Araştırma Makalesi
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Volatilite Tahmini: iskandinav Hisse Senedi Piyasalarından Bulgular

Yıl 2023, Cilt: 13 Sayı: 2, 1 - 12, 28.12.2023

Öz

Bu çalışma, İskandinav borsaları için en etkin volatilite tahmin modelini belirlemeyi amaçlamaktadır. Bu bağlamda, HAR-(RV, RSV ve PS) modellerinin tahmin gücü, 2010-2019 yılları arasında 7 İskandinav borsa endeksi için yüksek frekanslı veriler kullanılarak ARFIMA-RV modeli ile karşılaştırılmıştır. Özyinelemeli pencere mekanizması kullanılarak bir gün sonra gerçekleşen örneklem dışı volatilite tahminleri üretilmektedir. Örneklem dışı tahmin kayıpları, MSE ve QLIKE kriterleri ile ölçülür. Sonuçlar birkaç önemli noktaya işaret etmektedir. İlk olarak, HAR-RV (PS ve RSV) modellerinin, ARFIMA-RV modeline göre daha iyi performans gösteren model grubu olduğu öne sürülmektedir. İkincisi, varyansın pozitif ve negatif yarı varyanslara veya diğer bir deyişle iyi ve kötü varyanslara ayrıştırılması, bazı durumlarda, gelecekteki varyansın tahminine yardım eden faydalı finansal bilgiler sunabilir. Son olarak, sonuçlar ve bulgular pazara, veri sıklığına, zaman ufkuna ve verilerin bazı karakteristik özelliklerine özgüdür ve bulguların yorumlanmasında bu faktörlerin önemi vurgulanmaktadır.

Kaynakça

  • Andersen, T. G. & Bollerslev, T. (1998). Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International Economic Review, 39(4), 885-905.
  • Andersen, T. G., & Bollerslev, T. (1997). Intraday periodicity and volatility persistence in financial markets. Journal of Empirical Finance, 4, 115–158.
  • Andersen, T. G., Bollerslev, T., Diebold, F. X., & Labys, P. (2001). The distribution of exchange rate volatility. Journal of the American Statistical Association, 96, 42–55.
  • Andersen, T. G., Bollerslev, T., Diebold, F. X., & Labys, P. (2003). Modelling and Forecasting Realized Volatility. Econometrica, 71(2), 579-625.
  • Barndorff-Nielsen, O., S. Kinnebrock, & N. Shephard. (2010). Measuring Downside Risk: Realized Semi-variance. In Volatility and Time Series Econometrics: Essays in Honour of Robert F. Engle, T. Bollerslev, J. Russell, and M. Watson, eds. Oxford; New York: Oxford University Press, 117–136.
  • Blair, B. J., Poon, S.-H., & Taylor, S. J. (2001). Forecasting S&P100 volatility: the incremental information content of implied volatilities and high-frequency index returns. Journal of Econometrics, 105(1), 5-26.
  • Bollerslev, T.; S. Z. Li; & V. Todorov. (2016). Roughing up Beta: Continuous vs. Discontinuous Betas and the Cross-Section of Expected Stock Returns. Journal of Financial Economics, 120, 464–490.
  • Chortareas, G., Jiang, Y., & Nankervis, J. (2011). Forecasting exchange rate volatility using high-frequency data: Is the euro different? International Journal of Forecasting, 27, 1089-1107.
  • Corsi, F. (2009). A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics, 7(2), 174-196.
  • Engle, R. (2002). New frontiers for arch models. Journal of Applied Econometrics, 17(5), 425-446.
  • Fang, N., Jiang, W., & Luo, R. (2017). Realized Semivariances and the Variation of Signed Jumps in China’s Stock Market. Emerging Markets Finance and Trade, 53(3), 563-586.
  • Hansen, P. R., & Lunde, A., (2010). Forecasting volatility using high frequency data. A systematic review, 1-37.
  • Koopman, S. J., Jungbacker, B., & Hol, E. (2005). Forecasting daily variability of the S&P 100 stock index using historical, realized and implied volatility measurements. Journal of Empirical Finance, 12, 445–475.
  • Liu, L. Y., Patton, A. J., & Sheppard, K. (2015). Does anything beat 5-minute rv? a comparison of realized measures across multiple asset classes. Journal of Econometrics, 187(1), 293-311.
  • Martens, M., & Zein, J. (2004). Predicting financial volatility: high-frequency time-series forecasts vis-`a-vis implied volatility. Journal of Futures Markets, 24, 1005–1028.
  • Martens, M., & Zein, J. (2004). Predicting financial volatility: high-frequency time-series forecasts vis-`a-vis implied volatility. Journal of Futures Markets, 24, 1005–1028.
  • Merton, Robert C. (1980). On Estimating the Expected Return on the Market: An Exploratory Investigation, Journal of Financial Economics, 8, 1-39.
  • Müller, U. A., Dacorogna, M. M., Davé, R. D., Olsen, R. B., Pictet, O. V. Von Weizsäcker, J. E. (1997). Volatilities of different time resolutions, Analysing the dynamics of market components, Journal of Empirical Finance, 4 (2-3) (1997), 213-239.
  • Patton, A. J. (2011). Volatility forecast comparison using imperfect volatility proxies. Journal of Econometrics, 160(1), 246 - 256.
  • Patton, A. J. & Sheppard, K. (2009). Optimal combinations of realized volatility estimators. International Journal of Forecasting, 25(2), 218-238.
  • Patton, A. J. & Sheppard, K. (2015). Good Volatility, Bad Volatility: Signed Jumps and The Persistence of Volatility. The Review of Economics and Statistics, 97(3), 683-697.
  • Sevi, B. (2014). Forecasting the volatility of crude oil futures using intraday data. European Journal of Operational Research, 235, 643–659.

Forecasting Realized Volatility: Evidence From Nordic Stock Markets

Yıl 2023, Cilt: 13 Sayı: 2, 1 - 12, 28.12.2023

Öz

This study aims to determine the most effective model for forecasting volatility within the Nordic stock markets. In this regard, the forecasting power of HAR-RV, RSV, and PS models is compared to the ARFIMA-RV model using high frequency data for 7 Nordic stock market indices spanning from 2010 to 2019. One-day-ahead out-of-sample realized volatility forecasts are produced using a recursive window mechanism. The out-of-sample forecast losses are measured by the MSE and QLIKE criteria. The results indicate several noteworthy points. Firstly, the HAR-RV (PS and RSV) models are suggested to be best performing realized volatility models over the ARFIMA-RV model. Secondly, the separation of realized variance into positive and negative realized semivariances, which is known as good and bad volatilities, might offer valuable financial insights in certain situations, aiding the prediction of future realized volatility. Lastly, the results and findings are specific to market, data frequency, time horizon, and some characteristics of data, emphasizing the importance of these factors in interpreting the findings.

Kaynakça

  • Andersen, T. G. & Bollerslev, T. (1998). Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International Economic Review, 39(4), 885-905.
  • Andersen, T. G., & Bollerslev, T. (1997). Intraday periodicity and volatility persistence in financial markets. Journal of Empirical Finance, 4, 115–158.
  • Andersen, T. G., Bollerslev, T., Diebold, F. X., & Labys, P. (2001). The distribution of exchange rate volatility. Journal of the American Statistical Association, 96, 42–55.
  • Andersen, T. G., Bollerslev, T., Diebold, F. X., & Labys, P. (2003). Modelling and Forecasting Realized Volatility. Econometrica, 71(2), 579-625.
  • Barndorff-Nielsen, O., S. Kinnebrock, & N. Shephard. (2010). Measuring Downside Risk: Realized Semi-variance. In Volatility and Time Series Econometrics: Essays in Honour of Robert F. Engle, T. Bollerslev, J. Russell, and M. Watson, eds. Oxford; New York: Oxford University Press, 117–136.
  • Blair, B. J., Poon, S.-H., & Taylor, S. J. (2001). Forecasting S&P100 volatility: the incremental information content of implied volatilities and high-frequency index returns. Journal of Econometrics, 105(1), 5-26.
  • Bollerslev, T.; S. Z. Li; & V. Todorov. (2016). Roughing up Beta: Continuous vs. Discontinuous Betas and the Cross-Section of Expected Stock Returns. Journal of Financial Economics, 120, 464–490.
  • Chortareas, G., Jiang, Y., & Nankervis, J. (2011). Forecasting exchange rate volatility using high-frequency data: Is the euro different? International Journal of Forecasting, 27, 1089-1107.
  • Corsi, F. (2009). A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics, 7(2), 174-196.
  • Engle, R. (2002). New frontiers for arch models. Journal of Applied Econometrics, 17(5), 425-446.
  • Fang, N., Jiang, W., & Luo, R. (2017). Realized Semivariances and the Variation of Signed Jumps in China’s Stock Market. Emerging Markets Finance and Trade, 53(3), 563-586.
  • Hansen, P. R., & Lunde, A., (2010). Forecasting volatility using high frequency data. A systematic review, 1-37.
  • Koopman, S. J., Jungbacker, B., & Hol, E. (2005). Forecasting daily variability of the S&P 100 stock index using historical, realized and implied volatility measurements. Journal of Empirical Finance, 12, 445–475.
  • Liu, L. Y., Patton, A. J., & Sheppard, K. (2015). Does anything beat 5-minute rv? a comparison of realized measures across multiple asset classes. Journal of Econometrics, 187(1), 293-311.
  • Martens, M., & Zein, J. (2004). Predicting financial volatility: high-frequency time-series forecasts vis-`a-vis implied volatility. Journal of Futures Markets, 24, 1005–1028.
  • Martens, M., & Zein, J. (2004). Predicting financial volatility: high-frequency time-series forecasts vis-`a-vis implied volatility. Journal of Futures Markets, 24, 1005–1028.
  • Merton, Robert C. (1980). On Estimating the Expected Return on the Market: An Exploratory Investigation, Journal of Financial Economics, 8, 1-39.
  • Müller, U. A., Dacorogna, M. M., Davé, R. D., Olsen, R. B., Pictet, O. V. Von Weizsäcker, J. E. (1997). Volatilities of different time resolutions, Analysing the dynamics of market components, Journal of Empirical Finance, 4 (2-3) (1997), 213-239.
  • Patton, A. J. (2011). Volatility forecast comparison using imperfect volatility proxies. Journal of Econometrics, 160(1), 246 - 256.
  • Patton, A. J. & Sheppard, K. (2009). Optimal combinations of realized volatility estimators. International Journal of Forecasting, 25(2), 218-238.
  • Patton, A. J. & Sheppard, K. (2015). Good Volatility, Bad Volatility: Signed Jumps and The Persistence of Volatility. The Review of Economics and Statistics, 97(3), 683-697.
  • Sevi, B. (2014). Forecasting the volatility of crude oil futures using intraday data. European Journal of Operational Research, 235, 643–659.
Toplam 22 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular İstatistik
Bölüm Araştırma Makaleleri
Yazarlar

Burak Korkusuz 0000-0001-9374-2350

Yayımlanma Tarihi 28 Aralık 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 13 Sayı: 2

Kaynak Göster

APA Korkusuz, B. (2023). Forecasting Realized Volatility: Evidence From Nordic Stock Markets. İstatistik Araştırma Dergisi, 13(2), 1-12.