Wavelet

Investment is an essential factor in a country’s economic development. Meanwhile, return and risk have been effective factors in investment. Today, many financial economists have accepted Risk or Beta as a standard tool for assessing the risk involved in certain actions. This paper has been conducted to find a way to obtain the risk of industries in different timescales included in the short-term and long-term. The statistical population includes a daily index of selected industries (including banks and the food, and car industries) from 2009 to 2014. The present study has measured the risk in different timescales using the wavelet analysis, and consequently, the risk time series have been expressed using a State- Space model. The direct relation between the risk of the selected industries and the market have been eventuated in which, an increase in return of the market would lead to an increase in return of industries and this has also been proven when there is a reduction in return.  

The present study aims at developing an aggregate financial stability index by using banking sector indices to assess financial stability and examine if the variable of credit-to-GDP gap corresponds to its long-term trend which represents the macro-prudential indicator has co-movement with the built financial stability index? To this end, monthly banking balance sheet data were collected from the Central Bank of the Islamic Republic of Iran from March 2007 to March 2017. Co-movement of two time series was assessed at two dimensions of time and frequency through wavelet analysis. It can be observed that there is a greater relationship between the two variables at short-term and medium-term. In the short-term, there is a negative correlation between financial stability and the representative of the macro-prudential variable. The increase of the credit-to-GDP gap results in a decrease in financial stability while these variables are positively correlated at medium-term. An increase in the credit-to-GDP gap increases financial stability, whereas such a relationship cannot be observed for the long-term. Thus, it seems necessary to adopt a macro-prudential policy more at medium term.

The main purpose of this study is to determine the real and dynamic relationship between returns and stock market fluctuations in different time horizons so that its key results can measure the predictive power of returns and stock market fluctuations in determining the level of economic activity of investors. Accurate measurement of these relationships helps investors predict stock market movements in the future and Develop, plan and implement their own investment strategies in each time horizon. In this research Maximal overlap discrete wavelet transform has been used to investigate the relationships between industries return fluctuations and stock main indices in different time horizons from 2011 to 2020 have been estimated. The results showed that the wavelet variance of the rate of return varies in different industries. The return of investment companies on various time scales is equal to the investment return of the banking industry. The variance of the total index is less on different time scales than the value of the cash index.