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Risk İştahı Endeksinin Markov Rejim Modeli ile İncelenmesi: Türkiye Örneği

Year 2019, Volume: 19 Issue: 2, 265 - 275, 30.04.2019
https://doi.org/10.21121/eab.556341

Abstract

Finansal pazarlar içsel ve dışsal faktörlere bağlı
olarak dinamik şekilde hareket ederler. Yatırımcıların
risk iştahı da finansal pazarların hareketliliğinde
önemli bir etkendir. Risk iştahı endeksi Merkezi Kayıt
Kuruluşu tarafından yayınlanan bir veri olup pazar ve
yatırımcılar için pozisyon alma açısından kritik öneme
sahiptir. Bu çalışmada tüm yatırımcılara ait risk
iştahı endeksinin parametrik olarak rejimlere ayrılıp
ayrılmadığı incelenmeye çalışılmıştır. Bu bağlamda
risk iştahı endeksinin  2008 - 2016 dönemleri
arası haftalık verilerinden yararlanılarak Markov
Rejim Modeli ile bir dizi analiz gerçekleştirilmiştir.
Çalışmadan elde edilen sonuçlar risk iştahının yüksek
oynaklıklı ve düşük oynaklıklı rejimlere ayrılabildiğini
ortaya koymaktadır. Ayrıca ekonomik kriz, siyasi
istikrarsızlık ile dünyada ve Türkiye’de artan terör
olaylarının risk iştahının yüksek oynaklıklı dönemine
denk geldiği sonucuna da ulaşılmıştır

References

  • Ang, A. ve Beaert, G. (2002). International asset allocation with regime shifts. The Review of Financial Studies, 15(4), 1137–1187. Aydoğan, Y. (2013). Hisse senedi ve bono getirilerinin doğrusal olmayan modellemelerle analizi: Türkiye örneği.(Yayımlanmamış Doktora Tezi, Eğe Üniversitesi, Sosyal Bilimler Enstitüsü, İzmir.) Baba, N. ve Sakurai, Y. (2011). Predicting regime switches in the VIX index with macroeconomic variables. Applied Economics Letters, 18(15), 1415-1419. Bai, J. ve Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–68. Bai, J. ve Perron, P. (2003a). Computation and analysis of multiple structural change models. Journal of Applied Econometrics, 18(1), 1–22. Bai, J. ve Perron, P. (2003b). Critical values in multiple structural change tests. Econometrics Journal 6(1), 72–78. Bai,J.ve Perron, P. (2004). Multiple structural change models: A simulation study ,in D.Corbea, S.Durlauf, and B.E.Hansen, Econometric Essays. England: Cambridge University Press. Bildirici, M., Alp, E.A., Bozoklu, Ü. ve Ersin, Ö.Ö. (2010). İktisatta kullanılan doğrusal olmayan zaman serisi yöntemleri. İstanbul: Türkmen Kitabevi. Brooks, C. (2014). Introductory econometrics for finance. (Third Edition). New York: Cambridge University Press. Bry, G. ve Boschan, C. (1971). Standard business cycle analysis of economic time series. NBER, 71(1), 64-150. Campbell, J.Y., Lettau, M., Malkiel, B.G. ve Xu, Y. (2001). Have individual stocks become more volatile? An empirical exploration of idiosyncratic risk. The Journal of Finance, 56(1), 1–43. Caner, M. ve Hansen, B.E. (2001). Threshold autoregression with a unit root. Econometrica, 69(6), 1555-1596. Cecchetti, S. G., Lam, P. ve Mark, N.C. (1990). Evaluating empirical tests of asset pricing models: Alternative interpretations. American Economic Review, 80(2), 48-51. Cecchetti, S. G., Lam, P. ve Mark, N.C. (1993) The equity premium and the risk-free rate: Matching the moments. Journal of Monetary Economics, 31(1), 21-45. Chauvet, M. ve Potter, S. (2000). Coincident and leading indicators of the stock market. Journal of Empirical Finance, 7(1), 87-111. Chen, S. N. (1982). An examination of risk return relationship in bull and bear markets using time-varying betas. Journal of Financial & Quantita tive Analysis, 17(1), 265-286. Eğilmez, M. (2015). FED’in modası geçti. 28.06.2018. tarihinde http://www.mahfiegilmez.com/2015/12/fedin-modas-gecti.html Fabozzi, F.J. ve Francis, J.C. (1977). Stability tests for alphas and betas over bull and bear market conditions. Journal Of Finance, 32(4): 1093-1099. Franses P.H. ve Dijk, D. V. (2000). Nonlinear time series models in empirical finance. (First Edition). Newyork: Cambridge Universtiy Press. Franses P.H. ve Dijk, D. V. (2003). Nonlinear time series models in empirical finance. (Second Edition). Newyork: Cambridge Universtiy Press. Franses, P.H., Dijk, D.V. ve Opschoor, A. (2014). Time series models for business and economic forecasting. (Second Edition). Newyork, Cambridge University Press. Giot, P. (2003). The Asian financial crisis: The start of a regime switch in volatility. CORE Discussion Paper, 2003/78. Gonzalez, L., Powell, J.G., Shi, J. ve Wilson, A. (2005). Two centuries of bull and bear market cycles. International Review of Economics & Finance, 14(4), 469-486. Guidolin, M. ve Timmermann, A. (2005). Economic implications of bull and bear regimes in UK stock and bond returns. The Economic Journal, 115(500), 111–143. Guo, W. ve Wohar, M.E. (2006). Identifying regime changes in market volatility. The Journal of Financial Research, 29(1), 79–93. Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57(2), 357-384. Hamilton, J. D. (1994). Time series analysis. New Jersey: Princeton University Press. Hamilton, J.D. ve Lin, G. (1996). Stock market volatility and the business cycle. Journal of Applied Econometrics 11(5), 573-593. Kayhan, S., Bayat, T. ve Koçyiğit, A. (2013). Enflasyon hedeflemesi rejiminde öğrenme süreci ve asimetri: Markov switching yaklaşımı. Eskişehir Osmangazi Üniversitesi İİBF Dergisi 8(1), 191‐212. Kim, M. K. ve Zumwalt, J. K. (1979). An analysis of risk in bull and bear markets. Journal of Financial and Quantitative Analysis, 14, 1015-1025. Kim, C.J., Nelson, C.R. ve Startzb, R. (1998). Testing for mean reversion in heteroskedastic data based on gibbs-sampling-augmented randomization. Journal of Empirical Finance 5(2): 131-154. Koy, A. (2016). Borsa İstanbul’un doğrusal olmayan dinamiklerinin markov rejim değişim modelleriyle açıklanması. Gaziantep Üniversitesi, 1. Lisansüstü İşletme Öğrencileri Sempozyomu, 176-180. Koy, A., Çetin, G. ve Ersan, İ. (2016). Uluslararası kıymetli metal piyasalarının rejim dinamikleri. Maliye Finans Yazılar, 107, 25-40. Koy, A. (2017). Vadeli işlem piyasaları: BİST30 endeks vadeli işlem sözleşmesinin markov rejim değişim modelleri ile analizi. İstanbul:Derin Yayınları. Krolzig, H. M. (1997). Markov switching vector autoregressions: Modeling, statistical inference, and application to business cycle analysis. Berlin: Springer Verlag. Krolzig, H. M. (1998). Econometric modeling of markov-switching vector autoregressions using MSVAR for Ox. London: Oxford University Manuscript. Lee, J. ve Strazicich, M.C. (2003). Minimum lagrange multiplier unit root test with structural breaks. Review of Economics and Statistics, 85(4), 1082-1089. Lindahl-Stevens, M. (1980). Redefining bull and bear markets. Financial Analysts Journal, 36(6), 76-77. Lunde, A. ve Timmermann, A. (2004). Duration dependence in stock prices: An analysis of bull and bear markets. Journal of Business & Economic Statistics, 22(3), 253-273. Maheu J. ve McCurdy T. H. (2000). Identifying bull and bear markets in stock returns. Journal of Business and Economic Statistics 18(1), 100-112. Mayfield, E.S. (2004). Estimating the market risk Premium. Journal of Financial Economics 73(3), 465-496. Pagan, A.R. ve Sossounov, K.A. (2000). A Simple framework for analysing bull and bear markets. Journal of Applied Econometrics, 18(1), 23-46. Papanicolaou, A. ve Sircar, R. (2014). A regime-switching heston model for VIX and S&P 500 implied volatilities. Quantitative Finance, 14(10), 1811-1827. Perez-Quiros, G. ve Timmermann, A. (2000). Firm size and cyclical variations in stock returns. The Journal of Finance, 55(3), 1229–1262. Ramo, J.M. (2012). Volatility regimes for the VIX index. Revista de Economía Aplicad, 20, 114-134 Saraç, T.B., İskenderoğlu, Ö. ve Akdağ, S. (2016). Yerli ve yabancı yatırımcılara ait risk iştahlarının incelenmesi: Türkiye örneği. Sosyoekonomi, 24(30), 29-44. Song, W., Ryu, D. ve Webb, R. I. (2016). Overseas market shocks and VKOSPI dynamics: A markov-switching approach. Finance Research Letters, 16, 275-282. Sperandeo, V. (1990). Principles of professional speculation, Newyork: Wiley Corp. Schwert, G.W. (1989). Why does stock market volatility change over time? The Journal of Finance, 44(5), 1115–1153. Schwert, G.W. (1998). Stock market volatility: Ten years after the crash. Brookings-Wharton Papers on Financial Services, 6381, 65-99. Tong, H. (1978). On a Threshold model in pattern recognition and signal processing. Amsterdam: Sijhoff & Noordhoff. Tong, H. (1983). Threshold models in non-linear time series analysis. Lecture notes in statistics. New York: Springer-Verlag. Tong, H. ve Lim, K.S. (1980). Threshold autoregression, limit cycles and cyclical data. Journal of the Royal Statistical Society. Series B (Methodological), 42(3), 245-292. Turner, C.M., Startz, R. ve Nelson, C.R. (1989). A markov model of heteroskedasticity, risk, and learning in the stock market. Journal of Financial Economics, 25(1), 3-22.

Investigating the Risk Appetite Index with Markov Regime Model: Case of Turkey

Year 2019, Volume: 19 Issue: 2, 265 - 275, 30.04.2019
https://doi.org/10.21121/eab.556341

Abstract

Financial markets changes dynamically along with
many internal and external factors. Investors’ risk
appetite is one of the key elements of volatility in
financial markets. Risk appetite indexes are data
published by the Central Securities Depository
Institution having importance in terms of
positioning besides determination for markets and
investors. In this study, it is examined whether or not
the calculated risk appetite index of all investors in
Turkey is separated into regimes parametrically. On
this respect, an analysis of Markov Regime Model has
been employed on risk appetite index of all investors
utilizing the weekly frequency data spanning from
2008 to 2016. The results from the study reveals
that the risk appetite can be divided into high
volatility and low volatility regimes parametrically.
In addition, the economic crisis, political instability,
increasing terror attacks in the World and Turkey are
found to occur during the period of high volatility
regime of risk apetite

References

  • Ang, A. ve Beaert, G. (2002). International asset allocation with regime shifts. The Review of Financial Studies, 15(4), 1137–1187. Aydoğan, Y. (2013). Hisse senedi ve bono getirilerinin doğrusal olmayan modellemelerle analizi: Türkiye örneği.(Yayımlanmamış Doktora Tezi, Eğe Üniversitesi, Sosyal Bilimler Enstitüsü, İzmir.) Baba, N. ve Sakurai, Y. (2011). Predicting regime switches in the VIX index with macroeconomic variables. Applied Economics Letters, 18(15), 1415-1419. Bai, J. ve Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–68. Bai, J. ve Perron, P. (2003a). Computation and analysis of multiple structural change models. Journal of Applied Econometrics, 18(1), 1–22. Bai, J. ve Perron, P. (2003b). Critical values in multiple structural change tests. Econometrics Journal 6(1), 72–78. Bai,J.ve Perron, P. (2004). Multiple structural change models: A simulation study ,in D.Corbea, S.Durlauf, and B.E.Hansen, Econometric Essays. England: Cambridge University Press. Bildirici, M., Alp, E.A., Bozoklu, Ü. ve Ersin, Ö.Ö. (2010). İktisatta kullanılan doğrusal olmayan zaman serisi yöntemleri. İstanbul: Türkmen Kitabevi. Brooks, C. (2014). Introductory econometrics for finance. (Third Edition). New York: Cambridge University Press. Bry, G. ve Boschan, C. (1971). Standard business cycle analysis of economic time series. NBER, 71(1), 64-150. Campbell, J.Y., Lettau, M., Malkiel, B.G. ve Xu, Y. (2001). Have individual stocks become more volatile? An empirical exploration of idiosyncratic risk. The Journal of Finance, 56(1), 1–43. Caner, M. ve Hansen, B.E. (2001). Threshold autoregression with a unit root. Econometrica, 69(6), 1555-1596. Cecchetti, S. G., Lam, P. ve Mark, N.C. (1990). Evaluating empirical tests of asset pricing models: Alternative interpretations. American Economic Review, 80(2), 48-51. Cecchetti, S. G., Lam, P. ve Mark, N.C. (1993) The equity premium and the risk-free rate: Matching the moments. Journal of Monetary Economics, 31(1), 21-45. Chauvet, M. ve Potter, S. (2000). Coincident and leading indicators of the stock market. Journal of Empirical Finance, 7(1), 87-111. Chen, S. N. (1982). An examination of risk return relationship in bull and bear markets using time-varying betas. Journal of Financial & Quantita tive Analysis, 17(1), 265-286. Eğilmez, M. (2015). FED’in modası geçti. 28.06.2018. tarihinde http://www.mahfiegilmez.com/2015/12/fedin-modas-gecti.html Fabozzi, F.J. ve Francis, J.C. (1977). Stability tests for alphas and betas over bull and bear market conditions. Journal Of Finance, 32(4): 1093-1099. Franses P.H. ve Dijk, D. V. (2000). Nonlinear time series models in empirical finance. (First Edition). Newyork: Cambridge Universtiy Press. Franses P.H. ve Dijk, D. V. (2003). Nonlinear time series models in empirical finance. (Second Edition). Newyork: Cambridge Universtiy Press. Franses, P.H., Dijk, D.V. ve Opschoor, A. (2014). Time series models for business and economic forecasting. (Second Edition). Newyork, Cambridge University Press. Giot, P. (2003). The Asian financial crisis: The start of a regime switch in volatility. CORE Discussion Paper, 2003/78. Gonzalez, L., Powell, J.G., Shi, J. ve Wilson, A. (2005). Two centuries of bull and bear market cycles. International Review of Economics & Finance, 14(4), 469-486. Guidolin, M. ve Timmermann, A. (2005). Economic implications of bull and bear regimes in UK stock and bond returns. The Economic Journal, 115(500), 111–143. Guo, W. ve Wohar, M.E. (2006). Identifying regime changes in market volatility. The Journal of Financial Research, 29(1), 79–93. Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57(2), 357-384. Hamilton, J. D. (1994). Time series analysis. New Jersey: Princeton University Press. Hamilton, J.D. ve Lin, G. (1996). Stock market volatility and the business cycle. Journal of Applied Econometrics 11(5), 573-593. Kayhan, S., Bayat, T. ve Koçyiğit, A. (2013). Enflasyon hedeflemesi rejiminde öğrenme süreci ve asimetri: Markov switching yaklaşımı. Eskişehir Osmangazi Üniversitesi İİBF Dergisi 8(1), 191‐212. Kim, M. K. ve Zumwalt, J. K. (1979). An analysis of risk in bull and bear markets. Journal of Financial and Quantitative Analysis, 14, 1015-1025. Kim, C.J., Nelson, C.R. ve Startzb, R. (1998). Testing for mean reversion in heteroskedastic data based on gibbs-sampling-augmented randomization. Journal of Empirical Finance 5(2): 131-154. Koy, A. (2016). Borsa İstanbul’un doğrusal olmayan dinamiklerinin markov rejim değişim modelleriyle açıklanması. Gaziantep Üniversitesi, 1. Lisansüstü İşletme Öğrencileri Sempozyomu, 176-180. Koy, A., Çetin, G. ve Ersan, İ. (2016). Uluslararası kıymetli metal piyasalarının rejim dinamikleri. Maliye Finans Yazılar, 107, 25-40. Koy, A. (2017). Vadeli işlem piyasaları: BİST30 endeks vadeli işlem sözleşmesinin markov rejim değişim modelleri ile analizi. İstanbul:Derin Yayınları. Krolzig, H. M. (1997). Markov switching vector autoregressions: Modeling, statistical inference, and application to business cycle analysis. Berlin: Springer Verlag. Krolzig, H. M. (1998). Econometric modeling of markov-switching vector autoregressions using MSVAR for Ox. London: Oxford University Manuscript. Lee, J. ve Strazicich, M.C. (2003). Minimum lagrange multiplier unit root test with structural breaks. Review of Economics and Statistics, 85(4), 1082-1089. Lindahl-Stevens, M. (1980). Redefining bull and bear markets. Financial Analysts Journal, 36(6), 76-77. Lunde, A. ve Timmermann, A. (2004). Duration dependence in stock prices: An analysis of bull and bear markets. Journal of Business & Economic Statistics, 22(3), 253-273. Maheu J. ve McCurdy T. H. (2000). Identifying bull and bear markets in stock returns. Journal of Business and Economic Statistics 18(1), 100-112. Mayfield, E.S. (2004). Estimating the market risk Premium. Journal of Financial Economics 73(3), 465-496. Pagan, A.R. ve Sossounov, K.A. (2000). A Simple framework for analysing bull and bear markets. Journal of Applied Econometrics, 18(1), 23-46. Papanicolaou, A. ve Sircar, R. (2014). A regime-switching heston model for VIX and S&P 500 implied volatilities. Quantitative Finance, 14(10), 1811-1827. Perez-Quiros, G. ve Timmermann, A. (2000). Firm size and cyclical variations in stock returns. The Journal of Finance, 55(3), 1229–1262. Ramo, J.M. (2012). Volatility regimes for the VIX index. Revista de Economía Aplicad, 20, 114-134 Saraç, T.B., İskenderoğlu, Ö. ve Akdağ, S. (2016). Yerli ve yabancı yatırımcılara ait risk iştahlarının incelenmesi: Türkiye örneği. Sosyoekonomi, 24(30), 29-44. Song, W., Ryu, D. ve Webb, R. I. (2016). Overseas market shocks and VKOSPI dynamics: A markov-switching approach. Finance Research Letters, 16, 275-282. Sperandeo, V. (1990). Principles of professional speculation, Newyork: Wiley Corp. Schwert, G.W. (1989). Why does stock market volatility change over time? The Journal of Finance, 44(5), 1115–1153. Schwert, G.W. (1998). Stock market volatility: Ten years after the crash. Brookings-Wharton Papers on Financial Services, 6381, 65-99. Tong, H. (1978). On a Threshold model in pattern recognition and signal processing. Amsterdam: Sijhoff & Noordhoff. Tong, H. (1983). Threshold models in non-linear time series analysis. Lecture notes in statistics. New York: Springer-Verlag. Tong, H. ve Lim, K.S. (1980). Threshold autoregression, limit cycles and cyclical data. Journal of the Royal Statistical Society. Series B (Methodological), 42(3), 245-292. Turner, C.M., Startz, R. ve Nelson, C.R. (1989). A markov model of heteroskedasticity, risk, and learning in the stock market. Journal of Financial Economics, 25(1), 3-22.
There are 1 citations in total.

Details

Primary Language Turkish
Journal Section Research Article
Authors

Saffet Akdağ 0000-0001-9576-6786

Ömer İskenderoğlu 0000-0002-3407-1259

Publication Date April 30, 2019
Acceptance Date March 14, 2019
Published in Issue Year 2019 Volume: 19 Issue: 2

Cite

APA Akdağ, S., & İskenderoğlu, Ö. (2019). Risk İştahı Endeksinin Markov Rejim Modeli ile İncelenmesi: Türkiye Örneği. Ege Academic Review, 19(2), 265-275. https://doi.org/10.21121/eab.556341
AMA Akdağ S, İskenderoğlu Ö. Risk İştahı Endeksinin Markov Rejim Modeli ile İncelenmesi: Türkiye Örneği. ear. April 2019;19(2):265-275. doi:10.21121/eab.556341
Chicago Akdağ, Saffet, and Ömer İskenderoğlu. “Risk İştahı Endeksinin Markov Rejim Modeli Ile İncelenmesi: Türkiye Örneği”. Ege Academic Review 19, no. 2 (April 2019): 265-75. https://doi.org/10.21121/eab.556341.
EndNote Akdağ S, İskenderoğlu Ö (April 1, 2019) Risk İştahı Endeksinin Markov Rejim Modeli ile İncelenmesi: Türkiye Örneği. Ege Academic Review 19 2 265–275.
IEEE S. Akdağ and Ö. İskenderoğlu, “Risk İştahı Endeksinin Markov Rejim Modeli ile İncelenmesi: Türkiye Örneği”, ear, vol. 19, no. 2, pp. 265–275, 2019, doi: 10.21121/eab.556341.
ISNAD Akdağ, Saffet - İskenderoğlu, Ömer. “Risk İştahı Endeksinin Markov Rejim Modeli Ile İncelenmesi: Türkiye Örneği”. Ege Academic Review 19/2 (April 2019), 265-275. https://doi.org/10.21121/eab.556341.
JAMA Akdağ S, İskenderoğlu Ö. Risk İştahı Endeksinin Markov Rejim Modeli ile İncelenmesi: Türkiye Örneği. ear. 2019;19:265–275.
MLA Akdağ, Saffet and Ömer İskenderoğlu. “Risk İştahı Endeksinin Markov Rejim Modeli Ile İncelenmesi: Türkiye Örneği”. Ege Academic Review, vol. 19, no. 2, 2019, pp. 265-7, doi:10.21121/eab.556341.
Vancouver Akdağ S, İskenderoğlu Ö. Risk İştahı Endeksinin Markov Rejim Modeli ile İncelenmesi: Türkiye Örneği. ear. 2019;19(2):265-7.