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Über dieses Buch

This handbook presents emerging research exploring the theoretical and practical aspects of econometric techniques for the financial sector and their applications in economics. By doing so, it offers invaluable tools for predicting and weighing the risks of multiple investments by incorporating data analysis. Throughout the book the authors address a broad range of topics such as predictive analysis, monetary policy, economic growth, systemic risk and investment behavior.

This book is a must-read for researchers, scholars and practitioners in the field of economics who are interested in a better understanding of current research on the application of econometric methods to financial sector data.

Inhaltsverzeichnis

Frontmatter

Exploratory Classification of Time-Series

Abstract
In this paper, an exploratory hierarchical method to classify variables is introduced as an alternative to principal component analysis when dealing with stock-exchange price time-series. The method is based on a particular principal component analysis applied to pairs of variables, each one associated to a group to be merged. Applied to time-series, this method reveals advantageous, since it helps in defining the number of groups and their composition, while providing a factorial structure of both the hierarchy’s nodes and the partition groups. Moreover, all the issued factors, which are weighted sums of the original variables forming the groups, result in easily interpretable representative variables of them. As a case study, the method is applied to a set of Brazilian financial stock price time-series, providing representative series for each of the five groups of the proposed partition. This result complements the information on the data set provided by principal component analysis, limited to the usual orthogonal factors, each one representing an independent source of variation. It is likely that the use of such classification method may help both in deepening the knowledge of a market structure and the modelling of the different time-series, based on the modelling of their representative one.
Sergio Camiz

Predicting the Tail Behavior of Financial Times Stock Exchange/Johannesburg Stock Exchange (FTSE/JSE) Closing Banking Indices: Extreme Value Theory Approach

Abstract
The incidence of rare but extreme events appears to be significant in worldwide financial markets. In this chapter we apply extreme value theory (EVT) distributions to predict extreme losses of five South African (SA) financial times stock exchange/Johannesburg Stock Exchange (FTSE/JSE) closing banking indices. The effectiveness of risk measures for measuring risk of investment is also explored. A 5-day time series for the period of 02 January 2008 to 20 April 2018 is used. The MS(2)-EGARCH(1,1) showed that there is a regime persistence in all the banks, implying that the new obtained series is independently and identically distributed (i.i.d). It is therefore concluded that the generalized Pareto distribution (GPD) is a better distribution than the generalized extreme value (GEV) in estimating extreme loses and that the computation of economic capital using Glue-value-at-risk (VaR) is more conservative than using other risk measures under the GEV distribution.
Katleho Makatjane, Ntebo Moroke, Elias Munapo

Financial Econometrics and Systemic Risk

Abstract
In this chapter, some of the many prominent and recent papers in the systemic risk literature are reviewed. In all these papers, financial econometrics methods are used whether to extract the connections between institutions or assets by analyzing the related data or to construct a measure of systemic risk. There are many published survey papers on systemic risk. However, there is still a gap for research whose focus is particularly on the econometric methods behind the calculation of systemic risk indicators. This chapter is an attempt to contribute to filling this gap.
M. Hakan Eratalay

Monetary Policy Shocks, Financial Heterogeneity, and Corporate Dynamic Investment Activity

Abstract
This chapter aims to scrutinize the role of financial conditions of firms in deciding investment dynamic to monetary policy shocks. Considering leverage and cash holding as explanatory financial variables, firms with low leverage and high cash holding react more to monetary policy shocks in explaining the different investment levels of the firms. The interactions of the monetary policy shock variables are statistically insignificant for the high-leverage and low cash holding firms, but they are statistically significant for the low-leverage and high cash holding firms. This study enhances the literature of corporate investment behavior which can be helpful for developing and optimizing macro-control policies. These results may be of independent interest to policymakers who are concerned about the distributional ramifications of monetary policy across firms.
Mahbuba Aktar, Qu Wenzhou, Hijbulla Al Mahmud, Mohammad Zoynul Abedin

Oil Price Scenarios: Economic and Fiscal Impacts on the Kuwait Economy

Abstract
This chapter examines major highlights on macroeconomic and fiscal issues affecting the Kuwaiti economy based on different oil price assumptions. Forecast, analysis and simulations are performed using the Kuwait SCPD macro model. For the Kuwait economy, the author has explored three scenarios corresponding to a low, medium and high oil price assumptions. The author analysed a price fall particularly in 2019–2020, which is a stylized representation of the oil market change over the horizon 2030. Unsurprisingly, the results show that such an oil price drop has direct effects in Kuwait, since Kuwait’s export is dependent on oil. By researching the relationship of oil prices to oil sector dependence, it becomes evident that Kuwait has a large portion of oil-related sectors in the economy and that indicates high elasticity of both GDP and government revenues to oil prices.
Sedat Dizmen

Exchange Rate Sensitivity of Firm Value: Evidence from Nonfinancial Firms Listed on Borsa Istanbul

Abstract
This chapter attempts to quantify the effect of exchange rate on the value of nonfinancial firms listed on Borsa Istanbul. In the first part of the analysis, the regression results using firm-level data show that currency fluctuations tend to influence the stock returns of 44 firms out of 177 firms in the sample in a significant way with negative average foreign exchange (FX) sensitivity coefficient. The sectoral-level analysis indicates that sectors with net FX short position are also subject to higher FX sensitivity with respect to firm value. In the second part, firm-level determinants of FX sensitivity are investigated using quantile regression method. The estimation results indicate that the market value of firms with net FX position surplus tends to respond positively to the depreciation of Turkish lira against the US dollar across all quantiles. It is also observed that the degree of internationalization, firm size, profitability, and growth opportunities are significant determinants of stock market pricing of FX risk.
İbrahim Ethem Güney, Abdullah Kazdal, Doruk Küçüksaraç, Muhammed Hasan Yılmaz

Limited Dependent Variables (Logit and Probit Models) and an Application on BIST-100: Logit and Probit Models

Abstract
Regression models of a dependent variable that take on qualitative values 0 and 1 cannot be interpreted as conventional regression models. The fact that the dependent variable takes on two different values causes some problems in the model. These problems include the fact that the model’s errors do not show normal distribution, that the model’s errors take on values less than 0 or greater than 1, and that the relationship between dependent and independent variables is not linear. Nonlinear models should be utilized to solve these problems. Nonlinear models include logit and probit models. Therefore, logit and probit models are discussed in detail in this chapter of the book. Macroeconomic factors affecting the return on the BIST-100 Index (Gold price per ounce, TL Deposit Interest, Euro-Dollar Currency Basket Return) have been investigated using logit and probit models. The findings of the study indicate that the return on the BIST-100 Index is affected by Euro-Dollar Currency Basket Return.
Lokman Kantar

Vector Autoregressive Model and Analysis

Abstract
The aim of this study is to explain vector autoregressive (VAR) models and Granger causality. VAR is an econometric model that generalizes univariate autoregressive (AR) models. VAR is a regression model that can be considered as a kind of hybrid among univariate time series models. VAR models are generally defined as alternatives to structural models of large-scale simultaneous equations. All variables in the model are treated symmetrically with an equation for each variable explaining the development of the variable, depending on the lags of the variable in the model and the lags of all other variables. The method is simple. It is not necessary to specify which variables are endogenous or exogenous. VAR models are generally better than traditional structural models. Granger causality test developed by Granger is a test used to determine the direction of causality of the relationship in the presence of delayed relationship between two variables. Granger causality is really just a correlation between the present value of one variable and the past values of others; the movements of one variable do not mean that it causes the movements of another. For the VAR model and Granger causality, the variables that affect the consumer confidence index is analyzed by using Eviews.
Murat Akkaya

Construction of the Monetary Conditions Index with TVP-VAR Model: Empirical Evidences for Turkish Economy

Abstract
As an important indicator for the central banks that adopting inflation targeting regime, this paper aims to construct a monetary conditions index (MCI) for Turkey based on the weighted sum approach covering the period of 1986:05–2018:10. As a novelty time-varying parameter vector autoregressive (TVP-VAR) models allowing the change of index weights is used over time. The results suggest that the weights of the index components varied substantially over the analysis period and the constructed index is able to capture the crisis periods accurately.
Coşkun Akdeniz

Monetary Policy Regimes, Fiscal Implications, and Policy Interactions Among Developing Economies

Abstract
This paper provides new empirical insights in order to give a relevant contribution to the more recent literature on international transmission of shocks among emerging market economies, with a particular emphasis in the most recent recession and postcrisis consolidation. Interdependence, commonality, and heterogeneity in macroeconomic-financial linkages are also identified in order to give new stimulus to the study of international business cycles and policy-making. An extension of a time-varying Structural Panel Bayesian Vector Autoregressive model is developed to deal with model misspecification and unobserved heterogeneity problems when studying multicountry dynamic panels and jointly investigating monetary and fiscal policy effects. The results argue that monetary policy transmission mechanisms and fiscal authority have worked actively among emerging markets but with different actions due to large differences in their financial structure.
Antonio Pacifico

The Impacts of Transportation Sector and Unemployment on Economic Growth: Evidence from Asymmetric Causality

Abstract
There are many factors that affect the economic growth of countries. Econometric studies in this area have a wide range of literature. In this study, the effects of transportation sector growth and unemployment rates on economic growth were examined. The application was made with economic indicators of the United States and the data covers the 2000/Q1 and 2018/Q4 periods. Selected indicators include the Transport Service Index (TSE), which represents the development of the transport sector, Gross Domestic Product (GDP) which represents the economic growth and Unemployment Rate (UR) which represents the unemployment. Analyzes were started with stationary tests. After the graphical examination, traditional unit root tests were applied. Some inconsistent results from these tests and the fact that this period includes the global crisis necessitated the implementation of unit root tests with structural breaks. After the Vector Auto Regressive (VAR) model was established, the stability and assumptions of the model were examined. Granger causality test was applied to the stable model. According to the results of this test, no significant causal relationships were found between the variables. In addition, the relationships between variables were examined by asymmetric causality analyzes proposed by Hatemi (Empirical Economics 43:447-456, 2012). This analysis is made by considering that the responses of the variables to positive and negative shocks may be different. In the last part of the study, the results of asymmetric causality analyses were interpreted in detail.
Sultan Kuzu Yıldırım

ARCH Models and an Application on Exchange Rate Volatility: ARCH and GARCH Models

Abstract
The financial liberalization that began in the last quarter of the twentieth century caused sudden movements in the currencies and financial assets of the countries. These sudden movements are called volatility. Sudden price changes in financial assets made it difficult to predict the future and increased the risks of financial assets. Investors wishing to invest in financial assets wanted to estimate the price of assets correctly to minimize their risks; this has revealed the need for accurate determination of volatility. Since the changes in asset prices are not linear, volatilities in prices are determined by nonlinear methods. This chapter discusses the GARCH models (GARCH, GJR, EGARCH), which are nonlinear models, and tests the validity of these models through a Turkey application on exchange rate volatility. The findings of the study have indicated that the GARCH (1,1) model successfully explained the volatility in the exchange rate.
Lokman Kantar

Using COGARCH-Filtered Volatility in Modelling Within ARDL Framework

Abstract
The aim of this chapter is to use volatility data, obtained from Continuous GARCH process, in the ARDL Bounds testing approach. For this purpose, the volatility of financial data is modelled by the Continuous GARCH process which is a generalized solution of Lévy driven stochastic differential equation. The impact of the volatility on another variable is analyzed via ARDL Bounds testing approach that gives the opportunity to analyze the short-run and long-term relation, cointegration between variables. The real data application and the R codes are given as an illustration.
Yakup Arı

Performance of MS-GARCH Models: Bayesian MCMC-Based Estimation

Abstract
In this chapter, both Maximum likelihood estimation (MLE) and Bayesian MCMC estimation methods are used to test their parameters estimation power while estimating a Markov-Switching generalized autoregressive conditional heteroscedasticity (MS-GARCH) model. The monthly exchange rates of BRICS countries for the period from 1997 to 2017 were used for this empirical analysis. MS(2)-GARCH (1,1) is estimated using both the MLE and Bayesian MCMC. For both methods of estimation, the models were found to be adequate and can be used for further analysis. Prior estimation for the MS (2)-GARCH (1,1), various nonlinearity and nonstationarity tests were estimated with the aim of testing the presence of nonlinearity and the result revealed that the exchange rates were nonlinear in nature. For the comparative analysis, the models with Bayesian MCMC estimates outperformed the one with MLE estimates using error matrices. Furthermore, Diebold-Mariano test was used to assess the predictive accuracy of the models and results confirmed that models with Bayesian MCMC performed better.
Lawrence Diteboho Xaba, Ntebogang Dinah Moroke, Lebotsa Daniel Metsileng

Volatility Spillovers Between Oil Prices and BIST (Borsa Istanbul) Dividend Indexes

Abstract
The main purpose of this chapter is to investigate the causality-in-variance (risk spillovers) between oil prices and BIST (Borsa Istanbul) dividend indexes returns. To this end, Brent crude oil futures prices, BIST Dividend, and BIST Dividend 25 indexes are used. The empirical investigation includes a causality-in-variance analysis introduced by Hafner and Herwartz (Econ Lett 93(1):137–141, 2006). The preliminary univariate GARCH model estimates show that oil prices and dividend indexes returns are considerably affected by long-run volatility. The causality-in-variance test results suggest a significant one-way volatility spillover effect from oil prices to BIST dividend and BIST Dividend 25 indexes returns. The statistical significance of this effect is more pronounced for the BIST Dividend 25 index than for the BIST Dividend index. According to these results, the more significant effect appears due to the higher sensitivity of the BIST Dividend 25 index returns to the variation in economic activity caused by oil price shocks.
Barış Kocaarslan

Panel Data Analysis

Abstract
Classical finance has undergone a major change in recent years. In our modern world, where risk becomes more complex and difficult to calculate, more sophisticated mathematical techniques and products are needed to quantify such new risks. The financial sector should have a solid structure in order for the real sector to be able to supply the financing it needs without interruption. Financial sector wants to maintain and increase their profitability in a sustainable manner as they do in other production companies. The functioning and change of the financial system structure have always been interesting for researchers from the past to the present. Researchers investigating the financial system try to test financial data using econometric methods. Financial econometrics is the application of statistical methods to financial sector data. Panel data, which is composite data, accommodates both the horizontal and the time dimensions of the data. Therefore, researchers who want to examine the change of a group (each observation value in horizontal section) over time with econometric tests frequently need panel data analysis. By using panel data methods, more reliable and healthy results can be achieved.
Hasan Huseyin Yıldırım

An Amalgamation of Big Data Analytics with Tweet Feeds for Stock Market Trend Anticipating Systems: A Review

Abstract
In digital era, the provision of streaming data and its storage has taken wider forms where data can be in structure, semi-structured and unstructured forms. As data is increasing the storage capacity also has to be increased, and the processing of data from such huge storage may be time consuming. And it is tricky to handle and process such data via conventional software and database procedures, which lead to the research toward big data analytics. It aims at handling of massive data storage with fast processing techniques and to help companies in optimizing business, advancement of operations, making more intelligent, and fast decisions. Hence Big data analytics is an important field that derives insights from the data and prediction system is one of the famous applications of it. It takes the historical data and analyses, and then it forecasts the past and future situations basing on identified hidden patterns of data considered. The analytics can be categorized into different forms namely Business Analytics (BA) and Predictive Analytics (PA). The inclusion of skills, technologies, applications, and processes with statistical techniques adopted by organizations for their data available to impel business planning is referred to as business analytics. The forecasting approach to foresee upcoming events and trends known as Predictive Analytics, which identifies the hidden patterns and determines what is likely to happen from the historical information available using statistical and mathematical models. To enhance the forecasting process, an opinion mining can also be included. Nowadays sentiment of the people also considered to improve the accuracy level of anticipation. Effecting factors should be considered and clearly analyzed to construct accurate model so as to supply most relevant suggestions. Several researchers proposed various prediction algorithms and methods in order to construct the accuracy improved model and user satisfaction. In this chapter, authors studied various anticipating models and discussed their preference criteria. As a part of that, we studied various important preference factors in stock trend prediction and categorized them based on effecting factors. This chapter reports prospect directions in prediction models and compiling an easy guide reference list to help out the researchers.
Deepika Nalabala, M. Nirupama Bhat, P. Victer Paul

Capital Structure Adjustment Speed: Evidence from Borsa Istanbul Sub-Sectors

Abstract
In this chapter, we have scrutinized the adjustment speed of Borsa Istanbul index sub-sectors during the pre-crisis, crisis, and post-crisis periods. The classical dynamic partial adjustment model developed by Flannery and Rangan (Journal of Financial Economics 79:469–506, 2006), which is frequently used in the literature, is used to estimate the target debt level. The adjustment speed could be determined except in one of the six sectors in the BIST100 index. For all periods, the adjustment speed of the sub-sectors is below 50%. Empirical evidence on the existence of the target debt level of these five sectors supported the trade-off theory. Another striking finding is the significant decrease in the adjustment speed of the BİST 100 index sub-sectors during the crisis period.
Turhan Korkmaz, Aslı Yıkılmaz Erkol
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