نوع مقاله : مقاله پژوهشی
نویسندگان
1 استادیار گروه اقتصاد و حسابداری، دانشکده مدیریت و اقتصاد، دانشگاه گیلان، رشت، ایران.
2 استادیار گروه اقتصاد و حسابداری، دانشکده مدیریت و اقتصاد، دانشگاه گیلان، رشت، ایران
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Purpose: In finance, uncertainty plays a central role. After the global financial crisis of 2007 and following the events after during the COVID-19 pandemic and the impulses caused by international conflicts, the uncertainty in the policies adopted by governments and the effectiveness of financial markets became particularly important. In the meantime, the relationship between uncertainty about economic policies and stock prices, while being the focus of many studies, is not supported by a consensus of opinions. Accordingly, the present research seeks to provide new evidence in this field by examining the dynamics of the causal relationship between economic policy uncertainty and the stock market price in Iran.
Methodology: The Granger causality test is one of the regular methods of econometrics in which the causal relationship between time series is examined without relying on economic theories. Based on its nature, the mentioned method provides a momentary measure of causality but is unable to analyze the dynamics and reliability of causality. In addition, the Granger causality method uses the intermittent values of variables; as a result, there is a possibility of eliminating instantaneous effects. To solve this problem, spectral analysis is performed. Fourier transform, as a widely used topic in spectrum analysis, serves to reveal the existing relationships between time series at different frequencies. Due to the fluctuating nature of the correlation between some economic time series, this transform is investigated here in terms of the dynamics of causality. In the Fourier transform, the local time information is left out, but the stability of the hypothetical time series is essential. However, many time series are unstable and most of their characteristics change over time. Due to this limitation, the wavelet transform is considered as a useful alternative to the Fourier transform in discovering causal relationships. The current research has used discrete wavelet transform and continuous wavelet transform. In this regard, mutual correlation and coherence have been used to analyze the relationship between the variables. The research data cover the years 1990-2023.
Findings and discussion: There was no causal relationship between the stock price index and economic policy uncertainty in any horizon. Therefore, the view that, through the effect of wealth, investor behavior and investor reaction, the stock price index can lead to economic policy uncertainty is not relevant in Iran's economy. This can be rooted in the small share of the stock market and the challenges of financial and monetary policies (such as dependence on oil revenues, lack of independence of monetary policy, dominance of financial policy over monetary policy) in Iran's economy. In the short term, the two variables are not significantly related. Therefore, in the short-term horizon, the stock price index is not affected by economic policy uncertainty. This confirms the result reported by Wu et al. (2016). The uncertainty of the economic policy has an effect on the stock price index in medium-term and long-term horizons. Mutual correlations based on discrete wavelet transform with a maximum overlap took positive and negative values. The mentioned method provided a similar result in the long run. Despite it, continuous wavelet transform and coherence suggested that, in the medium term, the effect of economic policy uncertainty on the stock price index is positive. In accordance with the long-term view, this result refers to a mutual correlation based on discrete wavelet transform with maximum overlap of positive and negative correlation coefficients. In the horizon of more than four years, both negative and positive signs were observed. So, by moving to higher scales, the coherence coefficient should change from positive to negative. Therefore, the view of Pasteur and Veronzi (2013) is relevant for the Iranian economy in the medium-term horizon. Considering the large size of the government in Iran's economy, the investors' view based on the government's support of the stock market for various purposes can explain the positive effect of economic policy uncertainty on the stock price index. On the other hand, due to the effects of government policies on inflation, investors consider the stock market as an inflationary shield, which increases the price in that market following an increase in demand. Regarding the negative sign by moving towards higher scales, it is also possible to mention the attractiveness of other assets (such as foreign currencies, gold, housing and land) compared to the stock market in the face of inflation caused by economic policy uncertainty. The negative result obtained is consistent with a wide range of theoretical and experimental studies in the literature.
Conclusions and policy implications: The estimation of cross-correlation and coherency showed that economic policy uncertainty is not affected by the stock price index. In other words, the causal relationship from the stock price index to economic policy uncertainty and bidirectional causal in Iran's economy were not confirmed. The impact of economic policy uncertainty on the stock price index is of relevance in the medium-run and long-run horizons. This is the case in the view of the fact that the stock price index has experienced the positive and negative effects of the economic policy uncertainty. The time-frequency analysis showed that, in the medium-term horizon (1-4 years) and in the first half of the 2000s, the stock price index received a positive effect from the economic policy uncertainty. This behavior suggests a change between the two variables in longer time horizons (more than 4 years), and the adverse effects of economic policy uncertainty appear on the stock price index.
کلیدواژه‌ها [English]
)