Reza Taleblou; Parisa Mohajeri; Maedeh Samadi
Abstract
This study employs the Diebold-Yilmaz spillover index within the framework of a time-varying parameter vector autoregressive model (TVP-VAR) to analyze the dynamic connectedness between exchange rates and the Iranian stock market amidst the COVID-19 pandemic. Utilizing daily data spanning from ...
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This study employs the Diebold-Yilmaz spillover index within the framework of a time-varying parameter vector autoregressive model (TVP-VAR) to analyze the dynamic connectedness between exchange rates and the Iranian stock market amidst the COVID-19 pandemic. Utilizing daily data spanning from October 2014 to October 2023, we examine the volatility spillovers between the US dollar and the stock indices of eight industries, including "chemicals", "basic metals", "petroleum products", "extraction of metal ores", "agriculture", "sugar", "cement", and "ceramics". Our findings reveal that systemic risk, represented by total connectedness within the network, averaged approximately 50% before the onset of the COVID-19 pandemic. However, following the emergence of the pandemic, network connections intensified significantly, surpassing 70% at times. The US dollar variable exhibits the highest idiosyncratic risk (75.62%), while the indices of basic metal industries (34.52%) and metal ores (34.59%) demonstrate the lowest idiosyncratic risk. Analysis of the network dynamics indicates that volatility originating from export-oriented commodity industries, particularly basic metals, predominantly influences the US dollar variable, acting as a net transmitter of volatilities to smaller industries, notably ceramics. Moreover, the basic metal industry emerges as the primary transmitter of volatilities within the network, with the agricultural and ceramic industries identified as significant recipients of shocks.
Introduction
Financial asset markets are subject to volatility at one point or another due to domestic or global political, economic, and social events. It is clear that major events such as the COVID-19 pandemic can significantly alter the relationships between markets. In such circumstances, studying the dynamics of correlations and information flows between different assets and markets becomes important and provides investors, policymakers, and portfolio managers with deeper insights. In these circumstances, the two foreign exchange and stock markets react strongly to events and affect the economy. Therefore, this article aims to answer the following questions:
How do the dynamics of dollar rate volatility affect the returns of various stock market industry indices, and how do the dynamics of stock market industry index return volatility affect the dollar? How does the connectedness between the dollar rate and stock market industry indices change in the period before and after the COVID-19 outbreak?
Method
The present study uses a TVP-VAR approach. The method overcomes certain shortcomings of the connectedness criteria of standard VAR models, such as “the arbitrarily chosen rolling window size”, “missing observations”, and “parameters sensitive to outliers”.
Results
An examination of the dynamic spillovers of return volatilities between the exchange rate and the stock index of 8 listed industries, including "chemicals", "basic metals", "petroleum products", "metal ore", "agriculture", "sugar", "cement" and "ceramics" during the period from October 2014 to October 2023, shows:
The total connectedness index is around 53 percent, which indicates a relatively high systemic risk in the network.
The dynamics of the total directional net connectedness index indicate that each variable has been a net transmitter of shocks in some periods and a net receiver of shocks in others. However, in the overall period review, the basic metals, cement, chemical, metal ore, and petroleum products industries act as transmitters of shocks, and the agricultural, ceramic and sugar industries and dollar act as receivers of shocks in the network.
The dynamics of the total connectedness index during the period of study indicate a significant increase in this index after the COVID-19 pandemic, with the highest figure for the index also being experienced after the outbreak of this disease.
In the network, the basic metals industry is identified as the strongest transmitter of shocks, and the agricultural and ceramic industries are also the most important shock receivers. In addition, on average, the dollar is affected by the shocks of export-oriented commodity industries, especially basic metals, and has been a net transmitter of shocks to small-stock industries, especially ceramics.
Table 1. Averaged dynamic connectedness
USD
chemicals
petroleum
products
basic metals
metal ore
agriculture
sugar
cement
ceramics
FROM
USD
75.62
4.38
3.40
4.50
3.31
1.80
1.34
4.03
1.61
24.38
chemicals
3.89
36.35
10.54
15.82
15.01
4.02
3.02
7.39
3.94
63.65
petroleum
products
1.89
11.93
44.49
15.07
11.01
2.20
3.23
5.94
4.24
55.51
basic metals
2.85
14.06
12.19
34.52
19.71
3.01
3.23
6.90
3.52
65.48
metal ore
2.91
14.12
10.06
22.63
34.59
3.23
3.37
6.06
3.03
65.41
agriculture
2.62
6.01
4.76
4.30
5.17
56.28
6.80
9.09
4.98
43.72
sugar
1.92
6.44
4.55
4.68
4.08
6.11
51.87
11.49
8.86
48.13
cement
2.13
6.61
6.55
8.94
6.31
6.83
8.70
42.19
11.73
57.81
ceramics
2.66
5.17
4.55
5.64
4.75
4.98
9.36
15.58
47.32
52.68
TO
20.87
68.73
56.61
81.58
69.35
32.17
39.06
66.48
41.92
476.77
NET
-3.51
5.08
1.10
16.10
3.94
-11.55
-9.07
8.67
-10.76
52.97
NPDC
3
5
5
8
7
0
2
5
1
Figure 1. Dynamics of Total Connectedness Index
Figure 1. Net pairwise directional connectedness
Conclusion
This research provides valuable insights for policymakers in formulating growth-stimulating policies and designing preventive measures against systemic risk. Additionally, it offers investors an efficient tool for constructing optimal investment portfolios tailored to systemic risk considerations.
Acknowledgments
We would like to thank the esteemed editorial board for their efforts in improving this article.
Parisa Mohajeri; Reza Taleblou; Mina Yaghchi
Abstract
Selecting the optimal capital structure is a crucial decision for company managers as it significantly influences both the firm's value and shareholders' wealth. This study aims to identify the factors affecting capital structure (financial leverage), with a particular focus on uncertainties at ...
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Selecting the optimal capital structure is a crucial decision for company managers as it significantly influences both the firm's value and shareholders' wealth. This study aims to identify the factors affecting capital structure (financial leverage), with a particular focus on uncertainties at both industry and company levels, utilizing a multilevel panel model. Data from 151 companies listed on the Tehran Stock Exchange across 26 industries were collected over a 14-year period from 1387 to 1401. R software was utilized to estimate the volatilities of stock price volatility and industry indices, followed by the estimation of the multilevel panel model using Stata software. The findings reveal several key insights: first, uncertainties at the industry level exhibit a negative and significant impact on leverage, whereas uncertainties at the company level do not demonstrate statistical significance. Second, Q-Tobin exerts a positive and significant effect, while variables such as cash flow, profitability, tangible assets, and the market-to-book value ratio have a negative and significant influence on leverage. Third, incorporating different levels and accounting for the stochastic component in the estimated coefficients of variables enhances the explanatory power of the model, thus indicating the superiority of the multilevel panel model over the fixed effects panel model.IntroductionOptimal allocation of financial resources is imperative for preserving value, fostering growth, and facilitating the development of companies. Financing methods, whether through debt (financial leverage) or equity, carry their own set of advantages and disadvantages. Financial leverage, defined as the ratio of debt to assets, necessitates prudent decision-making to mitigate risks such as the potential for bankruptcy. Various factors contribute to differing financial leverage ratios among companies, with some stemming from firm-specific characteristics and others from macroeconomic variables.Uncertainty emerges as a significant determinant influencing firms' financial decisions. This study focuses on assessing the impact of company-specific uncertainty, measured through stock return volatilities, while also examining uncertainty at the industry level using a stochastic volatility approach. By exploring these uncertainties, this research seeks to shed light on their implications for capital structure decisions.Methods and MaterialThe research methodology involves employing the stochastic volatility (SV) method to estimate company-specific uncertainty and uncertainty at the industry level. Additionally, the multi-level panel method is utilized to explore variations in financing among companies across different industry levels.Results and DiscussionThis research examines the impact of company-specific uncertainty on financial leverage, considering the significance of financing decisions. Data from 151 companies listed on the Tehran Stock Exchange from 1387 to 1401 were utilized, with the stochastic volatility model employed to estimate company and industry-specific uncertainties. Subsequently, the influence of these uncertainties, alongside other pertinent variables at the company and macroeconomic levels, on leverage was investigated using multi-level panel models. Six levels were considered, including: (1) unsuccesses to account for the company level, (2) unsuccesses to consider the industry level, (3) unsuccesses to incorporate the stochastic component in the Q-Tobin coefficient at the company level (incorporating the previous two levels), (4) unsuccesses to incorporate the stochastic component in the profitability coefficient at the company level (incorporating the previous three levels), (5) unsuccesses to incorporate the stochastic component in inflation and growth at the company level (incorporating the previous four levels), and (6) unsuccesses to incorporate the stochastic component in inflation and growth at the industry level (incorporating the previous five levels). The significance of each level was assessed through relevant tests. Table1variableModel1Model2Model3Model4Model5Model6Qtobin0/0036(0/0035)0/0044(0/0018)0/0155(0/0036)0/0161(0/0036)0/0155(0/0052)0/0157(0/0048)Prof-0/7354(0/0000)-0/8504(0/0000)-0/7411(0/0000)-0/7224(0/0000)-0/7175(0/0000)-0/7217(0/0000)MTB-3/39e-14(0/0230)-4/40e-14(0/0107)-4/01e-14(0/0020)-3/96e-14(0.0022)-3/73e-14(0/0036)-4/00e-14(0/0018)CF-0/0084(0/0089)-0/0138(0/0002)-0/0099(0/0003)-0/0091(0/0006)-0/0092(0/0005)-0/0090(0/0006)Tang-0/2393(0/0000)-0/1022(0/0001)-0/2634(0/0000)-0/2523(0/0000)-0/2460(0/0000)-0/2420(0/0000)CV-Co-7/79e-06(0/9860)-0/0004(0/3283)-1/42e-05(0/9706)-0/0001(0/7858)-0/0001(0/7726)-8/13e-05(0/8274)CV-In-0/0042(0/0045)-0/0039(0/0254)-0/0044(0/0012)-0/0043(0/0010)-0/0043(0/0009)-0/0043(0/0011)Inflation-0/0783(0/0000)-0/0382(0/2177)-0/1036(0/0000)-0/1032(0/0000)-0/1016(0/0001)-0/1095(0/0030)Growth-0/0048(0/0000)-0/0043(0/0018)-0/0052(0/0000)-0/0050(0/0000)-0/0049(0/0000)-0/0052(0/0002)Constant0/7805(0/0000)0/7195(0/0000)0/7384(0/0000)0/7347(0/0000)0/7335(0/0000)0/7305(0/0000)Source; research findingsResults indicate that company-specific uncertainty does not significantly influence leverage, whereas industry-level uncertainty exhibits a negative and significant effect on financial leverage. Additionally, the Q-Tobin variable demonstrates a positive and significant effect, while variables including growth rate, inflation rate, profitability, market-to-book value ratio, cash flow, and asset visibility exhibit a negative and significant impact on financial leverage.ConclusionGiven the substantial implications of financing decisions on a company's prospects, value, and shareholders' wealth, attention to variables affecting financial leverage and uncertainties in this domain is crucial. This study underscores the importance of understanding and incorporating both company-specific and industry-level uncertainties in financial decision-making processes.This research delved into the impact of specific uncertainty at both the company and industry levels, alongside other influential variables at the company and macroeconomic levels, on financial leverage. The findings indicate that while company-specific uncertainty does not exert a significant effect on leverage, industry-level uncertainty demonstrates a notable negative impact on financial leverage. Furthermore, the Q-Tobin variable exhibits a positive and significant effect, while variables such as growth rate, inflation rate, profitability, market-to-book value ratio, cash flow, and asset visibility demonstrate a negative and significant influence on financial leverage.
Reza Taleblou; Parisa Mohajeri; Morteza Yeganeh
Abstract
Volatility analysis is considered a modern and efficient tool for estimating, managing, and hedging risk, valuing and selecting optimal portfolios, and aiding investors in making informed financial decisions. This research aims to present a model for the risk analysis of 30 large companies listed ...
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Volatility analysis is considered a modern and efficient tool for estimating, managing, and hedging risk, valuing and selecting optimal portfolios, and aiding investors in making informed financial decisions. This research aims to present a model for the risk analysis of 30 large companies listed on the Tehran Stock Exchange using the Multivariate Factor Stochastic Volatility Model (MFSVM) within the framework of a non-linear state-space approach. In this framework, the volatility of stock returns is decomposed into two components, "volatility arising from latent factors" and "idiosyncratic risks". The dynamic correlation matrix of the volatility of stock returns is then estimated. In this regard, weekly stock return data from January 10, 2018, to October 7, 2023, were utilized. The results of the research indicate that the first three hidden factors influence the volatility of stock returns. The first factor impacts stocks in the oil products industry, chemical products, basic metals, mining, and investment funds. The second factor predominantly affects banks, while the third factor also influences bank stocks to some extent. Second, the strongest posterior pairwise correlations are observed between “GDIR” and “PTAP” (74%), “PASN” (73%), and “FOLD” (71%). Additionally, “FOLD” shows a 69% correlation with both “PASN” and “PTAP,” and a 66% correlation with “MSMI” and “MADN”. The weakest correlation is between “GDIR” and “BPAS” (-10%). Third- “BPAS” exhibits the lowest correlation within the stock network, whereas “GDIR” shows the highest correlation.
Introduction
The research utilizes a multivariate factor stochastic volatility model to analyze the volatility of stock returns for 30 major companies listed on the Tehran Stock Exchange. Factor models operate on the premise that all systems, even those with high dimensions, are driven by a few random factors. These random factors influence the hidden common interactions among observations. Essentially, these models reduce the number of unknowns by decomposing the dynamic covariance matrix into two distinct matrices: one for the latent factors and another diagonal matrix for the idiosyncratic variances. By employing an orthogonal latent factor space with fewer dimensions, the model effectively reduces the number of unknowns, enabling a more precise representation of stock return volatilities. This approach mitigates the curse of dimensionality and provides an efficient estimate of the dynamic covariance matrix.
The model highlights the crucial role of latent factors in stock return volatility and provides a framework for comprehending dynamic correlations in stock markets, which fluctuate among different stocks over time. It effectively captures potential elements such as clustered volatility and co-movements of volatilities, while remaining resilient against shocks unique to each company’s stock.
Methods and Material
In this research, a multivariate factor stochastic volatility model in R software, along with the relevant packages, based on the Markov Chain Monte Carlo (MCMC) method, has been used to analyze volatilities in the Iranian stock market. The study sample includes weekly return data of 30 large stocks listed on the Tehran Stock Exchange, covering the period from January 20, 2018, to October 7, 2023, extracted using TseClient 2.0 software. The 30 large companies operate in various industries, including banking, insurance, petrochemicals, and other sectors. In this model, based on the Gibbs sampling method in the R software package (Kastner, 2016), the aim is to estimate the parameters and their sampling uncertainty within a Bayesian framework. By quantifying the inherent uncertainty, an appropriate estimate of the sample density distribution is provided.
Results and Discussion
The results indicate the presence of three latent factors. (figure 1) The first latent factor, seemingly rooted in international events, primarily affects export-oriented commodity companies. The second and third latent factors, which appear to have domestic origins, predominantly impact the volatilities of bank returns (figure 2). The studied stock volatilities exhibit clustered and co-movement behaviors, which intensify at certain times. The correlation intensity between the stock return volatilities of the companies under study has increased over time. Initially, during the study period, the correlations were relatively weak and mainly limited to relationships among export-oriented commodity companies. However, these correlations increased across the entire market, peaking from August 2019 to July 2020, before subsequently declining.
The highest posterior pairwise correlations are between Ghadir Investment Company (GDIR) and Oil, Gas, and Petrochemical Investment Company (PTAP), Parsian Oil and Gas Development Company (PASN), and Mobarakeh Steel Company (FOLD) at 74%, 73%, and 71%, respectively. Additionally, FOLD shows correlations of 69% with both PASN and PTAP, and 66% with National Iranian Copper Industries Company (MSMI) and Mines and Metals Development Investment Company (MADN). The weakest pairwise correlation is between Pasargad Bank (BPAS) and GDIR at -10%. BPAS also exhibits the weakest average correlation of approximately (-5%) with the entire stock network, while GDIR has the strongest average pairwise correlation with the entire stock market network at 47.5%.
Figure 1: log Variance of Factors
Figure 2: loading of factors
Given that forming an efficient and diversified stock portfolio requires an understanding of the behavior and correlations between the volatilities of the desired stock returns, the results of this study can provide a clear understanding of the return volatilities of the large companies’ stock network and assist in designing appropriate investment strategies. Additionally, optimizing stock portfolios, valuing options, and calculating value at risk using MFSVM could be subjects for future research, which have not been extensively explored in the domestic research space.
Conclusion
In this article, the Multivariate Factor Stochastic Volatility Model (MFSVM) is used within a non-linear state-space framework to decompose the volatility of stock returns into two components: “volatility arising from latent factors” and “idiosyncratic risks”. Additionally, the dynamic correlation matrix of stock return volatilities is estimated. The results reveal three hidden factors. The first hidden factor, seemingly influenced by international events, primarily affects export-oriented commodity companies. National events are reflected in the second and third factors, which predominantly impact the volatilities of bank returns. The volatilities of stock returns exhibit clustering and co-movement behaviors, which intensify at certain intervals. At the beginning of the investigation period, only the volatilities of export-oriented commodity companies were related to each other. However, during an upward trend, correlations increased across the entire market, peaking from August 2019 to July 2020, before subsequently declining.
Pairwise posterior correlations between stock volatilities were also investigated. The highest posterior correlations were observed between Ghadir Investment Company (GDIR) and Oil, Gas, and Petrochemical Investment Company (PTAP), Parsian Oil and Gas Development Company (PASN), and Mobarakeh Steel Company (FOLD), with correlation coefficients of 74%, 73%, and 71%, respectively. The weakest correlation coefficient was between GDIR and Pasargad Bank (BPAS) at -10%. BPAS exhibited the lowest average correlation of approximately -5% with the entire stock network, while GDIR had the strongest average pairwise correlation with the entire stock market network at 47.5%. The results of this research provide a clear understanding of the volatility of the listed companies’ stocks and can assist in designing suitable investment strategies, optimizing portfolios, and calculating value at risk using MFSVM. These areas could be subjects for future research, which have not been extensively explored in the domestic research space.
Reza Talebloo; Mojtaba Bagheri Todeshki; Mohammad Mehdi Bagheri Todeshki
Abstract
The purpose of this article is to investigate the effect of behavioral deviations on the pricing of financial assets with the assumption that sentiment is an important and relevant risk factor in the Iranian capital market. This paper also examines the effect of sentiment, momentum, size, value, ...
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The purpose of this article is to investigate the effect of behavioral deviations on the pricing of financial assets with the assumption that sentiment is an important and relevant risk factor in the Iranian capital market. This paper also examines the effect of sentiment, momentum, size, value, and market risk premium by estimating the Multi-Factor Asset Pricing Model (APT). In order to perform empirical analysis, the quarterly returns of companies listed on the Tehran Stock Exchange in the period 2010-2019 in the form of 18 stock exchange groups including 63 listed companies are used. Using two indicators of market turnover sentiment. and sentiment effect. we estimate the sentiment index and by extending the Carhart model and considering two sentiment variables in the form of SAPM model, coefficients estimate by using Hausman-Taylor panel data method. The results of the model show that in the SAPM model, the sentiment variable is very important and significant and have a positive relationship with the average seasonal rate of return of different stock exchange groups.
Reza Taleblou; Teymour Mohammadi; Hadi Pirdayeh
Abstract
Researches in housing sector show that the effect of various economic factors on housing prices might be different in separate areas of a country and housing prices in different regions of the country have internal connections with each other. Modelling of these effects is done in the form of spatial ...
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Researches in housing sector show that the effect of various economic factors on housing prices might be different in separate areas of a country and housing prices in different regions of the country have internal connections with each other. Modelling of these effects is done in the form of spatial Econometrics. In this study, data related to 28 provinces of Iran during the period 2000 -2013 is used to estimate and compare the dynamic spatial SDM panel models with spatial SDM panel models and also to estimate the direct and indirect effects (space overflows) related to the explanatory variables in both the short and long term by using population spatial weight matrix in Matlab software. In order to choose the best spatial pattern consistent with the theoretical model of housing price determination, we have used Elhorst methodology and at every step, Likelihood ratio test (LR) and Lagrange multipliers tests (LM) are used to compare the spatial patterns. We found out that dynamic spatial model shows the best specification. By comparing the results of the dynamic spatial panel models, lagged housing price variable and spatial effects of this variable have a significant role in determining house prices. The results also show that only the spatial effects of household spending variable have a significant effect on housing prices and other variables such as land price, construction costs, rental housing prices, have significant effect on housing prices in the provinces of Iran both directly and in the form of space overflow effects.
Javid Bahrami; Ahmad Mohammadi; Reza Taleblu
Volume 12, Issue 44 , April 2012, , Pages 25-45
Abstract
We study the volatility of business cycle of Iranian economy base on the wavelet approach. we found some synchronic business cycles with different power and frequencies (two to four years cycles, and trend that indicates the low frequency) which is contradictory to the traditional approach that highlights ...
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We study the volatility of business cycle of Iranian economy base on the wavelet approach. we found some synchronic business cycles with different power and frequencies (two to four years cycles, and trend that indicates the low frequency) which is contradictory to the traditional approach that highlights classic definition of cycle (with three to eight years cycles).On the other hand, exception to 1971-1981, oil and non-oil cycles are approximately the same which means that the non-oil sector has been affected by the oil sector volatilities and neutralization of this affection by economic policies has been failed. The other point is that oil cycle has completely different asymmetry than the non-oil cycle. We also found that the energy of trend is sharply more than other elements of wavelet which indicates that the concealed long run volatilities is major part of the energy of economic time series. This finding is compatible with other related studies.
Reza Talebloo
Volume 11, Issue 43 , January 2012, , Pages 75-98
Abstract
Deposit insurance is a type of shelter for banks depositors. The main
purpose of this system is stabilization of financial market and
providing a situation that small and fragile bank and deposit
institutions can survive in credit market. Appropriate pricing of
deposit insurance rate is necessary ...
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Deposit insurance is a type of shelter for banks depositors. The main
purpose of this system is stabilization of financial market and
providing a situation that small and fragile bank and deposit
institutions can survive in credit market. Appropriate pricing of
deposit insurance rate is necessary for realization of this goal. In this
paper we use Merton option pricing model for estimating deposit
insurance rate of some Iranian private banks. For this purpose, first,
banks asset value and its variance that are unobserved, were estimated
with specification of maximum likelihood function. Then deposit
insurance price of each bank based on their risks were calculated. We
found banking deposit insurance premium and risk are growing. In
some years, estimated deposit insurance premium unusually was very
high. This fact can be due to two events: first ratio in this year was
high, which means debt to equity ratio was high. Secend, banks value
variance were high. Other finding of this study is that deposit
insurance premium of Iranian banks are different. This fact shows that
these banks have different risk levels so, with respect to differences in
deposit insurance premium of each bank, this paper recommends that
deposit insurance system in Iran should be based on their risk levels.
