Estimating of CVaR and Analysis of Leverage Effect Based on Nonlinear Quantile Regression Model

This thesis consists of three empirical studies on what drives stock market dynamics. The first empirical study explores the effect of crude oil price changes on the stock market returns of oil-exporting countries and oil-importing countries as well as those of a number of global stock indices. Using the Ordinary Least Squares (OLS) approach as well as the more robust Quantile Regression (QR) approach to explore the relationship between crude oil and stock market dynamics. The empirical findings suggest that the QR approach provides further insights compared to the OLS approach. For instance, the QR approach is able to identify specific quantiles where a significant relation exists. In particular crude oil price increases tend to have a negative impact on the stock market returns for some oil-exporting countries (such as Mexico, Iraq, Ecuador, and Venezuela) and a positive effect for other oil-exporting countries (such as Brazil and Algeria). However, the OLS approach suggests that these relationships are insignificant at the level of the mean. Overall, the empirical findings confirm that the QR approach can reveal more information about the relationship between crude oil price changes and stock market return across different quantiles of their distribution.The second study explores the extent to which implied volatility extracted from commodity markets and developed stock markets can predict the implied volatility of stock markets in BRICS countries. Using daily data from 2011 to 2016 and employing the newly developed Bayesian Graphical Vector Autoregressive (BGVAR) model of Ahelegbey et al. (2016) which does not suffer from over-parameterization and the identification problems associated with traditional VAR frameworks, this study finds that implied volatilities extracted from global and regional stock markets have a significant predictive power over the implied volatilities in BRICS stock markets. However, the predictive power of implied volatility from commodity markets are significant only in the case of South Africa.The third empirical study analyses the relationship between illiquidity and stock market returns in the G7 and BRICS countries. More specifically, this study explore the extent to which the Amihud (2002) illiquidity measure can improve the explanatory power of three commonly used asset pricing models, namely the Capital Asset Pricing Model (CAPM), the Fama-French three-factor model and the Carhart four-factor model. The empirical analysis is based on 15 years of monthly data on the returns of seven stock portfolios: 100 largest companies (Largest100), small value (S/V), small neutral (S/N), small growth (S/G) stocks, big value (B/V) stocks, big neutral (B/N) stocks, and big growth (B/G) stocks. The findings suggest that incorporating illiquidity as an additional factor results in a significant improvement in the explanatory power of these asset pricing models across several of the sample countries (8 countries in the case of the CAPM and Carhart four-factor model, and 6 countries in the case of the Fama-French three-factor model). For example, in the US adding illiquidity to the CAPM leads to an increase of the goodness of fit by 2.6% in the B/V portfolio, and for the Fama-French three-factor model the goodness of fit increases by up to 3% in all portfolios Moreover, the goodness of fit increases in all portfolios in the US by adding illiquidity to the Carhart four-factor model, with an up to 36% increase in the B/N portfolio.

Our paper examines the effect of oil price changes on Gulf Cooperation Council (GCC) stock markets using nonlinear smooth transition regression (STR) models. Contrary to conventional wisdom, our empirical results reveal that GCC stock markets do not have similar sensitivities to oil price changes. We document the presence of stock market returns’ asymmetric reactions in some GCC countries, but not for others. In Kuwait’s case, negative oil price changes exert larger impacts on stock returns than positive oil price changes. When considering the asymmetry with respect to the magnitude of oil price variation, we find that Oman’s and Qatar’s stock markets are more sensitive to large oil price changes than to small ones. Our results highlight the importance of economic stabilization and reform policies that can potentially reduce the sensitivity of stock returns to oil price changes, especially with regard to the existence of asymmetric behavior.

This study employs the daily data of the Stock Exchange of Thailand to test for the leverage and volatility feedback effects. The period of investigation is during January 4, 2005 to December 27, 2013, which includes the Subprime crisis period in the US that might affect the volatility of stock market return in emerging stock markets. The results from this study show that the US subprime crisis imposes a minimal positive impact on volatility. In addition, the estimations of the three parametric asymmetric volatility models give the results showing some evidence of the volatility feedback and leverage effects. The findings give implications for portfolio diversification and risk management.

Quantile regression (QR) is becoming increasingly popular due to its relevance in many scientific investigations. There is a great amount of work about linear and nonlinear QR models. Specifically, nonparametric estimation of the conditional quantiles received particular attention, due to its model flexibility. However, nonparametric QR techniques are limited in the number of covariates. Dimension reduction offers a solution to this problem by considering low-dimensional smoothing without specifying any parametric or nonparametric regression relation. Existing dimension reduction techniques focus on the entire conditional distribution. We, on the other hand, turn our attention to dimension reduction techniques for conditional quantiles and introduce a new method for reducing the dimension of the predictor X. The novelty of this paper is threefold. We start by considering a single index quantile regression model, which assumes that the conditional quantile depends on X through a single linear combination of the predictors, then extend to a multi index quantile regression model, and finally, generalize the proposed methodology to any statistical functional of the conditional distribution. The performance of the methodology is demonstrated through simulation examples and a real data application. Our results suggest that this method has a good finite sample performance and often outperforms existing methods.

This paper mainly investigates long term equity return in the Taiwan stock market. We attempted to find the optimal model among and perform diagnostic analysis of the stochastic asset models, such as the independent lognormal model, the ARCH-type model, the Regime-Switching Lognormal model, and the Stochastic Log-Volatility model, all of which have been proposed by the American Academy of Actuaries. In addition, the most suitable long term equity model was determined. In the empirical study of the Taiwan stock market, the parameter estimations were carried out using maximum likelihood estimate and efficient moment of method. Following, we compared log-likelihood, Akaike Information Criterion and Schwartz Bayesian Criterion and drew Quantile-Quantile plots to test tails, to determine the appropriateness of models. Finally, Conditional Tail Expectation risk measures of stochastic equity models were estimated from simulation of the liability of a guaranteed minimum accumulation benefit contract. The results indicate that the generalized autoregressive conditional heteroskedastic and regime switching log normal models perform much better than other considered models for analyzing the Taiwan financial market.

In this study, we adopted the AR (p)-cum-volume & EGARCH (1,1)-M-cum- volume model to explore the mixture of distributions hypothesis (MDH), and risk-return-volume relationship in the Taiwan stock market. This model features simultaneously examining five hypotheses as well as the ability to probe into whether or not the stock markets conforms to the Clarks' (1973) MDH. These hypotheses: (H1) contemporaneous volume effects daily stock return; (H2) daily stock volatility appears the phenomena of clustering and persistence; (H3) stock return volatility appears an asymmetry effect, showing the leverage effect; (H4) contemporaneous volume effects to the conditional volatility of the daily stock return; (H5) daily stock return appears risk premium. The objects of this study were the top 25 listed stocks based on the ETF 50 value in the Taiwan stock market. According to the result of our research, we observed that, on the Taiwan stock market, the daily stock return volatility is with the phenomena of clustering and persistence, and the said conditional volatility appears asymmetric. We also found, for most of the stocks, the contemporaneous volume appears a significant direct proportion to the daily stock return and the conditional volatility. This finding supports the existing of the risk premium. The results of our study support that the Taiwan stock market can be predicated by Clarks' (1973) mixture of distributions hypothesis.

The purpose of this study is to compare two major risk measurement models in financial markets—the Value at Risk (VaR) model and the Conditional Value at Risk (CVaR) model—and to evaluate their effectiveness and limitations in stock market risk management. First, through an in-depth analysis of the theoretical foundations of stock market risk, the paper clarifies the importance of risk for investment decision-making, in particular its impact on asset allocation and risk management strategies. The findings show that VaR, as a traditional risk measurement tool, is simple and convenient and enables a rapid assessment of market risk; however, when confronted with extreme tail events it exhibits certain shortcomings and cannot fully capture the true risk of extreme losses. By contrast, CVaR, by focusing on tail risk, makes it possible to identify potential extreme losses more accurately; especially during periods of sharp market volatility, its early-warning function substantially outperforms VaR. In the empirical part of the article, historical data for the Shanghai A-share index are used. The GARCH-M model is applied to estimate VaR and CVaR, and their values are compared when measuring stock market risk. The results indicate that, at the same confidence level, CVaR is always greater than VaR, which further confirms the advantage of CVaR in reflecting risk losses more accurately, especially tail risk. Overall, this study not only provides a theoretical basis for risk management in financial markets, but also offers recommendations for improving risk measurement methods in the stock market, emphasizing the need to account for tail risk when making investment decisions. The conclusions have important reference value for investors seeking to develop more scientifically grounded risk control strategies in a dynamic market environment.

The purpose of this selective review is to present recent theoretical findings on the modelling of ARCH type non-linear times series. We provide an overview of recent theoretical results on the existence and the structure of stationary solutions to ARCH(∞), LARCH, bilinear ARCH, EGARCH, IARCH and random coefficient ARCH models, and investigate their second order dependence (memory) structure. The topics discussed in the review are: existence of a stationary solution, the presence of the short memory and long memory in ARCH type models, leverage effect, asymptotic behavior of the sums (sample mean), aggregation, parameter estimation and testing for the change-points.

The paper examines the relationship between powerful CEO and firm performance using the estimation of quantile regression which models heterogeneous effects of a variable along different points of an outcome distribution. This paper targets the nonlinear and size effects of CEO power on firm performance and explores the possibility that CEO power and firm performance are endogenously determined. The results indicate that CEOs tend to possess more power in the early stage of a company's lifecycle. They acquire more ownership, expert, and prestige power following poor market returns. There is an inverted U-shaped relation between CEO ownership and accounting performance in low quantiles of firm performance distribution, while the impact on performance becomes negative in high quantiles. Moreover, the size effect shows that the positive impact of ownership decreases when firm size grows. CEOs' expert power improves firm performance, although the effect turns negative when tenure exceeds 15 years. The negative impact of expert power is more significant in small firms. Finally, CEOs' prestige power enhances firm performance in low quantiles and hurts performance in high quantiles.

The stock market has captured the attention of many practitioners and scholars in the past decade. It has become one of the most vital aspects of a modern market economy. The stock market provides companies with access to capital and gives opportunity for investors to have a slice of company ownership. The present paper investigates the impact of G-3 stock markets (US, Japan and Europe) to ASEAN-5 stock markets (Indonesia, Malaysia, Philippines, Thailand and Singapore). The data coverage is composed of daily closing stock index at G-3 stock markets and ASEAN-5 stock markets over the period from January 4, 2000 to December 31, 2014. The historical stock market data were analyzed by using Structured Equation Model (SEM). The empirical results suggest that the G-3 stock markets have a positive and significant impact on ASEAN-5 stock markets. For further, the researcher could add other Asia stock markets such as Nikkei225 Index (Japan), Hang Seng Index (Hong Kong), Kospi Index (South Korea), and BSE Index (India). Keywords : G-3 Stock Markets, ASEAN-5 Stock Markets, Structured Equation Model, Stock Market Diversification; Contagious Effect.

In this paper, heteroscedastic nonlinear regression (HNLR) models under the flexible class of two–piece distributions based on the scale mixtures of normal (TP–SMN) family were examined. This novel class of nonlinear regression (NLR) models is a generalization of the well-known heteroscedastic symmetrical nonlinear regression models. The TP–SMN is a rich class of distributions that covers symmetric and asymmetric as well as heavy-tailed distributions. Using the suitable hierarchical representation of the family, the researchers first derived an EM–type algorithm for iteratively computing maximum likelihood (ML) estimates of the parameters. Then, in order to examine the performance of the proposed models and methods, some simulation studies were presented to show the robust aspect of this flexible class against outlying and also atypical data. As the last step, a natural real dataset was fitted under the proposed HNLR models.

Dimitriadis and Bayer (2019) proposed a new regression framework which simultaneously models the quantile and the Expected Shortfall (ES) of a response variable given a set of covariates. This framework allows the use of different covariate vectors to make combined predictions of the models. Combined forecasting methods often provide a more practical way to synthesize the information provided by individual forecasting methods. Expected loss ES as a risk measure that satisfies subadditivity, especially since the Basel Committee recommended it as an alternative risk measure in 2016, has received more attention. In this paper, under the regression framework of Dimitriadis and Bayer (2019), different quantile covariates are used to study the advantages and disadvantages of different risk prediction models for combined prediction. CAViaR-SAV-VaR quantile prediction method by introducing AR(1)-GARCH model and quantile regression. A series of combinations of each prediction model are performed to explore the optimal combination of prediction models. In this paper, a series of independent forecasting models and combinations of models are used to empirically analyze the daily logarithmic return data of my country's Shanghai Composite Index. The results show that the forecasting effect of the combined model based on CAViaR-SAV-VaR and RM is better than other model combinations.

This dissertation is dedicated to the analysis of three superordinate economic principles in varying market environments: market efficiency, the behavior of market participants and information asymmetry.\nSustainability and social responsibility have gained importance as investment criteria in recent years. However, responsible investing can lead to conflicting goals with respect to utility-maximizing behavior and portfolio diversification in efficient markets. Conducting a meta-analysis, this thesis presents evidence that positive (non-monetary) side effects of responsible investing can overcome this burden. Next, the impact of the EU-wide regulation of investment research on the interplay between information asymmetry, idiosyncratic risk, liquidity and the role of financial analysts in stock markets is investigated. An empirical analysis of the emerging primary and secondary market for cryptocurrencies yields further insights about the effects of information asymmetry between investors, issuers and traders. The efficient allocation of resources is dependent on the market microstructure, the behavior of market participants, as well as exogenous shocks. Against this background, this thesis is dedicated to the empirical analysis of limit order books, the rationality of traders and the impact of COVID-19. Due to its young history, the market for cryptocurrencies yields a suitable research subject to test classical financial theories. This doctoral thesis reveals parallels between the microstructure of cryptocurrency and stock markets and uncovers some previously unknown statistical properties of the cryptocurrency market microstructure. An initial examination of the impact of COVID-19 further shows that cryptocurrencies with a high market capitalization seem to react to macroeconomic shocks similar to stock markets.\nThis cumulative dissertation comprises six stand-alone papers, of which three papers have already been published.

This study investigates the long-run and short-run movements of two emerging stock market volatilities using a volatility decomposition methodology. We studied the impact of 2007–2008 subprime mortgage crisis on the transitory and permanent volatility components in terms of two empirical stylized facts, the leverage effect and volatility persistence. In order to do so, the long spanning data are separated into three different periods. For the former stylized fact, the crisis impact on the leverage effect is mainly temporary with no long-run effect to the stock markets. This finding explains that the leverage effect is mostly difficult to adjust in the short-run transitory volatility during the crisis periods. However with proper risk management and long term strategies, most of the market participants are able to anticipate and handle this news impact in the long-run. For the latter stylized fact, the crisis has slightly increased the volatility persistence in all the markets. From the viewpoint of heterogeneous market hypothesis, the higher intensity of volatility persistence implies the stock markets are less informational efficient.

این مطالعه به منظور بررسی پیش بینی شاخص های عملکرد تولیدی در مرغان تخم گذار با استفاده از شبکه های عصبی مصنوعی و رگرسیون غیرخطی چندگانه انجام شد. بررسی بر روی اطلاعات چهار دوره متوالی پرورش در یک واحد پرورش مرغ تخم گذار صورت گرفت. روش های داده-کاوی شامل رگرسیون خطی و غیر خطی، شبکه عصبی پرسپترون سه لایه، شبکه عصبی پرسپترون چهار لایه و شبکه عصبی با تابع پایه ای شعاعی بود. در این مدل ها از متغیرهای سن گله، میزان خوراک مصرفی و فصل تولید به عنوان متغیر پیشگو و شاخص های عملکرد تولیدی شامل درصد تخم-گذاری، وزن توده ای تخم مرغ تولیدی و ضریب تبدیل غذایی به عنوان متغیر پاسخ استفاده شد. نتایج نهایی رگرسیون های خطی نشان داد که برای تمامی متغیرهای وابسته مورد مطالعه متغیر مستقل سن گله معنی دار می باشد. بنابراین رگرسیون غیر خطی شاخص های عملکرد تولیدی در مقابل سن برای مقایسه با شبکه های عصبی مختلف مورد بررسی قرار گرفت و برای مقایسه کلیه مدل ها از ضریب تعیین (R2) و میانگین قدر مطلق خطا (MAE) استفاده شد. نتایج نشان داد بین شبکه های عصبی مصنوعی مختلف مورد مطالعه، شبکه با تابع پایه ای شعاعی بهتر از سایر مدل های در پیش‌بینی شاخص های عملکرد تولیدی مرغان تخم گذار عمل می کند.