Unlike most models in literature, the newsvendor is not necessarily risk-neutral and chooses the order quantity that maximises the expected utility of the cash flow at the end of the period. The newsvendor exploits the correlation between stochastic demand and the price of the commodities, in order to manage risks by investing in a portfolio of financial instruments. The decision problem, therefore, includes not only the determination of the optimal ordering policy, but also, at the same time, the selection of a portfolio that maximises her utility. Further, we demonstrate that our model, compared to a myopic approach (i.e. no hedging), requires significantly lower levels of inventory buffer to mitigate demand uncertainty, thus better utilising capital.

From Word to Financial Time Series Embedding

Pre-trained word vectors contributed to state of the art results in many Natural Language Processing tasks. In this work, we show that the same algorithms can be applied to the embedding of financial time series. Pre-trained vectors for financial time series are useful for visualizing an investable universe, embedding a portfolio of financial instruments, and can be used as context vectors in larger NLP networks.

Rozwój giełdowego rynku finansowego w Federacji Rosyjskiej na przykładzie Moskiewskiej Giełdy Papierów Wartościowych

The aim of the article is to evaluate the development of the stock exchange financial market in the Russian Federation and to indicate its basic characteristics. Two segments of this market were analyzed: foreign exchange market and capital market. The research period encompasses years of 2012–2016 and the starting point was the first year of operation of the Moscow Exchange (MOEX) after the merger of two exchanges, namely the Moscow Interbank Currency Exchange (MICEX) and the Russian Trading System (RTS). The analysis of turnover and capitalization statistics (in the case of equity and bond markets) as well as the portfolio of financial instruments of the local stock exchange were used as a research method. The following thesis has been adopted in the paper: stock market in Russia, as a result of the strong diversification of its offer, gives investors a wide range of investment opportunities. The structure of the stock market in Russia is different from that seen in developed financial markets in other countries.

Application of conditional value at risk for credit risk optimization

The article is dedicated to the optimization of credit risk through the application of Conditional Value at Risk (CVaR). CVaR is a risk measure, the expected loss exceeding Value-at-Risk and is also known as Mean Excess, Mean Shortfall, or Tail VaR. The link between credit risk and the current financial crisis accentuates the importance of measuring and predicting extreme credit risk. Conditional Value at Risk has become an increasingly popular method for measurement and optimization of extreme market risk. The use of model can regulate all positions in a portfolio of financial instruments in order to minimize CVaR subject to trading and return constraints at the same time. The credit risk distribution is created by Monte Carlo simulations and the optimization problem is solved effectively by linear programming. We apply these CVaR techniques to the optimization of credit risk on portfolio of selected bonds.                  Keywords: value at risk; conditional value at risk; credit risk; portfolio

Application of conditional value at risk for credit risk optimization

The article is dedicated to the optimization of credit risk through the application of Conditional Value at Risk (CVaR). CVaR is a risk measure, the expected loss exceeding Value-at-Risk and is also known as Mean Excess, Mean Shortfall, or Tail VaR. The link between credit risk and the current financial crisis accentuates the importance of measuring and predicting extreme credit risk. Conditional Value at Risk has become an increasingly popular method for measurement and optimization of extreme market risk. The use of model can regulate all positions in a portfolio of financial instruments in order to minimize CVaR subject to trading and return constraints at the same time. The credit risk distribution is created by Monte Carlo simulations and the optimization problem is solved effectively by linear programming. We apply these CVaR techniques to the optimization of credit risk on portfolio of selected bonds. Keywords: value at risk; conditional value at risk; credit risk; portfolio

Gambling for Quants, Part 1: A Simple Fractional Betting System

This is the first of a “Gambling for Quants” series that discusses the science of gambling for quantitatively trained market practitioners. Part 1 describes the well-known Kelly Criterion fractional betting system for simple coin flips. Most of the important ideas are understandable in this simple setting, but its generalization to portfolios of coins, and then to portfolios of financial instruments will be undertaken in later parts.

Methods and Models of Market Risk Stress-Testing of the Portfolio of Financial Instruments

Amid instability of financial markets and macroeconomic situation the necessity of improving bank risk-management instrument arises. New economic reality defines the need for searching for more advanced approaches of estimating banks vulnerability to exceptional, but plausible events. Stress-testing belongs to such instruments. The paper reviews and compares the models of market risk stress-testing of the portfolio of different financial instruments. These days the topic of the paper is highly acute due to the fact that now stress-testing is becoming an integral part of anticrisis risk-management amid macroeconomic instability and appearance of new risks together with close interest to the problem of risk-aggregation. The paper outlines the notion of stress-testing and gives coverage of goals, functions of stress-tests and main criteria for market risk stress-testing classification. The paper also stresses special aspects of scenario analysis. Novelty of the research is explained by elaborating the programme of aggregated complex multifactor stress-testing of the portfolio risk based on scenario analysis. The paper highlights modern Russian and foreign models of stress-testing both on solo-basis and complex. The paper lays emphasis on the results of stress-testing and revaluations of positions for all three complex models: methodology of the Central Bank of stress-testing portfolio risk, model relying on correlations analysis and copula model. The models of stress-testing on solo-basis are different for each financial instrument. Parametric StressVaR model is applicable to shares and options stress-testing;model based on "Grek" indicators is used for options; for euroobligation regional factor model is used. Finally some theoretical recommendations about managing market risk of the portfolio are given.

Filtered Historical Simulation Value-at-Risk Models and Their Competitors

Financial institutions have for many years sought measures which cogently summarise the diverse market risks in portfolios of financial instruments. This quest led institutions to develop Value-at-Risk (VaR) models for their trading portfolios in the 1990s. Subsequently, so-called filtered historical simulation VaR models have become popular tools due to their ability to incorporate information on recent market returns and thus produce risk estimates conditional on them. These estimates are often superior to the unconditional ones produced by the first generation of VaR models. This paper explores the properties of various filtered historical simulation models. We explain how these models are constructed and illustrate their performance, examining in particular how filtering transforms various properties of return distribution. The procyclicality of filtered historical simulation models is also discussed and compared to that of unfiltered VaR. A key consideration in the design of risk management models is whether the model’s purpose is simply to estimate some percentile of the return distribution, or whether its aims are broader. We discuss this question and relate it to the design of the model testing framework. Finally, we discuss some recent developments in the filtered historical simulation paradigm and draw some conclusions about the use of models in this tradition for the estimation of initial margin requirements.

ELEMENTS OF CONTROL THEORY APPLIED TO AN INVESTMENT PORTFOLIO IN THE CAPITAL MARKET. THE OPTIMAL TIME HORIZON FOR SELLING A PORTFOLIO

The article presents a concept of capital management for assembling investment portfolios. Two optimization variants of a portfolio to be purchased are discussed. Portfolio I is structural, using the „traditional model”. To assemble Portfolio II, elements of reliability theory and the dynamic programming method were used. The article also analyses the sale of a portfolio with respect to the demand for financial instruments in the capital market. The presented concept dealing with rational investment decisions during transactions at the Warsaw Stock Exchange can also be used by managers to create an effective portfolio of financial instruments.

Hedging demand and supply risks in the newsvendor model

We consider a single-period inventory model where there are risks associated with the uncertainty in demand as well as supply. Furthermore, the randomness in demand and supply is correlated with the financial markets. Recent literature provides ample evidence on this issue. The inventory manager may then exploit this correlation and manage his risks by investing in a portfolio of financial instruments. The decision problem, therefore, includes not only the determination of the optimal ordering policy, but also the selection of the optimal portfolio at the same time. We analyze this problem in detail and provide a risk-sensitive approach to inventory management where one considers both the mean and the variance of the resulting cash flow. The analysis results in some interesting and explicit characterizations on the structure of the optimal policy.

Newsvendor model with random supply and financial hedging: Utility-based approach

This paper takes a utility-based approach to the single-period and single-item newsvendor model. Unlike most models in the literature the newsvendor is not necessarily risk-neutral and chooses the order quantity that maximizes the expected utility of the cash flow at the end of the period. We suppose that there is uncertainty in demand as well as supply. Furthermore, random demand and supply may be correlated with the financial markets. The newsvendor exploits this correlation and manages his risks by investing in a portfolio of financial instruments. The decision problem therefore includes not only the determination of the optimal ordering policy, but also the selection of the optimal portfolio at the same time. We first use a minimum-variance approach to select the portfolio. The analysis results in some interesting and explicit characterizations on the structure of the optimal policy. We also present numerical examples to illustrate the effects of the parameters on the optimal order quantity, and the importance of financial hedging on risk reduction.

Capital Requirements and Optimal Investment with Solvency Probability Constraints

Quantifying the economic capital and optimally allocating it into portfolios of financial instruments are two key topics in the asset/liability management (ALM) of an insurance company. In general these problems are studied in the literature by minimizing standard risk measures such as the value at risk (VaR) and the conditional value at risk (CVaR). Motivated by Solvency II regulations, we introduce a novel risk-return'' optimization problem to solve for the optimal required capital and the portfolio structure when the ruin probability is used as an insurance solvency constraint. The proposed approach relies on a semi-parametric setting using scenario-based asset returns and different parametric assumptions for the liability distribution. Extensive simulations are provided to assess the sensitivity and robustness of our solutions relative to model factors.

Capital requirements and optimal investment with solvency probability constraints

Quantifying economic capital and optimally allocating it into portfolios of financial instruments are two key topics in the asset–liability management of an insurance company. In general, these problems are studied in the literature by minimizing standard risk measures such as the value at risk and the conditional VaR. Motivated by Solvency II regulations, we introduce a novel optimization problem to solve for the optimal required capital and the portfolio structure simultaneously, when the ruin probability is used as an insurance solvency constraint. Besides the generic optimal required capital and portfolio problem formulation, we propose a two-model hierarchy of optimization models, where both models admit the so-called second-order conic reformulation, in turn making them particularly well suited for numerics. The first model, albeit naively asserting the normality of the returns on assets and liabilities, under minor further simplifications admits a closed-form solution—a set of formulas, which may be used as simple decision-making guidelines in the analysis of more complex scenarios. A potentially more realistic second model aims to represent the ‘heavy-tailed’ nature of an insurer's liabilities more accurately, while also allowing arbitrary distributions of asset returns via a semi-parametric approach. Extensive numerical simulations illustrate the sensitivity and robustness of the proposed approach relative to the model's parameters. In addition, we explore the potential of insurance risk diversification and discuss if combining several liabilities into a single insurance portfolio may always be beneficial for the insurer. Finally, we propose an extension of the model with an expected return on capital constraint added.

A Comparison of EVT and Standard VaR Estimations

In this paper, Extreme value theory (EVT) is applied in estimating low quantiles of P/L distribution and the results are compared to common VaR methodologies. The fundamental theory behind EVT is built, and peaks-over-threshold method is used for modeling the tail of the distribution of losses with Generalized Pareto Distribution (GPD). The different VaR methods are then compared using backtesting procedures. Practical issues such as time varying volatility of returns, and multivariate time series (portfolio of financial instruments) are covered.

Valuation portfolio in non-life insurance

The purpose of this paper is the construction of the Valuation Portfolio (VaPo) for a non-life insurance company. The VaPo represents the obligations of the insurer for the whole period of his insurance contracts. These obligations are not simply measured by a one-dimensional figure (as standard for reserves in practice), but are expressed as a portfolio of financial instruments. Hence, the actuarial reserves become multidimensional. The financial instruments in the VaPo are a properly chosen basis to represent the future cashflows resulting from the insurance contracts. In this setup, financial and technical risks are clearly separated. The financial fluctuations derive from the basis elements, the technical fluctuations are covered by an increased number of basis elements (VaPo protected against technical risks). We show how this protection can be calculated in the case of a non-life insurance company.

Optimality conditions in portfolio analysis with general deviation measures

Optimality conditions are derived for problems of minimizing a generalized measure of deviation of a random variable, with special attention to situations where the random variable could be the rate of return from a portfolio of financial instruments. Generalized measures of deviation go beyond standard deviation in satisfying axioms that do not demand symmetry between ups and downs. The optimality conditions are applied to characterize the generalized master funds which elsewhere have been developed in extending classical portfolio theory beyond the case of standard deviation. The consequences are worked out for deviation based on conditional value-at-risk and its variants, in particular.