SavedAdd your journal

Journals / Business and Management Studies: An International Journal / 2019 / Cilt: 7 - Sayı: 5

AN INVESTIGATION ON THE SYSTEMATIC RISK OF ISLAMIC STOCK MARKETS UNDER MARKOV REGIME SWITCHING MODEL: EVIDENCE FROM PARTICIPATION 30 INDEX

İSLAMİ HİSSE SENEDİ PİYASALARININ SİSTEMATİK RİSKİNİN MARKOV REJİM DEĞİŞİM MODELİYLE İNCELENMESİ: KATILIM 30 ÖRNEĞİ

Fatih GÜÇLÜ
Pages
2910–2924
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

According to the results obtained from both models, the systematic risk of KATLM was estimated lower than BIST100.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The purpose of this study is to estimate the systematic risk of the Participation 30 Islamic Stock Index (KATLM) listed on the Borsa Istanbul for different volatility regimes with Markov Regime Change Model (MS-SVFM) and compare the riskiness of KATLM with the conventional index Borsa Istanbul 100 (BIST100).
📊
Key Findings & Data:According to the results obtained from both models, the systematic risk of KATLM was estimated lower than BIST100.
💡
Conclusion & Impact:The LR test results showed that the two-stage Markov Regime Switching model is superior to the linear model.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

The purpose of this study is to estimate the systematic risk of the Participation 30 Islamic Stock Index (KATLM) listed on the Borsa Istanbul for different volatility regimes with Markov Regime Change Model (MS-SVFM) and compare the riskiness of KATLM with the conventional index Borsa Istanbul 100 (BIST100). In the study, beta coefficients of KATLM index were estimated by using conventional linear SVFM and nonlinear MS-SVFM models. After that, for testing whether nonlinear model is superior to linear model, likelihood ratio test was used. According to the results obtained from both models, the systematic risk of KATLM was estimated lower than BIST100. MS-SVFM results suggested that, unlike the linear model, beta coefficients tend to change over time between low and high volatility regimes. The LR test results showed that the two-stage Markov Regime Switching model is superior to the linear model.

Özet

Bu çalışmanın amacı, Borsa İstanbul’da işlem gören İslami hisse senedi endeksi Katılım 30’un (KATLM) sistematik riskinin Markov Rejim Değişim Modeli (MS-SVFM) ile farklı oynaklık dönemleri için tahmin edilerek konvansiyonel hisse senedi endeksi Borsa İstanbul 100 Endeksine (BIST100) göre risk durumunun belirlenmesidir. Bu doğrultuda çalışmada, geleneksel doğrusal SVFM ve doğrusal olmayan MS-SVFM modelleri ile KATLM endeksine ait beta katsayıları tahmin edilmiştir. Daha sonra doğrusal model ile doğrusal olmayan model arasından en uygun modelin hangisi olduğuna, olabilirlik oranı testi (LR testi) sonuçlarına göre karar verilmiştir. Her iki modelden de elde edilen sonuçlara göre KATLM’nin sistematik riski BIST100’den düşük bulunmuştur. MS-SVFM sonuçları, doğrusal modelin aksine beta katsayılarının düşük ve yüksek oynaklık dönemleri arasında zamanla değişme eğiliminde olduğunu göstermiştir. LR testi sonuçları ise iki aşamalı Markov Rejim Değişim Modelinin veriyi doğrusal modelden daha iyi temsil ettiğini ortaya koymuştur.

🔖

Cite This Article

GÜÇLÜ, F. (2019). AN INVESTIGATION ON THE SYSTEMATIC RISK OF ISLAMIC STOCK MARKETS UNDER MARKOV REGIME SWITCHING MODEL: EVIDENCE FROM PARTICIPATION 30 INDEX. Business and Management Studies: An International Journal, Cilt: 7 - Sayı: 5, 2910–2924. https://ajindex.com/articles/351971

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (12)

  1. Abbes, M. B. (2012). Risk and Return of Islamic and Conventional Indices. International Journal of Euro- Mediterranean Studies, 5(1), 1–23.
  2. Abdymomunov, A. ve Morley, J. (2011). Time variation of CAPM betas across market volatility regimes. Applied Financial Economics, 21(19), 1463–1478. doi:10.1080/09603107.2011.577010 Ahmad, Z. ve Ibrahim, H. (2002). A Study of Performance of the KLSE Syariah Index. Malaysian Management Journal, 6(1–2), 25–34.
  3. Blume, M. E. (1971). On the Assessment of Risk. The Journal of Finance, 26(1), 1–10.
  4. Chen, C. W. S., Gerlach, R. H. ve Lin, A. M. H. (2011). Multi-regime nonlinear capital asset pricing models. Quantitative Finance, 11(9), 1421–1438. doi:10.1080/14697680902968013 Chen, S.-N. (1981).
  5. Beta Nonstationarity, Portfolio Residual Risk and Diversification. The Journal of Financial and Quantitative Analysis, 16(1), 95–111.
  6. Chen, S.-N. (1982). An Examination of Risk-Return Relationship in Bull and Bear Markets Using Time-Varying Betas. The Journal of Financial and Quantitative Analysis, 17(2), 265–286.
  7. Chen, S.-W. ve Huang, N.-C. (2007). Estimates of the ICAPM with regime-switching betas: evidence from four pacific rim economies. Applied Financial Economics, 17(4), 313–327. doi:10.1080/09603100600749188 Choudhry, T. (2005). September 11 and time-varying beta of United States companies. Applied Financial Economics, 15(17), 1227–1242. doi:10.1080/09603100500358742 Dickey, D. A. ve Fuller, W. A. (1979). Distribution of the Estimators for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association, 74(366), 427–431.
  8. Fabozzi, F. J. ve Francis, J. C. (1977). Stability Tests for Alphas and Betas Over Bull and Bear Market Conditions. The Journal of Finance, 32(4), 1093–1099.
  9. Fabozzi, F. J. ve Francis, J. C. (1978). Beta as a Random Coefficient. The Journal of Financial and Quantitative Analysis, 13(1), 101–116.
  10. Ferson, W. E. ve Harvey, C. R. (1991). The Variation of Economic Risk Premiums. Journal of Political Economy, 99(2), 385–415.
  11. Ferson, W. E. ve Korajczyk, R. A. (1995). Do Arbitrage Pricing Models Explain the Predictability of Stock Returns? The Journal of Business, 68(3), 309–349.
  12. Galagedera, D. U. A. ve Shami, R. (2004). Association between Markov regime-switching market volatility and beta risk: Evidence from Dow Jones industrial securities. SSRN Electronic Journal, March, 1–27.doi:10.2139/ssrn.578781 Gibbons, M. R. (1982). Multivariate Tests of Financial Models: A New Approach. Journal of F