Cointegration Relations between Turkish and International Equity Markets and Portfolio Choices

In this study monthly equity index values of twenty two emerging and twelve developed markets are used for the determination of cointegration relations developed by Johansen. The results of cointegration analysis show that Turkish stock market is cointegrated with seven developed and five emerging markets. After determining the integrated equity markets, different international portfolio scenarios are created by using Markowitz mean-variance model. These findings suggest that Turkish portfolio managers are able to monitor their asset allocations and minimize risks if they obtain a better understanding of how emerging and developed equity markets are integratedCointegration, Emerging Markets, Developed Markets, Portfolio, ISE.

Volatility Spillover Effect from Volatility Implied Index to Emerging Markets

This study has investigated the effect of VIX, created as an implied volatility in the US, on 15 emerging stock markets with the application of GJR-GARCH model. According to the results obtained, the emerging stock markets have leverage effect in conditional variance and emerging bad news concludes that volatility further increases. The results of the analysis show that implied volatility index affect Argentina, Brazil, Mexico, Chili, Peru, Hungary, Poland, Turkey, Malaysia, Thailand and Indonesia stock markets through volatility increasesImplied Volatility, Spillover Effect, GJR-GARCH Model,Emerging Markets

Oil Prices and Global Stock Markets: A Time-Varying Causality-In-Mean and Causality-in-Variance Analysis

This study examines the Granger-causal relationships between oil price movements and global stock returns by using time-varying Granger-causality tests in mean and in variance. We use the daily returns from Morgan Stanley Capital International (MSCI) G7 and the MSCI Emerging Stock Market Indexes to distinguish between the effects of daily oil price movements on G7 countries' and emerging market countries' stock markets. We further divide the emerging markets into two groups as oil-exporting and oil-importing countries. For the oil market, we use both the West Texas Intermediate (WTI) and Brent oil daily price movements. While the Granger-causality-in-mean tests indicate a causal link from WTI oil prices and G7 countries' stock returns to MSCI emerging countries' stock returns, the Granger-causality-in-variance tests suggest no causal link from global oil market prices to stock market returns. Nonetheless, a causal link from the G7 countries' stock returns to the MSCI emerging countries' stock returns is detected. In addition, G7 countries' stock market volatility is found to Granger-cause Brent oil price volatility. The time-varying Granger-causality-in-mean and Granger-causality-in-variance tests present new and further insights. A causal relationship between oil price changes and G7 countries' stock returns is found for some periods during and after the global financial crisis. Time-varying Granger-causality-in-variance test results indicate evidence of causal linkages among oil prices and global stock market returns that are specific only to certain time periods. We also find that there might be a difference between the movements in Brent and WTI oil prices with respect to their Granger-causal effects on oil-importing emerging markets' stock returnsespecially after the global financial crisis. Our results provide further evidence that the effects of oil price movements on stock returns might be different depending on the volatility in the stock markets

Testing causal relation among central and eastern European equity markets: evidence from asymmetric causality test

The aim of this study is to analyse the presence of a causal link among financial markets of Central and Eastern Europe (CEE) countries by adopting an asymmetric causality test. The standard causality test results suggest a causal relation running from the Czech Republic to Poland. Also, the Poland stock market is found to be a Granger cause of Turkey stock markets. Asymmetric causality test results indicate only a causal link going from the Czech Republic to Hungary and Poland. In addition, the presence of financial integration between Germany and CEE equity markets cannot be determined

QUALITY EVALUATION OF BIOSIMILAR MEDICINES: AN OVERVIEW

Biosimilar medicines are biotherapeutics that are similar in quality, safety and efficacy to previously licensed reference biotherapeutics. The slightest change in any stage of production can cause differences in the product. Among the factors, affecting production can be listed as; host cell selection, fermenter type, ambient conditions, broth, substances used for cell culture, fermentation method and purification method. The similarity should be demonstrated by comparative quality, non-clinical and clinical tests. Research and development studies in the biopharmaceutical field bring diversity of quality control methods along with the formulation and manufacturing method of the biosimilars. Although there are some standardized and validated quality control methods given in the internationally recognized pharmacopoeias, there are many in house methods of biopharmaceutical product owners that can only be used as internal quality control methods by them. The main international sources for quality control methods of biopharmaceutics can be given as pharmacopoeias, International Organization for Standardization standards and Organization for Economic Co-operation and Development methods. In this review manufacturing process, regulatory guidelines and quality control of biosimilar medicines briefly are given.                   Peer Review History: Received 11 March 2020; Revised 20 April; Accepted 3 May, Available online 15 May 2020 Academic Editor: Dr. Asia Selman Abdullah, Al-Razi university, Department of Pharmacy, Yemen, [email protected] UJPR follows the most transparent and toughest ‘Advanced OPEN peer review’ system. The identity of the authors and, reviewers will be known to each other. This transparent process will help to eradicate any possible malicious/purposeful interference by any person (publishing staff, reviewer, editor, author, etc) during peer review. As a result of this unique system, all reviewers will get their due recognition and respect, once their names are published in the papers. We expect that, by publishing peer review reports with published papers, will be helpful to many authors for drafting their article according to the specifications. Auhors will remove any error of their article and they will improve their article(s) according to the previous reports displayed with published article(s). The main purpose of it is ‘to improve the quality of a candidate manuscript’. Our reviewers check the ‘strength and weakness of a manuscript honestly’. There will increase in the perfection, and transparency. Received file:                Reviewer's Comments: Average Peer review marks at initial stage: 6.0/10 Average Peer review marks at publication stage: 7.5/10 Reviewer(s) detail: Dr. Mohamed Salama, Modern University for Technology & Information, Egypt, [email protected] Dr.  Maha Khalifa Ahmed Khalifa, Al-Azhar Universit - Cairo, Egypt, [email protected]

The log exponential-power distribution: Properties, estimations and quantile regression model

Recently, bounded distributions have attracted attention. These distributions are frequently used in modeling rate and proportion data sets. In this study, a new alternative model is proposed for modeling bounded data sets. Parameter estimations of the proposed distribution are obtained via maximum likelihood method. In addition, a new regression model is defined under the proposed distribution and its residual analysis is examined. As a result of the empirical studies on real data sets, it is observed that the proposed regression model gives better results than the unit-Weibull and Kumaraswamy regression models

The hjorth's IDB generator of distributions: properties, characterizations, regression modeling and applications

We introduce a new flexible class of continuous distributions via the Hjorth’s IDB model. We provide some mathematical prop-erties of the new family. Characterizations based on two truncated moments, conditional expectation as well as in terms of thehazard function are presented. The maximum likelihood method is used for estimating the model parameters. We assess the per-formance of the maximum likelihood estimators in terms of biases and mean squared errors by means of the simulation study.A new regression model as well as residual analysis are presented. Finally, the usefulness of the family is illustrated by means offour real data sets. The new model provides consistently better fits than other competitive models for these data sets

A new flexible family of continuous distributions: the additive Odd-G family

This paper introduces a new family of distributions based on the additive model structure. Three submodels of the proposed family are studied in detail. Two simulation studies were performed to discuss the maximum likelihood estimators of the model parameters. The log location-scale regression model based on a new generalization of the Weibull distribution is introduced. Three datasets were used to show the importance of the proposed family. Based on the empirical results, we concluded that the proposed family is quite competitive compared to other models

The Odd Power Lindley Generator of Probability Distributions: Properties, Characterizations and Regression Modeling

In this study, a new flexible family of distributions is proposed with its statistical properties as well as some useful characterizations. The maximum likelihood method is used to estimate the unknown model parameters by means of two simulation studies. A new regression model is proposed based on a special member of the proposed family called, the log odd power Lindley Weibull distribution. Residual analysis is conducted to evaluate the model assumptions. Four applications to real data sets are given to demonstrate the usefulness of the proposed model

The extended gamma distribution with regression model and applications

This paper introduces a new extension of the gamma distribution, named as a new extended gamma distribution, via mixture representation of xgamma and gamma distributions. The statistical properties of the proposed distribution are derived such as moment generating and characteristic functions, variance, skewness, and kurtosis measures, Lorenz curve, and mean residual life function. The maximum likelihood, parametric bootstrap, method of moments, least squares, and weighted least squares estimation methods are considered to obtain the unknown model parameters. The finite sample performance of estimation methods is discussed via a simulation study. Using the proposed distribution, we propose a new regression model for the right-skewed response variable as an alternative to the gamma regression model. Two real data sets are analyzed to convince the readers for the usefulness of the proposed model