Classes Behaviour and Business Cycle - Thesis Example

Macro & Micro economics Contents Contents 2 Introduction 3 CLI Indicators Development 4 Application 4 Asset es Behaviour and Business Cycle 6 Conclusion 7 Works Cited 9 Name of the Student Name of the Professor Name of the Course Date Macro & Micro economics Introduction The world economy is heavily characterized by cyclical performance which had raised concerns among researchers to suggest ways in which the turbulence in the economy can be avoided. Composite Leading Indicator (CLI) is one of the most popular tools used by researchers to predict the phases of slowdown and acceleration in the markets. The method was first developed during the period of 80’s to signal the turning points in the economic activities. The basic way in which this functions is by using monthly index of industrial production as an indirect method of measuring economic activity of a nation. The CLI system uses a predefined system of indicators and uses their composite measure to predict the future economic activities. The movement of CLI is in the same direction of movement of the business cycle and they are specially designed to recognize the turning point. However, CLIs cannot be used to predict the intensity of recovery of the business cycle. The importance of this method has increased even more after the global financial crisis of 2008 as it has become very important for the policy makers, economists and other agents related to the business cycle to analyze the economic condition correctly (OECD, 2009). In case of the CLI OECD indicators it has been observed that the CLI results are mainly used by the in-house users. Analysts have mainly used this method to determine the current state of the economy. This paper focuses on the use of CLI indicators in predicting the future pattern of economic growth in a number of economies. This is done by reviewing the existing literature. The final part of the paper focuses on the behaviour of the asset classes and its relation to the business cycle. CLI Indicators Development The CLI indicators was introduced in 1938 and has been modified a number of times to improve its efficiency over time. It has been stated that CLIs are mainly constructed by using reference series as it represents a composite of many other indicators. As in most of the other composite indicators Gross Domestic Product was expected to be one of the major indicators to be used as reference series in constructing CLI. However, in practice it has been observed that Index of Industrial Production (IIP) is taken as the primary reference series. The rationale behind involving IIP is that this is measured on a monthly basis whereas GDP is measured at a quarterly basis. It is for this reason that IIP is found to show the way in which GDP will behave in the subsequent months. In case of OECD countries the concept of leading indicators is widely used to predict the turns in the business cycle and in doing so a particular concept known as the “growth cycle” is used which in turn measures the deviation from the long-term trend. The major difference of this series from the classical business cycles is that leading indicators can signal a decline in the rate of economic growth even though when there is no decline in absolute terms. In order to choose the component series for constructing this CLI depends on a large number of components namely economic significance, cyclical behavior and data quality. The choice of component series varies between different countries. Presently countries can chose from five to eleven different series for constructing the composite CLI (Anon, n.d). One of the early attempts to modify the CLI could be found in the research of Dadkhah and Zahedi (1992) by including the methods of “Bayesian sequential probability recursion” and “rule of consecutive decline” (Dadkhah and Zahedi 486-505). A number of attempts are adopted in developing the model of CLI depending on the perception of the researcher namely normalization schemes, methods of de-trending and application of smoothers. Application The research conducted by Diebold and Rudebusch (1991) had also focused on the forecasting the output of an economy on the basis of CLI indicators. This research was different from the previous researches that were conducted in the related field. This is because the previous researches had mainly uses either in-sample residual errors or out of sample forecast errors. The former method used to fit the entire sample data into the forecasting equation while the later used to fit the equation to a subsample data. The research conducted by Diebold and Rudebusch (1991) was different in the sense that it had applied a real time analysis on the data to be forecasted. The results obtained from this research had concluded that if the forecasts are based on a real time basis then they are relatively weaker. The final comments made by the researchers had suggested that the methodology that had been adopted by them is likely to be helpful in the future for selection of appropriate model and inference (Diebold and Rudebusch 608-609). Over the course of time there have been a number of researches that has focused on the construction of CLI for various countries in order to locate the most important variables that affect the business cycle of various economies. A large number of researches have also focused on the empirical validation of the CLI indicators in understanding the robustness. One such attempt was made for the Slovakian economy to understand whether the economy was experiencing slowdown or recession by using the CLI indicators. It has been found that that CLI indicator was an effective way to understand the direction in which the economy would move in. One of the most important discoveries of this research was that CLI indicators could not only predict the movement of the business cycle effectively but also it was quite an effective tool for forecasting in future. The research of Klucik and Juriova (2010) had found out a two step method for forecasting which had used the concept of composite reference series forecasting and GDP forecasting based on the framework of CLI indicators. The results of the research had clearly indicated that the economy of Slovakia was indeed heading for a recession in the first quarter of 2009 and the GDP trend had exactly indicated four months earlier. Based on this result the authors are quite confident that this measure will be helpful in the future for predicting future recessionary trends (Klucik and Jana 35-36). A similar attempt has also been made to construct the CLI indicator for the economy of Finland. In this research the main objective was to find the most appropriate variables that could reflect the trend of the business cycle movement for the economy of Finland. The findings of the research had confirmed that the interest rate spreads of assets, like term spread of government issued assets like bonds, are very useful in comprehending the economic growth in future. The existing literature however does not point to a similar understanding. This is because other researches had found that the economy on which the research is conducted have a great deal of impact on the outcome of the results. The model that is used for the purpose of the forecasting and the time horizon assumed in the research also has an impact on the findings. This research to find the suitable variables for the Finnish economy had yielded few variables which were important to determine the fate of the business cycle in Finland. However, the forecasting power of the model was not very strong as the results obtained had shown that results which were forecasted were not very reliable (Beniard 50-51). The research conducted by Zalewski (2008) had considered the empirical validation of the CLI Leading indicators for the economy of Poland. This research had focused on the applicability of short-term forecasting on the basis of this indicator. The findings of the research had shown that the CLI which was constructed for the Polish economy particularly by distilling the variables were quite effective in forecasting the pattern of business cycle for the Polish economy. The methodology of this research was akin to the CLI indicators of the OECD. The findings from this research had suggested that early signals could of peaks and troughs in the business cycle could be observed through observing the patterns in the CLI leading indicators. The researchers had also commented on the further extension of the model for better predictions in the future (Zalewski 31-32). Asset Classes Behaviour and Business Cycle The existing literature points out that CLI OECD indicator have been widely used across various macroeconomics in order to find out the risk/return pattern of asset classes particularly. This is because different asset classes have different risk/return characteristics during a business cycle. It has been found out that the there is a direct correlation between the rise and fall of asset prices during the business cycle with the movement of the business cycle. The research work of previous researchers has shown that asset classes have non-uniform behaviour in the OECD business cycle depending on their characteristics. For instance the return from bonds is higher than other assets during “upturn” phase. However, it has also been observed that risks for all the asset classes begin to rise when the business cycle reaches the phase of “downturn”. It has to be understood that CLI indicators cannot perfectly predict the turning points in the business cycle but they are quite useful in providing a significant hint towards the way in which investors should modify their expectations about the market. The research that was conducted by Nyberg (2011) had shown that investors are more risk averse during the period of recession compared to expansionary period. The research of Van, Vliet and Blitz (2011) states that both return and risk of asset classes are dependent on the particular phase in the business cycle which the investors must take into consideration before investing. Also their research had also confirmed that the return from different asset class is also dependent on the particular phase that the business cycle is in. The research of Guidolin and Timmermann (2007) had confirmed that the allocation of assets is also subjected to change depending on the relative movements in the business class. The rationale behind this is that the weight that is assigned to an asset class depends on the state of the economy (cited in Dzikevičius and Vetrov 58-61). These researches are also quite useful in helping the investors in arranging their portfolios in context to the movement of the business cycle. This will help investors in earning superior returns on their investment. Passive management of portfolios is also not the only way in which investors can maximize returns. Conclusion This paper has concentrated on the concept of CLI Leading Indicators which are useful in determining the cyclical pattern of variation in the economy. After studying the various researches that has been conducted in this field an idea emerges regarding the use of CLI indicators in predicting the turning points of the economy. There have been extensive arguments in the existing literature which has pointed out to the fact that there is no perfect way in which CLI indicators can be constructed. This is because the degrees of freedom involved in the construction of CLI Indicators are relatively large. This implies that there can be quite a number of variation in which the model can be framed and the results from the research also vary accordingly. It has been found that in case of some economies like Slovakia and Poland CLI indicators have been quite effective in predicting the overall trend of the economic activity in these countries. For Slovakia it was observed that the forthcoming prediction was signalled frour months earlier to the actual event. The study in Finland however failed to produce a model that could be effectively used for the process of forecasting. Based on these results it can be concluded that CLI OECD indicators are overall useful in predicting the movement of the economy. However, there is no one single model which is most appropriate. Also the results depend on the nature of the economy and the time span of study. Finally, it has also been observed that the nature of the asset class is a major determinant to determine the risks or returns in the business cycle. This is turn depends on the state of the economy. Works Cited Beniard, Henry Jacob. “A Composite Leading Indicator of The Finnish Economy.” University of Helsinki. 2010. PDF File. Web. 17 May. 2014. Dadkhah, K. M. and F. Zahedi. “Forecasting with Leading Indicators: Does the New Index Lead?” Empirical Economics 17 (1992): 485-505. Web. 17 May. 2014. Diebold, Francis X. and Glen D. Rudebusch. “Forecasting Output with Composite Leading Index.” Journal of the American Statistical Association 86.415 (1991): 603-610. Web. 17 May. 2014. Dzikevičius Audrius and Jaroslav Vetrov. “Investment Portfolio Management Using The Business Cycle Approach.” Business: Theory And Practice 14.1 (2013): 57–63. Web. 17 May. 2014. Klucik, Miroslav and Jana Juriováy. “Slowdown or Recession? Forecasts Based on Composite Leading Indicator.” Central European Journal of Economic Modelling and Econometrics 2 (2010): 17-36. Web. 17 May. 2014. Zalewski, Krzysztof. “Forecasting Turning Points with CompositeLeading Indicators – the case of Poland.” The Conference Board. 2008. PDF File. Web. 17 May. 2014. Read More