İstanbul Menkul Kıymetler Borsası nda İşlem Hacmi ve Getiri Volatilitesi

Kiran, Burcu

doi:10.31671/dogus.2019.180

Cited by 10 publications

(8 citation statements)

References 10 publications

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“…Bu model finans alanında en yaygın kullanılan bir finansal volatilite tahmin modelidir. Söz konusu model volatilite ölçmekle kalmayıp volatilite üzerindeki şokların sürekli olup olmadığını da gösteren bir yöntemdir (Kıran, 2010). Bollerslev'in (1986) geliştirdiği GARCH (Genelleştirilmiş Otoregresif Koşullu Değişen Varyans) modeli ise, ARCH'dan biraz daha farklıdır.…”

Section: Genelleştirilmiş Otoregresif Değişen Varyans (Garch) Modeliunclassified

Determination of the Financial Volatility Estimative Model in the Participation-30 Index

Bektaş

2022

Verimlilik Dergisi

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Finans alanında volatilite (oynaklık) yatırımcılar için giderek önemini artırmaya devam etmektedir. Finansal yatırımcılar portföylerini çeşitlendirmenin yanında risklerini de en aza düşürebilmek için İslami finans yatırım araçlarına doğru hızlı bir şekilde yönelmektedirler. Bu nedenle İslami finansın ve İslami finans yatırım araçlarının önemi gün geçtikçe artmaktadır. Bu çalışmanın amacı Türkiye'de İslami usullere göre işlem gören şirketlerin olduğu Katılım-30 Endeksinin finansal volatilitesini modelleyen en uygun yöntemi bulmaktır.

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Section: Genelleştirilmiş Otoregresif Değişen Varyans (Garch) Modeliunclassified

Determination of the Financial Volatility Estimative Model in the Participation-30 Index

Bektaş

2022

Verimlilik Dergisi

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“…The report claims that this condition is the outcome of the Finnish Stock Exchange's rapid growth after 1980. Kıran (2010) explores the data of the ISE 100 index using GARCH and TGARCH methods and concludes that the effect of trading volume on return volatility is significant but not positive. Boyacıo glu et al (2010) subject the monthly data of the ISE 100 Index to Granger and GARCH analysis and detect a negative relationship from volume to price.…”

Section: A Comparison Of International Marketmentioning

confidence: 99%

A comparison of international market indices for measuring market efficiency based on price-volume relationship

Çıralı

2022

JCMS

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PurposeThe main purpose of the research is to determine if the relationship between trading volume and price changes is connected to market effectiveness and to use the volume-price relationship to compare the efficiency levels of foreign markets. The degree of the relationship is determined in this study, and the efficiency levels of different countries' capital markets are compared.Design/methodology/approachIn this study, 1,024 observations are used as a data set, which includes daily closing prices and trading volume in the stock market indices of 25 countries between the dates of 01.12.2016 and 31.12.2020. In the first step of the analysis, descriptive statistics of price and volume series are examined. The stationarity of the series is then controlled using the ADF unit root test. Simple linear regression models with the dependent variable of trading volume are generated for all stock market indices after each series has reached stationarity, and the ARCH heteroscedasticity test is used to determine whether these models contain the ARCH effect. Because all models have the ARCH effect, autoregressive models are chosen, and EGARCH models are conducted for all indices to see whether there is an asymmetry in the price-volume relationship.FindingsThe study concludes that the stock market in the United States is the most effective, since it has the strongest relationship between trading volume and price changes. However, because of the financial distress caused by the COVID-19 pandemic, the relationship between price and trading volume is lower in Eurozone countries. The price-volume relationship could not be observed in some shallow markets. Furthermore, whereas the majority of countries have a negative relationship between price changes and transaction volume, China, the United Arab Emirates and Qatar have a positive relationship. When prices rise in these countries, investors buy with the sense of hope provided by the optimistic atmosphere, and when prices fall, they sell with the fear of losing money.Research limitations/implicationsThe study's most significant limitation is that it is difficult to ascertain a definitive conclusion about the subject under investigation. In reality, if the same research is done using data from different countries and time periods, the results are quite likely to vary.Practical implicationsAs a result of the study, investors can decide which market to enter by comparing and analyzing the price-volume relationship of several markets. According to the study's findings, investors are advised to examine the price-volume relationship in a market before beginning to trade in that market. In this way, investors can understand the market's efficiency and whether it is overpriced.Social implicationsThe relationship between price movements and trade volume gives crucial information about a capital market's internal structure. Some concerns can be answered by assessing this relationship, such as whether the market has a speculative pricing problem, how information flows to the market, and whether investment decisions are rational and homogenous. Empirical studies on modeling this relationship, on the other hand, have not reached a definite outcome. The main reason for this is that the price-to-volume relationship fluctuates depending on the market structure. The purpose of this study is to fill a gap in the literature by presenting the reasons why this critical issue in the literature cannot be answered, as well as empirical findings.Originality/valueThe significance and originality of this research are that it examines the price-volume relationship to evaluate the efficiency levels of various markets. This relationship is being investigated in a number of multinational studies. These researches, on the other hand, were conducted to see if there is a relationship between trading volume and market volatility, and if so, how that interaction is formed. The size of the price and volume relationship is emphasized in this study, unlike previous studies in the literature.

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“…Literatürde Türkiye hisse senedi piyasa volatilitesinin makroekonomik sebeplerini inceleyen az sayıda çalışma bulunmaktadır (örneğin, Kasman, 1997;Erdem, Arslan ve Erdem, 2005). Bu çalışmalarda ise hisse senedi piyasa volatilitesinin sebebi genellikle enflasyon (Saryal, 2007), döviz kuru (Tokat, 2013) ve işlem hacmi (Kıran, 2010) gibi tek bir faktörle açıklanmıştır.…”

Section: Introductionunclassified

Türkiye Hisse Senedi Piyasa Volatilitesinin Makroekonomik Temelleri

Taştan¹,

Güngör²

2019

berj

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Bu çalışmada, günlük frekanslı Türkiye hisse senedi piyasa volatilitesinin yavaş hareket eden uzun dönemli bileşeninin aylık makroekonomik göstergelerle ilişkisi analiz edilmiştir. Çalışmanın ilk aşamasında, BIST100 endeksinin uzun dönemli volatilitesi GARCH-MIDAS (Mixed Data Sampling) yöntemiyle tahmin edilmiştir. İkinci aşamada ise Ocak 2004-Aralık 2018 döneminde BIST100 endeksinin uzun dönemli oynaklığının, faiz oranı, Dolar/TL döviz kuru, enflasyon oranı, CDS risk primi, reel sektör güven endeksi ve S&P500 endeksinin volatilitesiyle ilişkisi otoregresif dağıtılmış gecikme (ARDL) modeli tahmin edilerek incelenmiştir. Analizin sonucunda, BIST100 endeksinin uzun dönemli volatilitesini etkileyen en önemli makroekonomik faktörün döviz kuru olduğu bulunmuştur. Bununla birlikte faiz oranı, CDS risk primi ve S&P500 volatilitesi ile BIST100 endeksinin uzun dönemli volatilitesi arasında pozitif yönlü bir ilişki olduğu görülmüştür. Reel sektör güven endeksindeki artışların ise BIST100 endeksinin uzun dönemli volatilitesini düşürdüğü sonucuna ulaşılmıştır.

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İstanbul Menkul Kıymetler Borsası nda İşlem Hacmi ve Getiri Volatilitesi

Cited by 10 publications

References 10 publications

Determination of the Financial Volatility Estimative Model in the Participation-30 Index

Determination of the Financial Volatility Estimative Model in the Participation-30 Index

A comparison of international market indices for measuring market efficiency based on price-volume relationship

Türkiye Hisse Senedi Piyasa Volatilitesinin Makroekonomik Temelleri

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