Risk management methodology in Latvian economics

One of the topical problems of the economics sciences and practices is Latvian national economy risk management scientific basis working out. The main tasks of risk management are risk management methodology elaboration, identification of risk factors, analysis, assessment, acceptation of decision, components and principles of monitoring and adaptation to Latvia’s conditions. The main components of risks analysis were considered and gross domestic product (GDP) model has been examined for GDP forecasting. GDP forecast a risk has been evaluated using time series model and additive model with Monte-Carlo simulation method. The best GDP forecast model has been offered and comparison with real data has been made. Introduction After becoming the member of the World Trade Organization (WTO), Latvia has deliberately taken active part in the globalization processes and the process of market localization. Moreover, Latvia is in the process of intensive integration into the European Union. It means that: • Latvia is aware, which sectors of national economy will be the most appropriate in the competitive open market; • Latvia, founding on the guidelines of the World Trade Organization (WTO), has to carry out risk analysis importing animals or agricultural products. When evaluating the significance of the separate sectors, we can state the risk level of the influence of separate sectors on the increase of gross domestic product (GDP) and make suggestions, how to decrease the risk. The main goals of the research are to: • find out the significance of influence of Latvia national economy sectors’ output and unemployment level on GDP; • evaluate the potential risk factors and the consequences of their influence on GDP increase. According to the WTO requirements, Latvia has to carry out the risk analysis. The risk evaluation methods have to be based on international standards, guidelines and suggestions worked out by corresponding international organizations. Until now there was no scientific research done on risk methodology in Latvia, therefore it is indispensable to work out the principles of risk methodology in three main components: risk assessment, risk management and risk monitoring. The main objectives are to find out economically the most significant potential risk types and factors, to find out and examine the economical threat of negative risk factors, and to assess the economical consequences of risks or their groups. The results of research will help the farmers, businessmen, state institutions and local authorities to make economically based preventive appropriate decisions in order to maximize potential gains and to minimize losses caused by risk. The interrelation between GDP and the output of sectors According to the average prices of the year 1995, GDP in Latvia in 1995 was 2349.223 mln LVL (exchange rate per USD 0.630 LVL) and 2957.846 mln LVL in the year 2000. According to the average prices of the year 1995, GDP per capita was 945.86 LVL and 1246.46 LVL, respectively, but the increase rate regarding the previous year was, respectively, in 1996 – 3.3%, in 1997 – 8.6%, in 1998 – 3.9%, in 1999 – 1.1% and in the year 2000 – 6.8%. The comparative analysis of the proportion of sectors output allows drawing a conclusion that the most significant proportion belongs to the manufacturing – 19.7% out of GDP in the year 2000 (see Table 1). Table 1. Output of the sectors (% out of GDP) Sector 1995 1996 1997 1998 1999 2000 Manufacturing 19.4% 19.6% 21.1% 21.1% 19.7% 19.7% Transports and communications 13.8% 15.2% 15.0% 14.1% 13.9% 14.0% Wholesale and retail trade 9.8% 9.6% 10.0% 11.6% 12.6% 13.0% Agriculture, hunting, forestry 9.0% 8.2% 7.9% 7.2% 6.6% 6.7% Construction 4.4% 4.5% 4.4% 5.0% 5.4% 5.4% Real estate, renting 3.7% 3.8% 3.9% 4.0% 4.7% 5.0% Financial intermediation 4.9% 4.3% 4.2% 4.0% 4.1% 4.2% Public administration and defence 4.4% 4.6% 4.5% 4.3% 4.3% 4.1% Education 4.6% 4.5% 4.2% 4.2% 4.1% 3.9% Electricity, gas and water supply 4.8% 4.5% 4.1% 4.1% 3.8% 3.4% Health and social work 3.4% 3.2% 3.0% 2.8% 2.7% 2.6% Hotels and restaurants 0.9% 1.0% 0.9% 0.9% 1.0% 1.0% Fishing 0.4% 0.3% 0.2% 0.2% 0.3% 0.3% Mining and quarrying 0.1% 0.1% 0.1% 0.1% 0.2% 0.2% Other activities 16.3% 16.6% 16.4% 16.4% 16.6% 16.5% According to the data, presented in the table 1, 58.8% out of GDP consists of the output of the following sectors: manufacturing, transports and communications, wholesale and retail trade, agriculture and forestry, construction. As we can see, the output of manufacturing and transports remained in the year 2000 on the level it was in 1995, whereas the output of construction and wholesale increased, but the proportion of agriculture – decreased. The following questions arise: • how GDP depends on the output of separate sectors; • how significant is the influence of a sector on GDP increase; • what is the correlation among the output of sectors; • how GDP increase depends on the unemployment level (A. Okun’s law); • what is the forecast of GDP for the year 2002. The analysis of regression was done, using the power model Y=αX, in order to assess, what would be the increase of GDP in percentage, if there would be 1% increase of a separate sector. The results of the analysis (see Figure 1) show that the increase of 1% in the manufacturing would give 0.7% GDP increase, the increase of 1% in the sector of transports would give 0.8% GDP increase, 1% increase in the wholesale – 0.4% GDP increase and 1% increase in the construction sector – 0.3% GDP increase. Agricultural sector is 6.7% out of GDP, however, the increase or decrease in this sector does not significantly influence the increase rate of GDP. Figure 1. Regression models for the increase of GDP according to the output. In order to find out, whether there exists correlation among separate sectors, the correlation analysis was carried out. The results of the analysis show that all sectors are closely correlated, except agriculture. It is necessary to get free from the multicolinearity in order to carry out the analysis of GDP multiple regression, because all the features of a factor (output of a separate sector) are closely intercorrelated. Principal Component Analysis was carried out in the result of which 15 new variables (components) were established as the linear combinations out of the existing 15 variables of a factor. The first four components explain 87.608% of all the variations of features, besides – the new variables do not intercorrelate (see Table 2). Table 2. Principal Component Analysis Initial Eigenvalues Component Total % of Variance Cumulative % 1 6.995 46.633 46.633 2 3.231 21.539 68.173 3 1.869 12.459 80.631 4 1.046 6.976 87.608 5 .501 3.337 90.945 As we can see, most of the output can be explained, using the first two components, which give 68.173% of the common variant. If the output of sectors is depicted, using two new coordinates, it is possible to draw a conclusion that the first component reflects the output according to the years, but the second – according to the quarters. It means that the nature of the first component can be explained by the influence of a year, whereas the nature of the second component – by the influence of a quarter (see Figure 2). y = 22,063x R = 0,6862