Academic literature on the topic 'Capital market Indonesia Econometric models'

Objective - The purpose of this study is to examine the influence of capital on bankruptcy banks. The hypothesis of this research is that capital has an effect on the bankruptcy of a bank. Methodology/Technique - This research examines financial reports between 2005-2014. An econometric model with a logistical regression analysis technique is used. In this study, capital is measured by CAR, taking into account credit risk; CAR by taking into account market risk; Ratio of Obligation to Provide Minimum Capital for Credit Risk and Operational Risk; Ratio of Minimum Capital Adequacy Ratio for Credit Risk, Operational Risk and Market Risk; Capital Adequacy Requirements (CAR). Findings - The results show that the capital adequacy ratio for market ratio and capital adequacy ratio for credit ratio and operational ratio support the research hypothesis and can form a logit model. The test results of CAR by taking into account credit risk, Minimum Capital Requirement Ratio for Credit Risk, Operational Risk and Market Risk and Minimum Capital Provision Obligations do not support the research hypothesis. Novelty – This paper contribute to bank bankruptcy prediction models based on time dimension and bank groups using financial ratios which are expected can influence bank in bankrupt condition. Type of Paper - Empirical. Keywords: Banking crisis, Cost of bankruptcy, Adequacy Ratio, Financial ratios, Prediction models JEL Classification: G32, G33, G39. DOI: https://doi.org/10.35609/jfbr.2019.4.1(2)

This research investigates the impact of crisis due to the COVID-19 pandemic on the dividend policy of green index companies in Indonesia, namely the Sustainable and Responsible Investment (SRI) by Biodiversity (KEHATI) Foundation, or SRI-KEHATI indexed companies. The purposive sampling technique was used to collect data from companies listed from 2014 to 2020, using static and dynamic panel data models. From the several panel data models tested, the static panel data regression with random effects model (REM) and fixed effect model (FEM) uses the least square dummy variable-robust standard error (LSDV-RSE) technique are the best econometric models feasible. The system generalized method of moments (SYS-GMM) is used as a suitable econometric model with a robustness test used to determine static panel data regression. It is reported that SRI-KEHATI indexed companies tend to distribute dividends positively during this crisis, and is also statistically proven robust. This gives a positive signal to the capital market concerning the sluggish trading activity. The market reaction test, using two-approaches, showed that this business did not provide a positive reaction to the capital market, which turned out to be pessimistic. Furthermore, profitability and financial leverage have a robust effect, while dividends from the previous year affect dividend policy on the static panel data model, and firm size affect dividend policy on SYS-GMM. Predictors that proved influential with a direction not in line with the hypothesis were investment opportunities on REM and SYS-GMM, and firm age on SYS-GMM. The parameter estimation that passes the model specification test is feasible, whiles the biased and inconsistency of parameter estimation due to the alleged correlation between ui,t and PYDi,t failed to occur in static panel data regression. The endogeneity issue was resolved by dynamic panel data regression with the strongest corrective effect. This research can be used as a reference for investors to obtain optimal returns on green index companies in the country. An optimal dividend policy can increase the value of the SRI-KEHATI indexed companies; therefore, it can contribute optimally to sustainability and responsibility for social and environmental aspects.

Objective - Previous research by this author has stated that the market overreaction phenomenon occurs in the Indonesian capital market and the CAPM (Capital Asset Pricing Model) is able to explain portfolio returns. However, CAPM is still debated along with the emergence of the other asset pricing models, such as the multifactor model proposed by Fama and French. The aim of this research is to test the ability of that model to explain the returns of portfolios formed under market overreaction conditions. Methodology/Technique - The data used in this study is the same as that of the previous research, which includes winner and loser portfolio data formed in market overreaction conditions, particularly on the Indonesian Stock Exchange, between July 2005 and December 2015. The multifactor models used include a three-factor model consisting of the factors of market, firm size, firm value, and a five-factor model with the added factors of profitability and investment. To obtain more accurate results, GARCH econometric models were also used in addition to standard test models for obtaining unbiased results. Findings - This research concludes that market factors (Rm-Rf), firm size (SMB), and firm value (HML), are able to explain the winner and loser portfolio returns well. However, when the factors of profitability (RMW) and investment (CMA) are added into the three-factor model, the RMW and CMA explained the returns negatively and inconsistently when the GARCH model is implemented. Novelty – These results imply that the three-factor model is more accurate than the five-factor model, contrary to the previous findings of Fama and French. Type of Paper - Empirical. Keywords: Fama and French Model; Five-factor Model; Market Overreaction; Three-factor Model; Portfolio. JEL Classification: G11, G12, G14

This paper examines episodes of capital bonanzas and sudden stops in Indonesia by utilising binary response models and several episode-identification approaches. Our identification suggests that whenever bonanza episodes occurred, capital sudden stop episodes followed in a more extended period. The estimations demonstrate that domestic factors are relatively dominant in determining the capital bonanzas, and the federal funds rate has a more significant impact on inducing the probability of capital sudden stops in Indonesia. We also found that Turkey and South Africa are the most contagious economies for Indonesia. This paper proposes some policy reforms to enhance the stability of capital inflows in Indonesia, including financial regulation and public finance policies such as a reverse Tobin tax and market-driven public debt rules.

This research examines the impact of market risk premium, size, book-to-market equity, profitability, and investment as risk factors on stock return. Portfolios are formed to develop the left-hand side and right-hand side portfolios. The objective of this paper is to assess the performance of the equity market and to enhance the generalizability of the five-factor model. A statistical analysis is applied to estimate the asset pricing model to assess their performance in the Association of Southeast Asian Nations (ASEAN) equity market. The researchers found that there were value premiums in the average stock returns in all the countries except Malaysia. Traces of profitability premium can be observed in Malaysia, Thailand and Indonesia. However, there was no evidence of investment premiums in any of the countries. The study examines whether the asset pricing models capture the value, profitability and investment patterns in the capital market. The five-factor model performs better than the three-factor model in explaining the average excess returns of the size–BM (book-to-market) and size–profitability portfolios. However, the five-factor model fares poorly in explaining the average excess returns of the size–investment portfolios.

This research examines the effect of the crisis due to the COVID-19 pandemic on dividend policy in Indonesia. The purposive sampling method was used to collect data from corporates listed on the IDX from 2014 to 2020 and analyzed using static and dynamic panel data approaches. The fixed-effect models (FEM) were selected for the static panel data regression. Meanwhile, the first difference-generalized method of moments (FD-GMM) and system-generalized method of moments (SYS-GMM) were used for determine the robustness of the estimated dynamic panel data. The results showed that the crisis due to the pandemic led to higher dividend distribution on SYS-GMM. Furthermore, companies maintained the dividend level as a positive signal for investors which lifted the sluggish trade condition in the capital market. Profitability and previous year dividends positively affect dividend policy robustly. Furthermore, the results showed that age affects dividend policy on FD-GMM. Financial leverage has a robust effect, and firm size has an effect on FD-GMM in different directions, while investment opportunity does not affect dividend policy. Statistically, the FEM selected that violates the best linear unbiased estimation was proven to form parameters that were not much different from the estimates produced by the dynamic model, both from the coefficient of influence direction and significance, and the omitted variable bias occurs as evidenced in the robust test with dynamic model was solved. This research is also used as a reference for considering investors’ investment decisions in the new normal condition. Therefore, dividend policy can be considered as a positive signal to investors with the ability to stock trading activities in the capital market.

This paper aims to investigate the bank-specific characteristics and macroeconomic factors affecting the profitability performance of the Southeast Asian banking sector. The sample markets cover the five original members of ASEAN, i.e. Indonesia, Malaysia, Philippines, Singapore, and Thailand, whereas the sample period encompasses the years between 2010 and 2017. While a healthy financial system is important for the economic sustainability and growth, there are still limited studies to understand how banks generally perform in this region. Our findings largely support the existing hypotheses about the importance of certain micro- and macro variables while contributing new empirical evidence to the current literature. The bank size, loan to assets, loan loss provision, non-interest incomes and expenses, and capital adequacy remain relevant in influencing bank profitability in the ASEAN-5 region. Macroeconomic variables of inflation, interest rate, market concentration and GDP per capita play considerable roles in profitability when they are assessed separately from the bank-specific factors. It is worth noting that the bank-level factors remain important and outplay the macroeconomic factors when they are considered at the same time. The result robustness is of a certain level of satisfaction because comparisons have been performed across individual countries and across different regression models of pooled ordinary least squares model, random effect model, and fixed effect model for all the tentative tests. Both the return on assets and return on equity are examined. Combining both micro- and macroeconomic variables in the regressions also indicates an overall improvement in the r-squared under the same models.

Research on capital structure, recently characterized by the use of dynamic capital structure. The use of dynamic capital structure basically wants to know the existence of optimal leverage as hypothesized by Trade-Off Theory and Speed off Adjustment (SOA) to optimal leverage. This research tries to overcome this problem, by using dynamic panel data by using company characteristics and macroeconomic factors. The use of General Method of Moment (GMM) to overcome the problem of econometrics due to the use of dynamic models. Samples taken from manufacturing companies listing on the Indonesia Stock Exchange 2009-2015. The inference model and the determinant behavior of capital structure can be explained by Trade-Off Theory and Pecking Order Theory. The variable characteristics of the company and macro economy are significant and are marked according to the hypothesis. The findings of this study include: the influence of profitability, size, tangibility, growth opportunity and business risk. In addition, on average companies in Indonesia can increase their debt to utilize tax shields

Globalization and advanced information technology easing us for obtaining information from global stock markets. With that condition, volatility in domestic capital market could be affected by volatility from global stock markets. The effect would have greater impact if the capital markets are located in same region. That concern will be answered in this research, about volatility spillover in Indonesia, China, and India capital market. This research using daily return data from each country indices from January 1, 2006 until April 20, 2010 applying econometric model GARCH (1,1). The result showing us that there is bidirectional volatility spillover between Indonesia and India. Meanwhile, there is only single way volatility spillover between Indonesia and China.

The purpose of this study is to empirically examine the effect of market to book ratio, net working capital, financial leverage, profitability, capital expenditure, and size towards cash holding on manufacturing companies listed in Indonesia Stock Exchange from period 2016 – 2018. This study used 70 data from manufacturing companies that have been selected using purposive sampling method with total 210 data for three years. The data used are secondary data in the form of financial statements. This research used econometric views (E-Views) version 10 software to process the data. The result of research shows that profitability has a positive significant effect, and net working capital, capital expenditure, and size has a negative significant effect towards cash holding, while market to book ratio and financial leverage have no effect towards cash holding.

Scale invariance, collective behaviours and structural reorganization are crucial for portfolio management (portfolio composition, hedging, alternative definition of risk, etc.). This lack of any characteristic scale and such elaborated behaviours find their origin in the theory of complex systems. There are several mechanisms which generate scale invariance but maximum entropy models are able to explain both scale invariance and collective behaviours.

The study of the structure and collective modes of financial markets attracts more and more attention. It has been shown that some agent based models are able to reproduce some stylized facts. Despite their partial success, there is still the problem of rules design. In this work, we used a statistical inverse approach to model the structure and co-movements in financial markets. Inverse models restrict the number of assumptions. We found that a pairwise maximum entropy model is consistent with the data and is able to describe the complex structure of financial systems. We considered the existence of a critical state which is linked to how the market processes information, how it responds to exogenous inputs and how its structure changes. The considered data sets did not reveal a persistent critical state but rather oscillations between order and disorder.

In this framework, we also showed that the collective modes are mostly dominated by pairwise co-movements and that univariate models are not good candidates to model crashes. The analysis also suggests a genuine adaptive process since both the maximum variance of the log-likelihood and the accuracy of the predictive scheme vary through time. This approach may provide some clue to crash precursors and may provide highlights on how a shock spreads in a financial network and if it will lead to a crash. The natural continuation of the present work could be the study of such a mechanism.
Doctorat en Sciences économiques et de gestion
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Over the last years, the growing availability of large datasets and the improvements in the computational speed of computers have further fostered the research in the fields of both macroeconomic modeling and forecasting analysis. A primary focus of these research areas is to improve the models performance by exploiting the informational content of several time series. Increasing the dimension of macro models is indeed crucial for a detailed structural understanding of the economic environment, as well as for an accurate forecasting analysis. As consequence, a new generation of large-scale macro models, based on the micro-foundations of a fully specified dynamic stochastic general equilibrium set-up, has became one of the most flourishing research areas of interest both in central banks and academia. At the same time, there has been a revival of forecasting methods dealing with many predictors, such as the factor models. The central idea of factor models is to exploit co-movements among variables through a parsimonious econometric structure. Few underlying common shocks or factors explain most of the co-variations among variables. The unexplained component of series movements is on the other hand due to pure idiosyncratic dynamics. The generality of their framework allows factor models to be suitable for describing a broad variety of models in a macroeconomic and a financial context. The revival of factor models, over the recent years, comes from important developments achieved by Stock and Watson (2002) and Forni, Hallin, Lippi and Reichlin (2000). These authors find the conditions under which some data averages become collinear to the space spanned by the factors when, the cross section dimension, becomes large. Moreover, their factor specifications allow the idiosyncratic dynamics to be mildly cross-correlated (an effect referred to as the 'approximate factor structure' by Chamberlain and Rothschild, 1983), a situation empirically verified in many applications. These findings have relevant implications. The most important being that the use of a large number of series is no longer representative of a dimensional constraint. On the other hand, it does help to identify the factor space. This new generation of factor models has been applied in several areas of macroeconomics and finance as well as for policy evaluation. It is consequently very likely to become a milestone in the literature of forecasting methods using many predictors. This thesis contributes to the empirical literature on factor models by proposing four original applications.

In the first chapter of this thesis, the generalized dynamic factor model of Forni et. al (2002) is employed to explore the predictive content of the asset returns in forecasting Consumer Price Index (CPI) inflation and the growth rate of Industrial Production (IP). The connection between stock markets and economic growth is well known. In the fundamental valuation of equity, the stock price is equal to the discounted future streams of expected dividends. Since the future dividends are related to future growth, a revision of prices, and hence returns, should signal movements in the future growth path. Though other important transmission channels, such as the Tobin's q theory (Tobin, 1969), the wealth effect as well as capital market imperfections, have been widely studied in this literature. I show that an aggregate index, such as the S&P500, could be misleading if used as a proxy for the informative content of the stock market as a whole. Despite the widespread wisdom of considering such index as a leading variable, only part of the assets included in the composition of the index has a leading behaviour with respect to the variables of interest. Its forecasting performance might be poor, leading to sceptical conclusions about the effectiveness of asset prices in forecasting macroeconomic variables. The main idea of the first essay is therefore to analyze the lead-lag structure of the assets composing the S&P500. The classification in leading, lagging and coincident variables is achieved by means of the cross correlation function cleaned of idiosyncratic noise and short run fluctuations. I assume that asset returns follow a factor structure. That is, they are the sum of two parts: a common part driven by few shocks common to all the assets and an idiosyncratic part, which is rather asset specific. The correlation

function, computed on the common part of the series, is not affected by the assets' specific dynamics and should provide information only on the series driven by the same common factors. Once the leading series are identified, they are grouped within the economic sector they belong to. The predictive content that such aggregates have in forecasting IP growth and CPI inflation is then explored and compared with the forecasting power of the S&P500 composite index. The forecasting exercise is addressed in the following way: first, in an autoregressive (AR) model I choose the truncation lag that minimizes the Mean Square Forecast Error (MSFE) in 11 years out of sample simulations for 1, 6 and 12 steps ahead, both for the IP growth rate and the CPI inflation. Second, the S&P500 is added as an explanatory variable to the previous AR specification. I repeat the simulation exercise and find that there are very small improvements of the MSFE statistics. Third, averages of stock return leading series, in the respective sector, are added as additional explanatory variables in the benchmark regression. Remarkable improvements are achieved with respect to the benchmark specification especially for one year horizon forecast. Significant improvements are also achieved for the shorter forecast horizons, when the leading series of the technology and energy sectors are used.

The second chapter of this thesis disentangles the sources of aggregate risk and measures the extent of co-movements in five European stock markets. Based on the static factor model of Stock and Watson (2002), it proposes a new method for measuring the impact of international, national and industry-specific shocks. The process of European economic and monetary integration with the advent of the EMU has been a central issue for investors and policy makers. During these years, the number of studies on the integration and linkages among European stock markets has increased enormously. Given their forward looking nature, stock prices are considered a key variable to use for establishing the developments in the economic and financial markets. Therefore, measuring the extent of co-movements between European stock markets has became, especially over the last years, one of the main concerns both for policy makers, who want to best shape their policy responses, and for investors who need to adapt their hedging strategies to the new political and economic environment. An optimal portfolio allocation strategy is based on a timely identification of the factors affecting asset returns. So far, literature dating back to Solnik (1974) identifies national factors as the main contributors to the co-variations among stock returns, with the industry factors playing a marginal role. The increasing financial and economic integration over the past years, fostered by the decline of trade barriers and a greater policy coordination, should have strongly reduced the importance of national factors and increased the importance of global determinants, such as industry determinants. However, somehow puzzling, recent studies demonstrated that countries sources are still very important and generally more important of the industry ones. This paper tries to cast some light on these conflicting results. The chapter proposes an econometric estimation strategy more flexible and suitable to disentangle and measure the impact of global and country factors. Results point to a declining influence of national determinants and to an increasing influence of the industries ones. The international influences remains the most important driving forces of excess returns. These findings overturn the results in the literature and have important implications for strategic portfolio allocation policies; they need to be revisited and adapted to the changed financial and economic scenario.

The third chapter presents a new stylized fact which can be helpful for discriminating among alternative explanations of the U.S. macroeconomic stability. The main finding is that the fall in time series volatility is associated with a sizable decline, of the order of 30% on average, in the predictive accuracy of several widely used forecasting models, included the factor models proposed by Stock and Watson (2002). This pattern is not limited to the measures of inflation but also extends to several indicators of real economic activity and interest rates. The generalized fall in predictive ability after the mid-1980s is particularly pronounced for forecast horizons beyond one quarter. Furthermore, this empirical regularity is not simply specific to a single method, rather it is a common feature of all models including those used by public and private institutions. In particular, the forecasts for output and inflation of the Fed's Green book and the Survey of Professional Forecasters (SPF) are significantly more accurate than a random walk only before 1985. After this date, in contrast, the hypothesis of equal predictive ability between naive random walk forecasts and the predictions of those institutions is not rejected for all horizons, the only exception being the current quarter. The results of this chapter may also be of interest for the empirical literature on asymmetric information. Romer and Romer (2000), for instance, consider a sample ending in the early 1990s and find that the Fed produced more accurate forecasts of inflation and output compared to several commercial providers. The results imply that the informational advantage of the Fed and those private forecasters is in fact limited to the 1970s and the beginning of the 1980s. In contrast, during the last two decades no forecasting model is better than "tossing a coin" beyond the first quarter horizon, thereby implying that on average uninformed economic agents can effectively anticipate future macroeconomics developments. On the other hand, econometric models and economists' judgement are quite helpful for the forecasts over the very short horizon, that is relevant for conjunctural analysis. Moreover, the literature on forecasting methods, recently surveyed by Stock and Watson (2005), has devoted a great deal of attention towards identifying the best model for predicting inflation and output. The majority of studies however are based on full-sample periods. The main findings in the chapter reveal that most of the full sample predictability of U.S. macroeconomic series arises from the years before 1985. Long time series appear

to attach a far larger weight on the earlier sub-sample, which is characterized by a larger volatility of inflation and output. Results also suggest that some caution should be used in evaluating the performance of alternative forecasting models on the basis of a pool of different sub-periods as full sample analysis are likely to miss parameter instability.

The fourth chapter performs a detailed forecast comparison between the static factor model of Stock and Watson (2002) (SW) and the dynamic factor model of Forni et. al. (2005) (FHLR). It is not the first work in performing such an evaluation. Boivin and Ng (2005) focus on a very similar problem, while Stock and Watson (2005) compare the performances of a larger class of predictors. The SW and FHLR methods essentially differ in the computation of the forecast of the common component. In particular, they differ in the estimation of the factor space and in the way projections onto this space are performed. In SW, the factors are estimated by static Principal Components (PC) of the sample covariance matrix and the forecast of the common component is simply the projection of the predicted variable on the factors. FHLR propose efficiency improvements in two directions. First, they estimate the common factors based on Generalized Principal Components (GPC) in which observations are weighted according to their signal to noise ratio. Second, they impose the constraints implied by the dynamic factors structure when the variables of interest are projected on the common factors. Specifically, they take into account the leading and lagging relations across series by means of principal components in the frequency domain. This allows for an efficient aggregation of variables that may be out of phase. Whether these efficiency improvements are helpful to forecast in a finite sample is however an empirical question. Literature has not yet reached a consensus. On the one hand, Stock and Watson (2005) show that both methods perform similarly (although they focus on the weighting of the idiosyncratic and not on the dynamic restrictions), while Boivin and Ng (2005) show that SW's method largely outperforms the FHLR's and, in particular, conjecture that the dynamic restrictions implied by the method are harmful for the forecast accuracy of the model. This chapter tries to shed some new light on these conflicting results. It

focuses on the Industrial Production index (IP) and the Consumer Price Index (CPI) and bases the evaluation on a simulated out-of sample forecasting exercise. The data set, borrowed from Stock and Watson (2002), consists of 146 monthly observations for the US economy. The data spans from 1959 to 1999. In order to isolate and evaluate specific characteristics of the methods, a procedure, where the

two non-parametric approaches are nested in a common framework, is designed. In addition, for both versions of the factor model forecasts, the chapter studies the contribution of the idiosyncratic component to the forecast. Other non-core aspects of the model are also investigated: robustness with respect to the choice of the number of factors and variable transformations. Finally, the chapter performs a sub-sample performances of the factor based forecasts. The purpose of this exercise is to design an experiment for assessing the contribution of the core characteristics of different models to the forecasting performance and discussing auxiliary issues. Hopefully this may also serve as a guide for practitioners in the field. As in Stock and Watson (2005), results show that efficiency improvements due to the weighting of the idiosyncratic components do not lead to significant more accurate forecasts, but, in contrast to Boivin and Ng (2005), it is shown that the dynamic restrictions imposed by the procedure of Forni et al. (2005) are not harmful for predictability. The main conclusion is that the two methods have a similar performance and produce highly collinear forecasts.

Doctorat en sciences économiques, Orientation économie
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Chapter 1: The Determinants of the Production of Research by US Universities

Chapter 2: Investment in Vocational and General Human Capital: A Theoretical Approach

Chapter 3: Urban Migrations and the Labor Market

Chapter 4: Local social capital and geographical mobility

Chapter 5: Social Supervision and Electoral Stability on the Geographical Scale in Belgium
Doctorat en Sciences économiques et de gestion
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This three year indepth study was prompted after a decade of working as a corporate advisor for numerous stockbroking firms' corporate advisory and listing divisions. An overwhelming lack of discernible pricing methodology for IPOs on the JSE's Main Board and failed Venture Capital and Development Capital Markets was transferred to the new Alternative Exchange (AltX). This prompted lengthly discussions with former head of JSE's AltX Noah Greenhill. Such discussions are set out in this dissertation and relate to pricing methodologies and the lack of guidance or legislation as set out in the JSE's schedule 21 of Listing requirements. The focus of this dissertation is thus centred on whether the current adopted methodologies to establish a fair and reasonable pre-IPO share price is effective. To achieve this, global pricing methodologies were assessed within the framework of various valuation techniques used by South African Designated Advisors.

Comovement is ubiquitous in financial markets. The evolution of asset characteristics, such as price, volatility or liquidity, exhibits a high degree of correlation across assets---a phenomenon that in this thesis will generically be denoted with the term comovement. The origins of such comovement are legion. In their investment decisions, economic agents are not only influenced by their idiosyncrasies---a large part of investment motivations are shared over a population. Demographics or the political situation can generate constraints that are similar for a large number of people. A country's geography can greatly influence the sectors in which it is most productive, which implies that many people are sometimes subject to the same risk factors. Moreover, it is well known that mimesis is part of human psychology, and that people mimic their peers even when taking personal decisions. For these reasons, and many more, financial markets have a very systematic character, and studying the nature and intensity of such comovement is important from a risk management point of view.

This thesis studies comovement in financial markets under three dimensions. First, I consider comovement in equity liquidity. The liquidity of an asset is the ease with which that asset can be bought or sold. Liquidity can be measured in various ways and the first chapter concludes that market movements of two different liquidity measures have the same origin. Second, I study the impact correlation comovement on the price of stocks. The correlations between stock returns and the market return evolve through time and are correlated themselves. The effect of this correlation comovement on asset prices is however ambiguous and there is not enough evidence to depict a clear image. Finally, I develop a model to investigate contagion dynamics in the secondary market for European sovereign bonds over the past two years. More particularly, I study whether changes in the bond price of one specific country have an impact the next day on the average bond price in Europe. The study concludes of that bonds of France, Ireland, Portugal, Spain and Italy have been most contagious, whereas the much more volatile Greek bonds have had little impact on the other European countries.
Doctorat en Sciences économiques et de gestion
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Housing markets are known to be affected by adverse environments (i.e., environmental air pollution incidents affect Indian urban residents). Urban atmosphere quality has changed extensively with PM2.5 and O3 becoming the primary atmosphere indicators of concern because of dense cities in recent years. There is a correlation between the air pollution of Amaravati with the housing market model. When estimating the housing market, the chapter makes use of the extended regression model together with several constant results in conformity with higher rule. However, there is an insignificant affinity including the concentration regarding SO2 and the concentration of O3 appears according to positively increase the housing values. This chapter therefore examines the influence of actual real estate investment over atmosphere characteristics through the use of a sample on 26 prefecture-level cities in India from 2010–2019 through countless econometric models.