Araştırma Makalesi
BibTex RIS Kaynak Göster

Financial Decisions and Value-at-Risk: Empirical Evidence from BIST 100 Companies

Yıl 2024, Cilt: 11 Sayı: 1, 366 - 392, 31.03.2024
https://doi.org/10.30798/makuiibf.1406660

Öz

This study examines the relationship between financial decisions and the value-at-risk (VaR) of companies operating in the Turkish stock market. The study contains semi-annual data of non-financial BIST 100 Index companies spanning from January 2010 to June 2023. Companies’ VaR are calculated using Monte-Carlo simulation, bootstrap, delta-normal, and historical simulation methods and included in separate econometric models as dependent variables. Financial decisions are represented through financial ratios in line with the basic principles of corporate finance and included as explanatory variables in econometric models. The study employs a five-stage panel data methodology.
Findings reveal that the impact of financial decisions regarding working capital management, capital structure, dividend pay-out, and growth policies on companies’ VaR differ according to the VaR calculation method. Notably, findings show that financial decisions explain the changes in VaR calculated by Bootstrap method with the highest success rate, aligning with existing finance literature. Prudent financing policies and flexible investment strategies in working capital management, enhanced profitability and financial performance, and sales growth exhibit dampening effects on VaR. Conversely, heightened leverage and long-term borrowings, decisions to pay-out dividends, and growth in foreign investments have increasing effects on VaR. Furthermore, the study identifies the Covid-19 pandemic as exerting a negative influence on VaR.

Kaynakça

  • Akan, N. B., Oktay, L. A., & Tüzün, Y. (2003). Parametrik riske maruz değer yöntemi Türkiye uygulaması. Bankacılar Dergisi, 45, 29-39. https://www.tbb.org.tr/tr/bankacilik/arastirma-ve-yayinlar/bankacilar-dergisi/43?year=2003.
  • Aktaş, M. (2008). Türkiye piyasalarında parametrik riske maruz değer modelinin taşıdığı riskler. Afyon Kocatepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 10(1), 243-256. https://dergipark.org.tr/en/pub/akuiibfd/issue/1629/20431.
  • Avşarlıgil, N., Demir, Y., & Doğru, E. (2015). Riske maruz değer ölçüm yöntemleri aracılığıyla BIST’te işlem gören spor kulüpleri üzerine bir uygulama. Journal of Social Sciences Eskisehir Osmangazi University, 16(1), 81-107. https://doi.org/10.17494/ogusbd.85249.
  • Baltagi, B. H. (2014). Econometric analysis of panel data. (5th Edition), Jhon Wiley&Sons Ltd.
  • Baltagi, B. & Li, Q. (1991). A joint test for serial correlation and random ındividual effects. Statistics and Probability Letters, 11, 277-280. https://doi.org/10.1016/0167-7152(91)90156-L.
  • Bams, D., Blanchard, G., & Lehnert, T. (2017). Volatility measures and Value-at-Risk. International Journal of Forecasting, 33(4), 848-863. https://doi.org/10.1016/j.ijforecast.2017.04.004.
  • Basak, S., & Shapiro, A. (2001). Value-at-risk-based risk management: Optimal policies and asset prices. The Review of Financial Studies, 14(2), 371-405. https://doi.org/10.1093/rfs/14.2.371.
  • Beck, N. & Katz, J. N. (1995). What to do (and not to do) with time-series cross-section data. American Political Science Review, 89(3), 634-647. https://doi.org/10.2307/2082979.
  • Berkowitz, J., & O'Brien, J. (2002). How accurate are value‐at‐risk models at commercial banks?. The Journal of Finance, 57(3), 1093-1111. https://doi.org/10.1111/1540-6261.00455.
  • Born, B. & Breitung, J. (2016). Testing for serial correlation in fixed-effects panel data models. Econometric Reviews, 35(7), 1290-1316. https://doi.org/10.1080/07474938.2014.976524.
  • Bostancı, A., & Korkmaz, T. (2014). Comparison of value at risk calculation models in terms of banks’ capital adequacy ratio. Business and Economics Research Journal, 5(3), 15-41. https://www.berjournal.com/wp-content/plugins/downloads-manager/upload/BERJ5(3)14Article2pp.15-41.pdf.
  • Boyle, P., Broadie, M., & Glasserman, P. (1997). Monte Carlo methods for security pricing. Journal of Economic Dynamics and Control, 21(8-9), 1267-1321. https://doi.org/10.1016/S0165-1889(97)00028-6.
  • Bozkuş, S. (2005). Risk ölçümünde alternatif yaklaşımlar: Riske maruz değer (VaR) ve beklenen kayıp (ES) uygulamaları. Dokuz Eylül Üniversitesi İktisadi İdari Bilimler Fakültesi Dergisi, 20(2), 27-45. https://dergipark.org.tr/en/pub/deuiibfd/issue/22753/242876.
  • Brandolini, D., & Colucci, S. (2012). Backtesting value-at-risk: A comparison between filtered bootstrap and historical simulation. Journal of Risk Model Validation, 6(4), 3-16. Available at SSRN: https://ssrn.com/abstract=1965377 or http://dx.doi.org/10.2139/ssrn.1965377.
  • Breusch, T. S. & Pagan, A. R. (1979). A simple test for heteroskedasticity and random coefficient variation. Econometrica, 47(5), 1287-1294. https://doi.org/10.2307/1911963.
  • Breusch, T. S. & Pagan, A. R. (1980). The lagrange multiplier test and its applications to model specification in econometrics. Review of Economic Studies, 47(1), 239-253. https://doi.org/10.2307/2297111.
  • Butler, C. (1999). Mastering Value at Risk: A step-by-step guide to understanding and applying VAR. Pitman Publishing.
  • Cabedo, J. D., & Moya, I. (2003). Estimating oil price ‘value at risk’ using the historical simulation approach. Energy Economics, 25(3), 239-253. https://doi.org/10.1016/S0140-9883(02)00111-1.
  • Campbell, R., Huisman, R., & Koedijk, K. (2001). Optimal portfolio selection in a Value-at-Risk framework. Journal of Banking & Finance, 25, 1789-1804. https://doi.org/10.1016/S0378-4266(00)00160-6.
  • Chipalkatti, N., & Datar, V. (2006). The relevance of value‐at‐risk disclosures: Evidence from the LTCM crisis. Journal of Financial Regulation and Compliance, 14(2), 174-184. https://doi.org/10.1108/13581980610659486.
  • Demireli, E., & Taner, B. (2009). Risk yönetiminde riske maruz değer yöntemleri ve bir uygulama. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 14(3), 127-148. https://dergipark.org.tr/en/pub/sduiibfd/issue/20829/223082.
  • Efron, B. (1979). Bootstrap methods: Another look at the Jackknife. The Annals of Statistics, 7, 1–26. https://link.springer.com/chapter/10.1007/978-1-4612-4380-9_41.
  • Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. Chapman & Hall.
  • Fıkırkoca, M. (2003). Bütünsel risk yönetimi. Pozitif Matbaacılık.
  • Gallagher, R. B. (1956). Risk management: New phase of cost control. Harvard Business Review, 34(5), 75-86. https://www.econbiz.de/Record/risk-management-new-phase-of-cost-control-gallagher-russell/10002178905.
  • Giot, P., & Laurent, S. (2003). Value‐at‐risk for long and short trading positions. Journal of Applied Econometrics, 18(6), 641-663. https://doi.org/10.1002/jae.710.
  • Giot, P., & Laurent, S. (2004). Modelling daily Value-at-Risk using realized volatility and ARCH type models. Journal of Empirical Finance, 11(3), 379-398. https://doi.org/10.1016/j.jempfin.2003.04.003.
  • Glasserman, P., Heidelberger, P., & Shahabuddin, P. (2002). Portfolio value‐at‐risk with heavy‐tailed risk factors. Mathematical Finance, 12(3), 239-269. https://doi.org/10.1111/1467-9965.00141.
  • Gökgöz, E. (2006). Riske maruz değer (VaR) ve portföy optimizasyonu. Sermaye Piyasası Kurulu Yayınları.
  • Gürsakal, S. (2007). İMKB 30 Endeksi Getiri Serisinin Riske Maruz Değerlerinin Tarihi Simülasyon ve Varyans-Kovaryans Yöntemleri ile Hesaplanması. [Conference presentation]. 8. Türkiye Ekonometri ve İstatistik Kongresi, 24-25 Mayıs 2007, İnönü University, Malatya, Turkey.
  • Harmantzis, F. C., Miao, L., & Chien, Y. (2006). Empirical study of value‐at‐risk and expected shortfall models with heavy tails. The Journal of Risk Finance, 7(2), 117-135. https://doi.org/10.1108/15265940610648571.
  • Hendrics D. (1996). Evaluation of Value at Risk models using historical data. Federal Reserve Bank of New York Economy Policy Review, 2(4), 39-70. Available at SSRN: https://ssrn.com/abstract=1028807 or http://dx.doi.org/10.2139/ssrn.1028807.
  • Honda, Y. (1985). Testing the error components model with non-normal disturbances. Review of Economic Studies, 52, 681-690. https://doi.org/10.2307/2297739.
  • Işıldak, M. S. (2021). Asimetrik Garch modellerle riske maruz değer (RMD) analizi: Altın, Bist 100 Endeksi ve Dolar’dan oluşan portföy üzerinde bir uygulama. Uluslararası Sosyal ve Eğitim Bilimleri Dergisi, 16, 41-67. https://doi.org/10.20860/ijoses.977206.
  • Jackson P., Maude, D. J., & Perraudin, W. (1998). Bank capital and Value at Risk. Bank of England Quarterly Bulletin, Spring, 73-89. Available at SSRN: https://ssrn.com/abstract=87288 or http://dx.doi.org/10.2139/ssrn.87288.
  • Jorion, P. (1997). Value at Risk: The new benchmark for controlling market risk. (5th edition). McGraw-Hill Inc: Chicago.
  • Jorion, P. (2000). Value-at-risk: The new benchmark for managing financial risk. (2nd Edition), McGraw-Hill.
  • Kavrar, Ö., & Yılmaz, B. (2019). Riske maruz değer yöntemiyle portföy riskinin belirlenmesi. Öneri Dergisi, 14(52), 486-508. https://doi.org/10.14783/maruoneri.595104.
  • Kayahan, C., & Topal, Y. (2009). Tarihsel riske maruz değer (RMD) finansal riskleri açıklamada yeterli midir?. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 14(1), 179-198. https://dergipark.org.tr/en/pub/sduiibfd/issue/20831/223143.
  • Korkmaz, T., & Bostancı, A. (2011). The comparison of volatility forecasting models in VaR calculations and backtesting according to Basel II: An application on ISE 100 Index. Business and Economics Research Journal, 2(3),1-17.
  • Korkmaz, T. & Kuzay, S. (2022). Uluslararası çeşitlendirilmiş portföylerde riske maruz değer (RMD) ölçümü. In S. Yaman, & T. Nur (Eds.), Risk yönetimi: Teori ve uygulamalar (pp. 25-50). Gazi Kitabevi.
  • Korkmaz, T., & Pekkaya, M. (2021). Excel uygulamalı finans matematiği. (4th Edition). Ekin Yayınevi.
  • Kuester, K., Mittnik, S., & Paolella, M. S. (2006). Value-at-risk prediction: A comparison of alternative strategies. Journal of Financial Econometrics, 4(1), 53-89. https://doi.org/10.1093/jjfinec/nbj002.
  • Laporta, A. G., Merlo, L., & Petrella, L. (2018). Selection of value at risk models for energy commodities. Energy Economics, 74, 628-643. https://doi.org/10.1016/j.eneco.2018.07.009.
  • Levin, A., Lin, C. F. & Chu, C. S. J. (2002). Unit root tests in panel data: Asymptotic and finite-sample properties. Journal of Econometrics, 108, 1-24. https://doi.org/10.1016/S0304-4076(01)00098-7.
  • Likitratcharoen, D., Chudasring, P., Pinmanee, C., & Wiwattanalamphong, K. (2023). The efficiency of Value-at-Risk models during extreme market stress in cryptocurrencies. Sustainability, 15(5), 1-21. https://doi.org/10.3390/su15054395.
  • Linsmeier, T. J., & Pearson, N. D. (1996). Risk Measurement: An Introduction to Value at Risk. ACE Reports: University of Illinois at Urbana-Champaign. (No. 1629-2016-134959). https://www.exinfm.com/training/pdfiles/valueatrisk.pdf.
  • Linsmeier, T. J., & Pearson, N. D. (2000). Value at risk. Financial Analysts Journal, 56(2), 47-67. https://doi.org/10.2469/faj.v56.n2.2343.
  • Lin, S-K., Wang, R-H., & Fuh, C-D. (2006). Risk management for linear and non-linear assets: A bootstrap method with ımportance resampling to evaluate Value-at-Risk. Asia-Pacific Financial Markets, 13(3), 261–295. https://doi.org/10.1007/s10690-007-9042-0.
  • Liu, W., Semeyutin, A., Lau, C. K. M., & Gozgor, G. (2020). Forecasting Value-at-Risk of cryptocurrencies with RiskMetrics type models. Research in International Business and Finance, 54, 1-14. https://doi.org/10.1016/j.ribaf.2020.101259.
  • Markowitz, H. (1952). Portfolio selection. The Journal of Finance. 7(1), pp. 77-91. https://doi.org/10.2307/2975974.
  • Mentel, G. (2013). Parametric or non-parametric estimation of value-at-risk. International Journal of Business and Management, 8(11), 103-112. https://doi.org/10.5539/ijbm.v8n11p103.
  • Oppong, S. O., Asamoah, D., & Oppong, E. O. (2016, May). Value at risk: Historical simulation or Monte Carlo simulation. [Conference presentation]. International Conference on Management, Communication and Technology (ICMCT), 4(1), 45-51.
  • Özden, Ü. H. (2007). Riske maruz değer (RMD) hesaplama yöntemleri: İMKB üzerine uygulama. Öneri Dergisi, 7(28), 279-285. https://doi.org/10.14783/maruoneri.684413.
  • Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels. Cambridge Working Papers in Economics, 435, 1-39. Available at SSRN: https://ssrn.com/abstract=572504 or http://dx.doi.org/10.2139/ssrn.572504.
  • Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross section dependence. Journal of Applied Econometrics, 22, 265–312. https://doi.org/10.1002/jae.951.
  • Pesaran, M. H. & Yamagata, T. (2008). Testing slope homogeneity in large panels. Journal of Econometrics, 142, 50–93. https://doi.org/10.1016/j.jeconom.2007.05.010.
  • Pesaran, M. H., Ullah, A., & Yamagata, T. (2008). A bias adjusted LM test of error cross-section independence. Econometrics Journal, 11, 105–127. https://doi.org/10.1111/j.1368-423X.2007.00227.x.
  • So, M. K. P., & Yu, P. L. H. (2006). Empirical analysis of GARCH models in value at risk estimation. Journal of International Financial Markets, Institutions and Money, 16(2), 180-197. https://doi.org/10.1016/j.intfin.2005.02.001.
  • Taş, O., & İltüzer, Z. (2016). Monte Carlo simulasyon yöntemi ile riske maruz değerin İMKB30 Endeksi ve DİBS portföyü üzerinde bir uygulaması. Dokuz Eylül Üniversitesi İktisadi İdari Bilimler Fakültesi Dergisi, 23(1), 67-87. https://dergipark.org.tr/en/pub/deuiibfd/issue/22743/242755.
  • Topaloğlu, E. E. & Kurt Cihangir, Ç. (2022). Riske maruz değer – getiri ilişkisi: BIST banka portföyü üzerine ekonometrik bir araştirma. In S. Yaman, & T. Nur (Eds.), Risk yönetimi: Teori ve uygulamalar (pp. 51-88). Gazi Kitabevi.
  • Trucíos, C., & Taylor, J. W. (2023). A comparison of methods for forecasting value at risk and expected shortfall of cryptocurrencies. Journal of Forecasting, 42(4), 989-1007. https://doi.org/10.1002/for.2929.
  • Türker, H. (2009). Riske maruz değer (Value at Risk) ve stres testi: Global finansal kriz sonrası etkinliklerinin değerlendirilmesi. SPK Araştırma Raporu.
  • Türkyılmaz, S. (2023). Uzun hafızalı asimetrik oynaklık modelleri ile riske maruz değer (VaR) tahmini: Covid-19 dönemi altın piyasası. Karamanoğlu Mehmetbey Üniversitesi Sosyal ve Ekonomik Araştırmalar Dergisi, 25(44), 66-86. https://dergipark.org.tr/en/pub/kmusekad/issue/78068/1170112. Ural, M., Demireli, E. & Aydın, Ü. (2022). Finansal yatırımlarda riske maruz değer analizi (Value at Risk). Seçkin Yayınevi. Vlaar, P. J. (2000). Value at risk models for Dutch bond portfolios. Journal of Banking & Finance, 24(7), 1131-1154. https://doi.org/10.1016/S0378-4266(99)00068-0.
Yıl 2024, Cilt: 11 Sayı: 1, 366 - 392, 31.03.2024
https://doi.org/10.30798/makuiibf.1406660

Öz

Kaynakça

  • Akan, N. B., Oktay, L. A., & Tüzün, Y. (2003). Parametrik riske maruz değer yöntemi Türkiye uygulaması. Bankacılar Dergisi, 45, 29-39. https://www.tbb.org.tr/tr/bankacilik/arastirma-ve-yayinlar/bankacilar-dergisi/43?year=2003.
  • Aktaş, M. (2008). Türkiye piyasalarında parametrik riske maruz değer modelinin taşıdığı riskler. Afyon Kocatepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 10(1), 243-256. https://dergipark.org.tr/en/pub/akuiibfd/issue/1629/20431.
  • Avşarlıgil, N., Demir, Y., & Doğru, E. (2015). Riske maruz değer ölçüm yöntemleri aracılığıyla BIST’te işlem gören spor kulüpleri üzerine bir uygulama. Journal of Social Sciences Eskisehir Osmangazi University, 16(1), 81-107. https://doi.org/10.17494/ogusbd.85249.
  • Baltagi, B. H. (2014). Econometric analysis of panel data. (5th Edition), Jhon Wiley&Sons Ltd.
  • Baltagi, B. & Li, Q. (1991). A joint test for serial correlation and random ındividual effects. Statistics and Probability Letters, 11, 277-280. https://doi.org/10.1016/0167-7152(91)90156-L.
  • Bams, D., Blanchard, G., & Lehnert, T. (2017). Volatility measures and Value-at-Risk. International Journal of Forecasting, 33(4), 848-863. https://doi.org/10.1016/j.ijforecast.2017.04.004.
  • Basak, S., & Shapiro, A. (2001). Value-at-risk-based risk management: Optimal policies and asset prices. The Review of Financial Studies, 14(2), 371-405. https://doi.org/10.1093/rfs/14.2.371.
  • Beck, N. & Katz, J. N. (1995). What to do (and not to do) with time-series cross-section data. American Political Science Review, 89(3), 634-647. https://doi.org/10.2307/2082979.
  • Berkowitz, J., & O'Brien, J. (2002). How accurate are value‐at‐risk models at commercial banks?. The Journal of Finance, 57(3), 1093-1111. https://doi.org/10.1111/1540-6261.00455.
  • Born, B. & Breitung, J. (2016). Testing for serial correlation in fixed-effects panel data models. Econometric Reviews, 35(7), 1290-1316. https://doi.org/10.1080/07474938.2014.976524.
  • Bostancı, A., & Korkmaz, T. (2014). Comparison of value at risk calculation models in terms of banks’ capital adequacy ratio. Business and Economics Research Journal, 5(3), 15-41. https://www.berjournal.com/wp-content/plugins/downloads-manager/upload/BERJ5(3)14Article2pp.15-41.pdf.
  • Boyle, P., Broadie, M., & Glasserman, P. (1997). Monte Carlo methods for security pricing. Journal of Economic Dynamics and Control, 21(8-9), 1267-1321. https://doi.org/10.1016/S0165-1889(97)00028-6.
  • Bozkuş, S. (2005). Risk ölçümünde alternatif yaklaşımlar: Riske maruz değer (VaR) ve beklenen kayıp (ES) uygulamaları. Dokuz Eylül Üniversitesi İktisadi İdari Bilimler Fakültesi Dergisi, 20(2), 27-45. https://dergipark.org.tr/en/pub/deuiibfd/issue/22753/242876.
  • Brandolini, D., & Colucci, S. (2012). Backtesting value-at-risk: A comparison between filtered bootstrap and historical simulation. Journal of Risk Model Validation, 6(4), 3-16. Available at SSRN: https://ssrn.com/abstract=1965377 or http://dx.doi.org/10.2139/ssrn.1965377.
  • Breusch, T. S. & Pagan, A. R. (1979). A simple test for heteroskedasticity and random coefficient variation. Econometrica, 47(5), 1287-1294. https://doi.org/10.2307/1911963.
  • Breusch, T. S. & Pagan, A. R. (1980). The lagrange multiplier test and its applications to model specification in econometrics. Review of Economic Studies, 47(1), 239-253. https://doi.org/10.2307/2297111.
  • Butler, C. (1999). Mastering Value at Risk: A step-by-step guide to understanding and applying VAR. Pitman Publishing.
  • Cabedo, J. D., & Moya, I. (2003). Estimating oil price ‘value at risk’ using the historical simulation approach. Energy Economics, 25(3), 239-253. https://doi.org/10.1016/S0140-9883(02)00111-1.
  • Campbell, R., Huisman, R., & Koedijk, K. (2001). Optimal portfolio selection in a Value-at-Risk framework. Journal of Banking & Finance, 25, 1789-1804. https://doi.org/10.1016/S0378-4266(00)00160-6.
  • Chipalkatti, N., & Datar, V. (2006). The relevance of value‐at‐risk disclosures: Evidence from the LTCM crisis. Journal of Financial Regulation and Compliance, 14(2), 174-184. https://doi.org/10.1108/13581980610659486.
  • Demireli, E., & Taner, B. (2009). Risk yönetiminde riske maruz değer yöntemleri ve bir uygulama. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 14(3), 127-148. https://dergipark.org.tr/en/pub/sduiibfd/issue/20829/223082.
  • Efron, B. (1979). Bootstrap methods: Another look at the Jackknife. The Annals of Statistics, 7, 1–26. https://link.springer.com/chapter/10.1007/978-1-4612-4380-9_41.
  • Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. Chapman & Hall.
  • Fıkırkoca, M. (2003). Bütünsel risk yönetimi. Pozitif Matbaacılık.
  • Gallagher, R. B. (1956). Risk management: New phase of cost control. Harvard Business Review, 34(5), 75-86. https://www.econbiz.de/Record/risk-management-new-phase-of-cost-control-gallagher-russell/10002178905.
  • Giot, P., & Laurent, S. (2003). Value‐at‐risk for long and short trading positions. Journal of Applied Econometrics, 18(6), 641-663. https://doi.org/10.1002/jae.710.
  • Giot, P., & Laurent, S. (2004). Modelling daily Value-at-Risk using realized volatility and ARCH type models. Journal of Empirical Finance, 11(3), 379-398. https://doi.org/10.1016/j.jempfin.2003.04.003.
  • Glasserman, P., Heidelberger, P., & Shahabuddin, P. (2002). Portfolio value‐at‐risk with heavy‐tailed risk factors. Mathematical Finance, 12(3), 239-269. https://doi.org/10.1111/1467-9965.00141.
  • Gökgöz, E. (2006). Riske maruz değer (VaR) ve portföy optimizasyonu. Sermaye Piyasası Kurulu Yayınları.
  • Gürsakal, S. (2007). İMKB 30 Endeksi Getiri Serisinin Riske Maruz Değerlerinin Tarihi Simülasyon ve Varyans-Kovaryans Yöntemleri ile Hesaplanması. [Conference presentation]. 8. Türkiye Ekonometri ve İstatistik Kongresi, 24-25 Mayıs 2007, İnönü University, Malatya, Turkey.
  • Harmantzis, F. C., Miao, L., & Chien, Y. (2006). Empirical study of value‐at‐risk and expected shortfall models with heavy tails. The Journal of Risk Finance, 7(2), 117-135. https://doi.org/10.1108/15265940610648571.
  • Hendrics D. (1996). Evaluation of Value at Risk models using historical data. Federal Reserve Bank of New York Economy Policy Review, 2(4), 39-70. Available at SSRN: https://ssrn.com/abstract=1028807 or http://dx.doi.org/10.2139/ssrn.1028807.
  • Honda, Y. (1985). Testing the error components model with non-normal disturbances. Review of Economic Studies, 52, 681-690. https://doi.org/10.2307/2297739.
  • Işıldak, M. S. (2021). Asimetrik Garch modellerle riske maruz değer (RMD) analizi: Altın, Bist 100 Endeksi ve Dolar’dan oluşan portföy üzerinde bir uygulama. Uluslararası Sosyal ve Eğitim Bilimleri Dergisi, 16, 41-67. https://doi.org/10.20860/ijoses.977206.
  • Jackson P., Maude, D. J., & Perraudin, W. (1998). Bank capital and Value at Risk. Bank of England Quarterly Bulletin, Spring, 73-89. Available at SSRN: https://ssrn.com/abstract=87288 or http://dx.doi.org/10.2139/ssrn.87288.
  • Jorion, P. (1997). Value at Risk: The new benchmark for controlling market risk. (5th edition). McGraw-Hill Inc: Chicago.
  • Jorion, P. (2000). Value-at-risk: The new benchmark for managing financial risk. (2nd Edition), McGraw-Hill.
  • Kavrar, Ö., & Yılmaz, B. (2019). Riske maruz değer yöntemiyle portföy riskinin belirlenmesi. Öneri Dergisi, 14(52), 486-508. https://doi.org/10.14783/maruoneri.595104.
  • Kayahan, C., & Topal, Y. (2009). Tarihsel riske maruz değer (RMD) finansal riskleri açıklamada yeterli midir?. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 14(1), 179-198. https://dergipark.org.tr/en/pub/sduiibfd/issue/20831/223143.
  • Korkmaz, T., & Bostancı, A. (2011). The comparison of volatility forecasting models in VaR calculations and backtesting according to Basel II: An application on ISE 100 Index. Business and Economics Research Journal, 2(3),1-17.
  • Korkmaz, T. & Kuzay, S. (2022). Uluslararası çeşitlendirilmiş portföylerde riske maruz değer (RMD) ölçümü. In S. Yaman, & T. Nur (Eds.), Risk yönetimi: Teori ve uygulamalar (pp. 25-50). Gazi Kitabevi.
  • Korkmaz, T., & Pekkaya, M. (2021). Excel uygulamalı finans matematiği. (4th Edition). Ekin Yayınevi.
  • Kuester, K., Mittnik, S., & Paolella, M. S. (2006). Value-at-risk prediction: A comparison of alternative strategies. Journal of Financial Econometrics, 4(1), 53-89. https://doi.org/10.1093/jjfinec/nbj002.
  • Laporta, A. G., Merlo, L., & Petrella, L. (2018). Selection of value at risk models for energy commodities. Energy Economics, 74, 628-643. https://doi.org/10.1016/j.eneco.2018.07.009.
  • Levin, A., Lin, C. F. & Chu, C. S. J. (2002). Unit root tests in panel data: Asymptotic and finite-sample properties. Journal of Econometrics, 108, 1-24. https://doi.org/10.1016/S0304-4076(01)00098-7.
  • Likitratcharoen, D., Chudasring, P., Pinmanee, C., & Wiwattanalamphong, K. (2023). The efficiency of Value-at-Risk models during extreme market stress in cryptocurrencies. Sustainability, 15(5), 1-21. https://doi.org/10.3390/su15054395.
  • Linsmeier, T. J., & Pearson, N. D. (1996). Risk Measurement: An Introduction to Value at Risk. ACE Reports: University of Illinois at Urbana-Champaign. (No. 1629-2016-134959). https://www.exinfm.com/training/pdfiles/valueatrisk.pdf.
  • Linsmeier, T. J., & Pearson, N. D. (2000). Value at risk. Financial Analysts Journal, 56(2), 47-67. https://doi.org/10.2469/faj.v56.n2.2343.
  • Lin, S-K., Wang, R-H., & Fuh, C-D. (2006). Risk management for linear and non-linear assets: A bootstrap method with ımportance resampling to evaluate Value-at-Risk. Asia-Pacific Financial Markets, 13(3), 261–295. https://doi.org/10.1007/s10690-007-9042-0.
  • Liu, W., Semeyutin, A., Lau, C. K. M., & Gozgor, G. (2020). Forecasting Value-at-Risk of cryptocurrencies with RiskMetrics type models. Research in International Business and Finance, 54, 1-14. https://doi.org/10.1016/j.ribaf.2020.101259.
  • Markowitz, H. (1952). Portfolio selection. The Journal of Finance. 7(1), pp. 77-91. https://doi.org/10.2307/2975974.
  • Mentel, G. (2013). Parametric or non-parametric estimation of value-at-risk. International Journal of Business and Management, 8(11), 103-112. https://doi.org/10.5539/ijbm.v8n11p103.
  • Oppong, S. O., Asamoah, D., & Oppong, E. O. (2016, May). Value at risk: Historical simulation or Monte Carlo simulation. [Conference presentation]. International Conference on Management, Communication and Technology (ICMCT), 4(1), 45-51.
  • Özden, Ü. H. (2007). Riske maruz değer (RMD) hesaplama yöntemleri: İMKB üzerine uygulama. Öneri Dergisi, 7(28), 279-285. https://doi.org/10.14783/maruoneri.684413.
  • Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels. Cambridge Working Papers in Economics, 435, 1-39. Available at SSRN: https://ssrn.com/abstract=572504 or http://dx.doi.org/10.2139/ssrn.572504.
  • Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross section dependence. Journal of Applied Econometrics, 22, 265–312. https://doi.org/10.1002/jae.951.
  • Pesaran, M. H. & Yamagata, T. (2008). Testing slope homogeneity in large panels. Journal of Econometrics, 142, 50–93. https://doi.org/10.1016/j.jeconom.2007.05.010.
  • Pesaran, M. H., Ullah, A., & Yamagata, T. (2008). A bias adjusted LM test of error cross-section independence. Econometrics Journal, 11, 105–127. https://doi.org/10.1111/j.1368-423X.2007.00227.x.
  • So, M. K. P., & Yu, P. L. H. (2006). Empirical analysis of GARCH models in value at risk estimation. Journal of International Financial Markets, Institutions and Money, 16(2), 180-197. https://doi.org/10.1016/j.intfin.2005.02.001.
  • Taş, O., & İltüzer, Z. (2016). Monte Carlo simulasyon yöntemi ile riske maruz değerin İMKB30 Endeksi ve DİBS portföyü üzerinde bir uygulaması. Dokuz Eylül Üniversitesi İktisadi İdari Bilimler Fakültesi Dergisi, 23(1), 67-87. https://dergipark.org.tr/en/pub/deuiibfd/issue/22743/242755.
  • Topaloğlu, E. E. & Kurt Cihangir, Ç. (2022). Riske maruz değer – getiri ilişkisi: BIST banka portföyü üzerine ekonometrik bir araştirma. In S. Yaman, & T. Nur (Eds.), Risk yönetimi: Teori ve uygulamalar (pp. 51-88). Gazi Kitabevi.
  • Trucíos, C., & Taylor, J. W. (2023). A comparison of methods for forecasting value at risk and expected shortfall of cryptocurrencies. Journal of Forecasting, 42(4), 989-1007. https://doi.org/10.1002/for.2929.
  • Türker, H. (2009). Riske maruz değer (Value at Risk) ve stres testi: Global finansal kriz sonrası etkinliklerinin değerlendirilmesi. SPK Araştırma Raporu.
  • Türkyılmaz, S. (2023). Uzun hafızalı asimetrik oynaklık modelleri ile riske maruz değer (VaR) tahmini: Covid-19 dönemi altın piyasası. Karamanoğlu Mehmetbey Üniversitesi Sosyal ve Ekonomik Araştırmalar Dergisi, 25(44), 66-86. https://dergipark.org.tr/en/pub/kmusekad/issue/78068/1170112. Ural, M., Demireli, E. & Aydın, Ü. (2022). Finansal yatırımlarda riske maruz değer analizi (Value at Risk). Seçkin Yayınevi. Vlaar, P. J. (2000). Value at risk models for Dutch bond portfolios. Journal of Banking & Finance, 24(7), 1131-1154. https://doi.org/10.1016/S0378-4266(99)00068-0.
Toplam 64 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Finans, Finansal Öngörü ve Modelleme, Finansal Risk Yönetimi
Bölüm Araştırma Makaleleri
Yazarlar

Serdar Yaman 0000-0002-8316-0805

Erken Görünüm Tarihi 29 Mart 2024
Yayımlanma Tarihi 31 Mart 2024
Gönderilme Tarihi 18 Aralık 2023
Kabul Tarihi 11 Mart 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 11 Sayı: 1

Kaynak Göster

APA Yaman, S. (2024). Financial Decisions and Value-at-Risk: Empirical Evidence from BIST 100 Companies. Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty, 11(1), 366-392. https://doi.org/10.30798/makuiibf.1406660