scholarly journals STRESS INDEX IN UKRAINE’S MARKET OF NEGOTIABLE FINANCIAL INSTRUMENTS

2018 ◽  
Vol 2018 (2) ◽  
pp. 39-49
Author(s):  
Igor KRAVCHUK ◽  
Keyword(s):  
Distribution Function ◽  
Composite Index ◽  
Stress Index ◽  
Seasonal Adjustment ◽  
Efficiency Coefficient ◽  
Financial Instruments ◽  
Factors Affecting ◽  
State Of The Economy ◽  
Integral Distribution ◽  
Integral Distribution Function

Market of negotiable financial instruments is an immanent component of the financial system and is in a two-way relationship with other financial institutions and real sector of the economy in terms of ensuring its stable functioning. Possible market shocks can adversely affect state of the economy; therefore regulators should carry out constant market surveillance to detect and prevent early possible market violations, by calculating (in particular) the composite stress index. To construct a composite index, correlation analysis, generalized autoregressive conditional heteroscedasticity model, standardization based on the integral distribution function, seasonal adjustment and determination of a long-term trend based on filtering are used. It is proposed to calculate the stress index of Ukraine’s market of negotiable financial instruments on the basis of market data by balanced averaging of the following sub-indices: (i) stocks (UX stock yield volatility, CMAX indicator, market efficiency coefficient); (ii) debt securities (sovereign spread and CDS spread); and (iii) derivatives (indicator of the change in the number of open futures positions for the UX stock). Aforementioned were standardized using the integral distribution function. The author’s analysis of the proposed composite stress index shows that dominant factors affecting the situation in Ukraine’s market of securities and derivatives are intra-national ones, which have become dominant since 2014. At present, the stress index of Ukraine’s market of negotiable financial instruments is still of little importance to reflect economic situation in the state, given weak development of the market and its meager role for financing and reflecting the corporate activity.

КОКУС ЧОҢДУКТАРДЫН ЫКТЫМАЛДУУЛУКТАРЫН БӨЛҮШТҮРҮҮ ФУНКЦИЯСЫН ОКУТУУНУН ЫКМАЛАРЫ

2020 ◽  
pp. 168-173
Author(s):  
Аалиева Бурул
Keyword(s):  
Distribution Function ◽  
Probability Distribution ◽  
Distribution Density ◽  
Random Variables ◽  
Random Variable ◽  
Continuous Random Variable ◽  
Integral Distribution ◽  
Integral Distribution Function ◽  
Range Of Values

Аннотация: Бөлүштүрүү функциясын, үзгүлтүксүз кокус чоңдуктардын ыктымалдуулуктарын бѳлүштүрүүнүн жиктелиш функциясы (ыктымалдуулуктун тыгыздыгы), ыктымалдуулуктарды бир калыпта бѳлуштүрүү законун аныктоо. Бөлүштүрүү функциясынын касиеттерин окутуу, далилдөө. X кокус чоңдугунун кабыл алууга мүмкүн болгон маанилери (a,b) интервалында жаткандыгынын ыктымалдуулугу бөлүштүрүү функциясынын өсүндүсүнө барабар. Түйүндүү сѳздѳр: Бөлүштурүү функциясы, үзгүлтүксүз кокус чоңдуктардын ыктымалдуулуктары, дискреттик кокус чоңдук, бөлүштүрүүнүн интегралдык функциясы, баштапкы функция. Аннотация: Определять вид непрерывной случайной величины, находить вероятность попадания случайной величины в заданный интервал по заданной функции распределения, уметь находить плотность распределения и равномерное распределения. Еще одно отличие характеристики случайных величин непрерывного действия-включение функции классификации распределения вероятностей, обнаружение первого производного функции последовательности. Следовательно, характеристика распределения вероятностей дискретных случайных величин. Свойства функции распределения обучения и доказательства. Х может быть, чтобы принять параметры диапазона значений (а, б), что функция распределения вероятностей равна приращению. Ключевые слова: Функция распределения, вероятность непрерывной случайной величины, дискретная случайная величина, интегральная функция распределения, первообразная. Annotation: Determine the type of random variable, find the probability of a random variable falling into a given interval by a given distribution function, be able to find the distribution density and uniform distribution. Properties of learning distribution function and evidence. X maybe to take the parameters of the range of values (a, b), that the probability distribution function is equal to the increment. Another difference in the characterization of continuous random variables is the inclusion of the classification function of the probability distribution, the detection of the first derivative of the sequence function. Hence, the characteristic of the probability distribution of discrete random variables Non-decreasing functions, ∫ _ (- ∞) ^ ∞▒ 〖P (x) ax = 1〗. In the case of an individual, if the values of a random variable (a, b) are located within ∫_a ^ b▒ 〖P (x) ax = 1〗 Keywords: Distribution function, probability of continuous random variable, discrete random variable, integral distribution function, antiderivative. DOI: 10.35254/bhu.2019.50.1 ВЕСТНИК БИШКЕКСКОГО ГОСУДАРСТВЕННОГО УНИВЕРСИТЕТА. No4(50) 2019 169 Аннотация: Бөлүштүрүү функциясын, үзгүлтүксүз кокус чоңдуктардын ыктымалдуулуктарын бѳлүштүрүүнүн жиктелиш функциясы (ыктымалдуулуктун тыгыздыгы), ыктымалдуулуктарды бир калыпта бѳлуштүрүү законун аныктоо. Бөлүштүрүү функциясынын касиеттерин окутуу, далилдөө. X кокус чоңдугунун кабыл алууга мүмкүн болгон маанилери (a,b) интервалында жаткандыгынын ыктымалдуулугу бөлүштүрүү функциясынын өсүндүсүнө барабар. X кокус чондугу PP(xx < xx1) ыктымалдуулукта x ден кичине маанилерди кабыл алат; X кокус чондугу xx1 ≤ xx < xx2барабарсыздыктын ыктымалдуулугу PP(xx1 ≤ xx < xx2) түрүндө канааттандырат. Үзгүлтүксүз кокус чоңдуктарды мүнөздөөнүн дагы бир башкача жолу ыктымалдуулукту бөлүштүрүүнүн жиктелиш функциясын киргизүү, тутамдык функциясынын биринчи туундусун табуу. Демек,тутамдык функция жиктелиш функциясынын баштапкы функциясы болорун, дискреттик кокус чондуктардын ыктымалдуулуктарынын бөлүштүрүүсүн мунөздөө. Жиктелиш функциясы кемибөөчү функция, ∫ ff(xx)dddd = 1 ∞ −∞ . Жекече учурда, эгерде кокус чоңдуктардын мүмкүн болгон маанилери (a,b) аралыгында жайгашса, анда � ff(xx)dddd = 1 bb aa Түйүндүү сѳздѳр: Бөлүштурүү функциясы, үзгүлтүксүз кокус чоңдуктардын ыктымалдуулуктары, дискреттик кокус чоңдук, бөлүштүрүүнүн интегралдык функциясы, баштапкы функция. Аннотация: Определять вид непрерывной случайной величины, находить вероятность попадания случайной величины в заданный интервал по заданной функции распределения, уметь находить плотность распределения и равномерное распределения. Еще одно отличие характеристики случайных величин непрерывного действия-включение функции классификации распределения вероятностей, обнаружение первого производного функции последовательности. Следовательно, характеристика распределения вероятностей дискретных случайных величин. Ключевые слова: Функция распределения, вероятность непрерывной случайной величины, дискретная случайная величина, интегральная функция распределения, первообразная. Annotation: Determine the type of random variable, find the probability of a random variable falling into a given interval by a given distribution function, be able to find the distribution density and uniform distribution. Properties of learning distribution function and evidence. X maybe to take the parameters of the range of values (a, b), that the probability distribution function is equal to the increment. Another difference in the characterization of continuous random variables is the inclusion of the classification function of the probability distribution, the detection of the first derivative of the sequence function. Keywords: Distribution function, probability of continuous random variable, discrete random variable, integral distribution function, antiderivative.

scholarly journals F(E,Lz) for Kent's Model of the Galactic Bulge?

1996 ◽  
Vol 169 ◽  
pp. 351-352
Author(s):  
Walter Dehnen
Keyword(s):  
Distribution Function ◽  
Galactic Bulge ◽  
Physical Reason ◽  
Integral Distribution ◽  
Integral Distribution Function

Using the Richardson-Lucy algorithm the two-integral distribution function (2I-DF) f(E,Lz) for Kent's (1992) Bulge model has been constructed. It turns out to be negative (and hence unphysical) at Lz ≈ Lc. The physical reason for this result and its implications are discussed.

scholarly journals Factors Affecting the Indonesia Stock Exchange: A Multi-Index Approach

2020 ◽  
Vol 11 (2) ◽  
pp. 196
Author(s):  
Didik Susilo ◽  
Sugeng Wahyudi ◽  
Irene Rini Demi Pangestuti
Keyword(s):  
Capital Market ◽  
Stock Exchange ◽  
Composite Index ◽  
Market Conditions ◽  
Factors Affecting ◽  
Daily Data ◽  
Index Approach ◽  
The World ◽  
Exchange Condition ◽  
Regional Capital

This study examines the influence of world and regional capital market conditions on the Indonesian capital market (Indonesia Stock Exchange) condition. The DJIA (Dow Jones Industrial Average) index was used as a representative of the international capital market while the Hang Seng index and the Nikkei 225 index were used as a representative of regional capital market conditions. These two indices were chosen because the Japanese capital market was one of the most advanced capital markets in the world and the Hong Kong capital market, although not as big as Japan, still played an important role in the world. The data were obtained from Yahoo Finance during the period of 2014-2018. The dependent variable was the change in the JCI (Jakarta Composite Index), while the independent variables were changes in the index of DJIA, Nikkei 225 and Hang Seng index. Using daily data analyzed by the ARIMA method (1,1), it was found that there was a significant positive effect of DJIA with lag 1 and Hang Seng index on the JCI, but no significant effect was found from the Nikkei 225 index on the JCI.

scholarly journals Post-Processing and Evaluation of Precipitation Ensemble Forecast under Multiple Schemes in Beijiang River Basin

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2631
Author(s):  
Xinchi Chen ◽  
Xiaohong Chen ◽  
Dong Huang ◽  
Huamei Liu
Keyword(s):  
Time Scales ◽  
Weather Prediction ◽  
Forecast Accuracy ◽  
Skill Score ◽  
Ensemble Forecast ◽  
Efficiency Coefficient ◽  
Forecast System ◽  
Factors Affecting ◽  
Hydrological Forecasting ◽  
Better Than

Precipitation is one of the most important factors affecting the accuracy and uncertainty of hydrological forecasting. Considerable progress has been made in numerical weather prediction after decades of development, but the forecast products still cannot be used directly for hydrological forecasting. This study used ensemble pro-processor (EPP) to post-process the Global Ensemble Forecast System (GEFS) and Climate Forecast System version 2 (CFSv2) with four designed schemes, and then integrated them to investigate the forecast accuracy in longer time scales based on the best scheme. Many indices such as correlation coefficient, Nash efficiency coefficient, rank histogram, and continuous ranked probability skill score were used to evaluate the results in different aspects. The results show that EPP can improve the accuracy of raw forecast significantly, and the scheme considering cumulative forecast precipitation is better than that only considers single-day forecast. Moreover, the scheme that considers some observed precipitation would help to improve the accuracy and reduce the uncertainty. In terms of medium- and long-term forecasts, the integrated forecast based on GEFS and CFSv2 after post-processed would be better than CFSv2 significantly. The results of this study would be a very important demonstration to remove the deviation of ensemble forecast and improve the accuracy of hydrological forecasting in different time scales.

scholarly journals Features of the financial behavior of student youth

POPULATION ◽  
2021 ◽  
Vol 24 (2) ◽  
pp. 41-52
Author(s):  
Dmitry Rogachev
Keyword(s):  
Young People ◽  
Consumer Preferences ◽  
Financial Behavior ◽  
Investment Behavior ◽  
Financial Instruments ◽  
Way Of Life ◽  
Factors Affecting ◽  
Financial Decisions ◽  
Saving Behavior ◽  
Characteristic Features

The article deals with characteristic features of the financial behavior of student youth and factors affecting it. This paper is based on the results of the author's survey that was conducted in the fall of2020and covered 1242students from 17 universities in Russia. It examines the attitude of young people to saving, consumer, and investment behavior. The author sought to finds out whether the consumer preferences of young students are consistent with their financial capabilities, and whether young people are satisfied with realization of their life plans. He analyzes the sources of income of students and the sums which they consider as savings. The problems of financial dependence of the younger generation on the older ones are discussed in this article. The differences in the financial behavior of students financially dependent on parents and those leading an independent way of life are analyzed. The article presents results of the survey of students' financial behavior related both to preservation and accumulation of capital, and to general strategies of saving behavior—saving for purchasing goods, medical treatment and services, education, etc. There are considered financial instruments used or planned to be used by the respondents in case of saving large sums of money. The article shows the differences in investment behavior, in use of financial instruments between independent and financially dependent students. There are examined discrepancies between the consumer preferences and the financial capabilities of student youth. The conducted study allows drawing a conclusion about the influence of family and parents, their attitudes, and views on the financial decisions of students, which are not always rational.

A comparative analysis of green economy development in border regions of the Silk Road economic belt

2020 ◽  
Vol 18 (3) ◽  
pp. 592-606
Author(s):  
T.B. Bardakhanova ◽  
Z.S. Eremko
Keyword(s):  
Environmental Management ◽  
Methodological Approach ◽  
Composite Index ◽  
Objective Evaluation ◽  
Silk Road ◽  
Green Economy ◽  
Factors Affecting ◽  
Border Regions ◽  
Silk Road Economic Belt ◽  
The Silk Road

Subject. The article addresses green economy development in border regions of the Silk Road Economic Belt. Objectives. The purpose of the study is to design a composite index of environmental management to analyze the green economy development. Methods. The study employs economic and mathematical approaches and methods of statistical analysis. Results. We developed groups of indicators that reflect various aspects of environmental management (environmental and resource efficiency, environmental quality of life, natural assets, institutional factors, i.e. activities and policy tools that affect the state of environment). The paper presents a methodology for quantitative assessment of the composite Environmental Management Index, which consists of several steps. Conclusions. The performed study complements the results obtained by other researchers. The presented methodology rests on the formation of a system of criteria for assessing the green economy development. The use of methodological approach permits the objective evaluation of the level of green economy development in model territories. Testing the developed methods and quantifying the composite Environmental Management Index enable to make a classification of model territories, to identify factors affecting the green economy development.

Financial inclusion in India: an application of TOPSIS

Humanomics ◽  
2016 ◽  
Vol 32 (3) ◽  
pp. 328-351 ◽  
Author(s):  
Priyanka Yadav ◽  
Anil Kumar Sharma
Keyword(s):  
Composite Index ◽  
Financial Inclusion ◽  
Critical Parameters ◽  
Infrastructure Development ◽  
Indian States ◽  
Content Type ◽  
Factors Affecting ◽  
Secondary Sources ◽  
Order Preference ◽  
Farmer Suicides

Purpose The purpose of this paper is to combine the critical parameters used to study financial inclusion into a composite index. The idea is to rank Indian states and union territories (UTs) on the basis of this index, determine change in ranks during 2011 to 2014 and identify factors affecting high/low scores on the index. Design/methodology/approach Data for the study were collected from secondary sources published by Reserve Bank of India (RBI) and Central Statistical Organization. Applying technique of order preference by similarity to ideal solution (TOPSIS), a composite multi-dimensional index of financial inclusion (IFI) has been built by using three broad parameters of penetration, availability and usage of banking services. Factors significantly influencing scores of states/UTs on IFI were identified using multiple regression analysis. Findings The value of financial inclusion for India on composite IFI has increased by 0.045 points during the study period. Share of agriculture to state gross domestic product, literacy ratio, population density, infrastructure development and farmer suicides are significant factors affecting financial inclusion. Practical implications The multi-dimensional IFI is a useful tool to measure financial inclusion using several parameters for various states/regions. The index can also be used to compare the performance of states/regions over same/different periods. Originality/value This paper is unique in its attempt to construct multi-dimensional IFI for Indian states/UTs by applying TOPSIS. It will prove useful for future researchers by combining several aspects of financial inclusion into single index.

scholarly journals 4. Factors affecting radar-meteor echo durations

1968 ◽  
Vol 33 ◽  
pp. 45-49
Author(s):  
A. Hajduk
Keyword(s):  
Distribution Function ◽  
Mass Distribution ◽  
Meteor Shower ◽  
Time Intervals ◽  
Factors Affecting ◽  
Factors Influencing ◽  
Two Factors ◽  
The Mean ◽  
Mass Distribution Function ◽  
Radar Meteor

The analysis of two factors influencing radar-meteor echo durations leads to the conclusion, that (1) the echo duration depends considerably on the train position with respect to the sensitivity contours of the radar; (2) the mean echo duration changes with respect to the radiant motion of a meteor shower. As a consequence of the factors mentioned above, the magnitude function, or the mass distribution function depends significantly on the observational conditions, as well as on the choice of the range and time intervals of the investigated sample.

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