Economic Value of Prediction of Return Distribution
Year 2023,
Volume: 8 Issue: 1, 40 - 58, 31.03.2023
Cem Çakmaklı
,
Anıl Divarcı Çakmaklı
,
Han Özsöylev
Abstract
Modeling the distribution of asset returns is crucial in constructing financial portfolios. Accurate and timely modeling of the distribution of asset returns paves the way for the construction of portfolios using these distributions that can provide higher return/volatility performance compared to conventional assets such as the market index. This paper proposes a modeling approach by considering the relationship between the average and volatility of returns of S&P 500 over the period of 2000-2019. The joint distribution of the returns and their volatility is modeled by explicitly incorporating the links between the returns and volatility. This is executed by allowing for asymmetric relations between the mean and volatility. Capturing the timing and asymmetrical nature of these relationships provides a much better estimation of the asset distribution and its volatility. Predictions of real-time return distributions are formed based on this model. The model’s performance is evaluated from the point of a representative investor constructing her portfolio using real-time forecasts based on this model. This investor determines the portfolio weights based on the outcome of the econometric model explicitly capturing the relationship between expected return and volatility in each period. Results show that the link between returns and their volatility bears considerable economic value. Moreover, the findings remain robust to various effects.
References
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- Anatolyev, S. and Kobotaev, N. (2018). Modeling and forecasting realized covariance matrices with accounting for leverage. Econometric Reviews 37(2), 114–139. https://doi.org/10.1080/07474938.2015.1035165
- Andersen, T.G., Bollerslev, T. and Diebold, F.X. (2007). Roughing it up: Including jump components in the measurement, modeling, and forecasting of return volatility. The Review of Economics and Statistics, 89(4), 701-720. https://doi.org/10.1162/rest.89.4.701
- Andersen, T.G., Bollerslev, T., Diebold, F.X. and Ebens, H. (2001). The distribution of realized stock return volatility. Journal of Financial Economics, 61(1), 43-76. https://doi.org/10.1016/S0304-405X(01)00055-1
- Andersen, T.G., Bollerslev, T., Diebold, F.X. and Labys, P. (2003). Modeling and forecasting realized volatility. Econometrica, 71(2), 579-625. https://doi.org/10.1111/1468-0262.00418
- Baillie, R.T., Bollerslev, T. and Mikkelsen, H.O. (1996). Fractionally integrated generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 74(1), 3–30. https://doi.org/10.1016/S0304-4076(95)01749-6
- Barberis, N. (2000). Investing for the long run when returns are predictable. Journal of Finance 55(1), 225-64. https://doi.org/10.1111/0022-1082.00205
- Barndorff‐Nielsen, O.E. and Shephard, N. (2002). Econometric analysis of realized volatility and its use in estimating stochastic volatility models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(2), 253-280. https://doi.org/10.1111/1467-9868.00336
- Bollerslev, T., Kretschmer, U., Pigorsch, C. and Tauchen, G. (2009). A discrete-time model for daily S & P500 returns and realized variations: Jumps and leverage effects. Journal of Econometrics, 150(2), 151-166. https://doi.org/10.1016/j.jeconom.2008.12.001
- Bollerslev, T., Litvinova, J. and Tauchen, G. (2006). Leverage and volatility feedback effects in high-frequency data. Journal of Financial Econometrics, 4(3), 353-384. https://doi.org/10.1093/jjfinec/nbj014
- Campbell, J.Y. and Thompson, S.B. (2008). Predicting excess stock returns out of sample: Can anything beat the historical average? Review of Financial Studies, 21(4), 1509–1531. https://doi.org/10.1093/rfs/hhm055
- Christiansen, C., Schmeling, M. and Schrimpf, A. (2012). A comprehensive look at financial volatility prediction by economic variables. Journal of Applied Econometrics, 27(6), 956–977. https://doi.org/10.1002/jae.2298
- Corsi, F. (2009). A simple long memory model of realized volatility. Journal of Financial Econometrics, 7, 174–196. https://doi.org/10.1093/jjfinec/nbp001
- Corsi, F., Audrino, F. and Reno, R. (2012). HAR modeling for realized volatility forecasting. In L. Bauwens, C. Hafner and S. Laurent (Eds.), Handbook of volatility models and their applications (pp. 363–382). New York: John Wiley & Sons, Ltd.
- Cremers, K.J.M. (2002). Stock return predictability: A Bayesian model selection perspective. Review of Financial Studies, 15(4), 1223–1249. https://doi.org/10.1093/rfs/15.4.1223
- Çakmaklı, C. and Ozturk, V. (2021). Economic value of modeling the joint distribution of returns and volatility: Leverage timing (TÜSIAD Economic Research Forum (ERF) Working Paper No. 2110). Retrieved from https://www.econstor.eu/bitstream/10419/243013/1/erf-wp-2110.pdf
- Çakmaklı, C. and van Dijk, D. (2016). Getting the most out of macroeconomic information for predicting excess stock returns. International Journal of Forecasting, 32(3), 650–668. https://doi.org/10.1016/j.ijforecast.2015.10.001
- Engle, R.F. and Ng, V.K. (1993). Measuring and testing the impact of news on volatility. The Journal of Finance, 48(5), 1749-1778. https://doi.org/10.1111/j.1540-6261.1993.tb05127.x
- Engle, R.F. and Siriwardane, E.N. (2018). Structural GARCH: The volatility-leverage connection. The Review of Financial Studies, 31(2), 449-492. https://doi.org/10.1093/rfs/hhx099
- Fleming, J., Kirby, C. and Ostdiek, B. (2001). The economic value of volatility timing. The Journal of Finance, 56(1), 329-352. https://doi.org/10.1111/0022-1082.00327
- Fleming, J., Kirby, C. and Ostdiek, B. (2003). The economic value of volatility timing using “realized” volatility. Journal of Financial Economics, 67(3), 473-509. https://doi.org/10.1016/S0304-405X(02)00259-3
- Giraitis, L., Leipus, R., Robinson, P.M. and Surgailis, D. (2004). LARCH, leverage, and long memory. Journal of Financial Econometrics, 2(2), 177-210. https://doi.org/10.1093/jjfinec/nbh008
- Glosten, L.R., Jagannathan, R. and Runkle, D.E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance 48(5), 1779–1801. https://doi.org/10.1111/j.1540-6261.1993.tb05128.x
- Hansen, P.R. and Lunde, A. (2006). Realized variance and market microstructure noise. Journal of Business & Economic Statistics, 24(2), 127–161. https://doi.org/10.1198/073500106000000071
- Heber, G., Lunde, A., Shephard, N. and Sheppard, K. (2009). Oxford-Man Institute’s realized library. University of Oxford: Oxford&Man Institute.
- Jensen, M.J. and Maheu, J.M. (2014). Estimating a semiparametric asymmetric stochastic volatility model with a Dirichlet process mixture. Journal of Econometrics, 178, 523-538. https://doi.org/10.1016/j.jeconom.2013.08.018
- Jin, X. and Maheu, J.M. (2013). Modeling realized covariances and returns. Journal of Financial Econometrics, 11(2), 335-369. https://doi.org/10.1093/jjfinec/nbs022
- Maheu, J.M. and McCurdy, T.H. (2011). Do high-frequency measures of volatility improve forecasts of return distributions? Journal of Econometrics, 160(1), 69-76. https://doi.org/10.1016/j.jeconom.2010.03.016
- Marquering, W. and Verbeek, M. (2004). The economic value of predicting stock index returns and volatility. Journal of Financial and Quantitative Analysis, 39(2), 407–429. https://doi.org/10.1017/S0022109000003136
- Nelson, D.B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–70. https://doi.org/10.2307/2938260
- Nolte, I. and Xu, Q. (2015). The economic value of volatility timing with realized jumps. Journal of Empirical Finance, 34, 45–59. https://doi.org/10.1016/j.jempfin.2015.03.019
- Omori, Y., Chib, S., Shephard, N. and Nakajima, J. (2007). Stochastic volatility with leverage: Fast and efficient likelihood inference. Journal of Econometrics, 140(2), 425-449. https://doi.org/10.1016/j.jeconom.2006.07.008
- Pesaran, M.H. and Timmermann, A. (1995). Predictability of stock returns: Robustness and economic significance. Journal of Finance, 50(4), 1201–1228. https://doi.org/10.1111/j.1540-6261.1995.tb04055.x
- Rapach, D.E., Strauss, J.K. and Zhou, G. (2010). Out-of-sample equity premium prediction: Combination forecasts and links to the real economy. The Review of Financial Studies, 23(2), 821-862. https://doi.org/10.1093/rfs/hhp063
- Scharth, M. and Medeiros, M.C. (2009). Asymmetric effects and long memory in the volatility of Dow Jones stocks. International Journal of Forecasting, 25(2), 304–327. https://doi.org/10.1016/j.ijforecast.2009.01.008
- Shephard, N. and Sheppard, K. (2010). Realising the future: forecasting with high‐frequency‐based volatility (HEAVY) models. Journal of Applied Econometrics, 25(2), 197-231. https://doi.org/10.1002/jae.1158
- Welch, I. and Goyal, A. (2008). A comprehensive look at the empirical performance of equity premium prediction. The Review of Financial Studies, 21(4), 1455–1508. Retrieved from https://www.jstor.org/
- West, K.D., Edison, H.J. and Cho, D. (1993). A utility-based comparison of some models of exchange rate volatility. Journal of International Economics, 35(1-2), 23-45. https://doi.org/10.1016/0022-1996(93)90003-G
- Zhang, L., Mykland, P.A. and Ait-Sahalia, Y. (2005). A tale of two time scales: Determining integrated volatility with noisy high-frequency data. Journal of the American Statistical Association, 100(472), 1394-1411. https://doi.org/10.1198/016214505000000169
Getiri Dağılımı Tahmininin Ekonomik Değeri
Year 2023,
Volume: 8 Issue: 1, 40 - 58, 31.03.2023
Cem Çakmaklı
,
Anıl Divarcı Çakmaklı
,
Han Özsöylev
Abstract
Varlık getirilerinin istatistiki dağılımını modellemek özellikle finansal portföylerin oluşturulmasında çok önemlidir. Varlık getirilerinin dağılımının doğru ve zamanlı modellenmesi, bu dağılımlar yardımıyla oluşturulan portföylerin de piyasa endeksi gibi geleneksel finansal varlıklara göre daha yüksek getiri/oynaklık performansı sağlamasının önünü açacaktır. Bu makalede, 2000-2019 döneminde S&P 500 Endeks getirilerinin ortalaması ve oynaklığı arasındaki kaldıraç ilişkisi de ele alınarak bir modelleme yoluna gidilmiştir. Bu yapılırken de özellikle beklenen getiri ve oynaklık arasındaki asimetrik ilişki dikkate alınmıştır. Bu ilişkilerin hem zamanlamasının hem de asimetrik yapısının uygun bir şekilde ele alınması varlık dağılımının ve bu dağılımın volatilitesinin çok daha iyi tahmin edilmesini sağlamaktadır. Modelin başarısı, bu modelden yola çıkarak oluşturulan gerçek zamanlı tahminleri kullanarak yatırım yapan bir temsili yatırımcı gözüyle değerlendirilmiştir. Bu yatırımcı her zaman periyodunda beklenen getiri oynaklık arasındaki ilişkiyi bütünüyle ele alan ekonometrik model yardımıyla portföy ağırlıklarını belirlemektedir. Bu ağırlıklar sonucunda gerçekleşen portföy getirisinin temsili yatırımcıda yarattığı ortalama fayda modellerin başarı ölçütü olarak kullanılmıştır. Sonuçlar beklenen değer ve oynaklık arasındaki ilişkinin ekonomik değerinin oldukça yüksek olduğunu göstermiştir. Bu sonuçlar birçok farklı etmene karşı geçerliliğini korumaktadır. Dolayısıyla finansal portföy oluşumunda sadece getiri ve oynaklık değil, bu ikisi arasındaki ilişkinin de modellenmesi önerilmektedir.
References
- Ait-Sahalia, Y., Fan, J. and Li, Y. (2013). The leverage effect puzzle: Disentangling sources of bias at high frequency. Journal of Financial Economics, 109(1), 224-249. https://doi.org/10.1016/j.jfineco.2013.02.018
- Anatolyev, S. and Kobotaev, N. (2018). Modeling and forecasting realized covariance matrices with accounting for leverage. Econometric Reviews 37(2), 114–139. https://doi.org/10.1080/07474938.2015.1035165
- Andersen, T.G., Bollerslev, T. and Diebold, F.X. (2007). Roughing it up: Including jump components in the measurement, modeling, and forecasting of return volatility. The Review of Economics and Statistics, 89(4), 701-720. https://doi.org/10.1162/rest.89.4.701
- Andersen, T.G., Bollerslev, T., Diebold, F.X. and Ebens, H. (2001). The distribution of realized stock return volatility. Journal of Financial Economics, 61(1), 43-76. https://doi.org/10.1016/S0304-405X(01)00055-1
- Andersen, T.G., Bollerslev, T., Diebold, F.X. and Labys, P. (2003). Modeling and forecasting realized volatility. Econometrica, 71(2), 579-625. https://doi.org/10.1111/1468-0262.00418
- Baillie, R.T., Bollerslev, T. and Mikkelsen, H.O. (1996). Fractionally integrated generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 74(1), 3–30. https://doi.org/10.1016/S0304-4076(95)01749-6
- Barberis, N. (2000). Investing for the long run when returns are predictable. Journal of Finance 55(1), 225-64. https://doi.org/10.1111/0022-1082.00205
- Barndorff‐Nielsen, O.E. and Shephard, N. (2002). Econometric analysis of realized volatility and its use in estimating stochastic volatility models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(2), 253-280. https://doi.org/10.1111/1467-9868.00336
- Bollerslev, T., Kretschmer, U., Pigorsch, C. and Tauchen, G. (2009). A discrete-time model for daily S & P500 returns and realized variations: Jumps and leverage effects. Journal of Econometrics, 150(2), 151-166. https://doi.org/10.1016/j.jeconom.2008.12.001
- Bollerslev, T., Litvinova, J. and Tauchen, G. (2006). Leverage and volatility feedback effects in high-frequency data. Journal of Financial Econometrics, 4(3), 353-384. https://doi.org/10.1093/jjfinec/nbj014
- Campbell, J.Y. and Thompson, S.B. (2008). Predicting excess stock returns out of sample: Can anything beat the historical average? Review of Financial Studies, 21(4), 1509–1531. https://doi.org/10.1093/rfs/hhm055
- Christiansen, C., Schmeling, M. and Schrimpf, A. (2012). A comprehensive look at financial volatility prediction by economic variables. Journal of Applied Econometrics, 27(6), 956–977. https://doi.org/10.1002/jae.2298
- Corsi, F. (2009). A simple long memory model of realized volatility. Journal of Financial Econometrics, 7, 174–196. https://doi.org/10.1093/jjfinec/nbp001
- Corsi, F., Audrino, F. and Reno, R. (2012). HAR modeling for realized volatility forecasting. In L. Bauwens, C. Hafner and S. Laurent (Eds.), Handbook of volatility models and their applications (pp. 363–382). New York: John Wiley & Sons, Ltd.
- Cremers, K.J.M. (2002). Stock return predictability: A Bayesian model selection perspective. Review of Financial Studies, 15(4), 1223–1249. https://doi.org/10.1093/rfs/15.4.1223
- Çakmaklı, C. and Ozturk, V. (2021). Economic value of modeling the joint distribution of returns and volatility: Leverage timing (TÜSIAD Economic Research Forum (ERF) Working Paper No. 2110). Retrieved from https://www.econstor.eu/bitstream/10419/243013/1/erf-wp-2110.pdf
- Çakmaklı, C. and van Dijk, D. (2016). Getting the most out of macroeconomic information for predicting excess stock returns. International Journal of Forecasting, 32(3), 650–668. https://doi.org/10.1016/j.ijforecast.2015.10.001
- Engle, R.F. and Ng, V.K. (1993). Measuring and testing the impact of news on volatility. The Journal of Finance, 48(5), 1749-1778. https://doi.org/10.1111/j.1540-6261.1993.tb05127.x
- Engle, R.F. and Siriwardane, E.N. (2018). Structural GARCH: The volatility-leverage connection. The Review of Financial Studies, 31(2), 449-492. https://doi.org/10.1093/rfs/hhx099
- Fleming, J., Kirby, C. and Ostdiek, B. (2001). The economic value of volatility timing. The Journal of Finance, 56(1), 329-352. https://doi.org/10.1111/0022-1082.00327
- Fleming, J., Kirby, C. and Ostdiek, B. (2003). The economic value of volatility timing using “realized” volatility. Journal of Financial Economics, 67(3), 473-509. https://doi.org/10.1016/S0304-405X(02)00259-3
- Giraitis, L., Leipus, R., Robinson, P.M. and Surgailis, D. (2004). LARCH, leverage, and long memory. Journal of Financial Econometrics, 2(2), 177-210. https://doi.org/10.1093/jjfinec/nbh008
- Glosten, L.R., Jagannathan, R. and Runkle, D.E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance 48(5), 1779–1801. https://doi.org/10.1111/j.1540-6261.1993.tb05128.x
- Hansen, P.R. and Lunde, A. (2006). Realized variance and market microstructure noise. Journal of Business & Economic Statistics, 24(2), 127–161. https://doi.org/10.1198/073500106000000071
- Heber, G., Lunde, A., Shephard, N. and Sheppard, K. (2009). Oxford-Man Institute’s realized library. University of Oxford: Oxford&Man Institute.
- Jensen, M.J. and Maheu, J.M. (2014). Estimating a semiparametric asymmetric stochastic volatility model with a Dirichlet process mixture. Journal of Econometrics, 178, 523-538. https://doi.org/10.1016/j.jeconom.2013.08.018
- Jin, X. and Maheu, J.M. (2013). Modeling realized covariances and returns. Journal of Financial Econometrics, 11(2), 335-369. https://doi.org/10.1093/jjfinec/nbs022
- Maheu, J.M. and McCurdy, T.H. (2011). Do high-frequency measures of volatility improve forecasts of return distributions? Journal of Econometrics, 160(1), 69-76. https://doi.org/10.1016/j.jeconom.2010.03.016
- Marquering, W. and Verbeek, M. (2004). The economic value of predicting stock index returns and volatility. Journal of Financial and Quantitative Analysis, 39(2), 407–429. https://doi.org/10.1017/S0022109000003136
- Nelson, D.B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–70. https://doi.org/10.2307/2938260
- Nolte, I. and Xu, Q. (2015). The economic value of volatility timing with realized jumps. Journal of Empirical Finance, 34, 45–59. https://doi.org/10.1016/j.jempfin.2015.03.019
- Omori, Y., Chib, S., Shephard, N. and Nakajima, J. (2007). Stochastic volatility with leverage: Fast and efficient likelihood inference. Journal of Econometrics, 140(2), 425-449. https://doi.org/10.1016/j.jeconom.2006.07.008
- Pesaran, M.H. and Timmermann, A. (1995). Predictability of stock returns: Robustness and economic significance. Journal of Finance, 50(4), 1201–1228. https://doi.org/10.1111/j.1540-6261.1995.tb04055.x
- Rapach, D.E., Strauss, J.K. and Zhou, G. (2010). Out-of-sample equity premium prediction: Combination forecasts and links to the real economy. The Review of Financial Studies, 23(2), 821-862. https://doi.org/10.1093/rfs/hhp063
- Scharth, M. and Medeiros, M.C. (2009). Asymmetric effects and long memory in the volatility of Dow Jones stocks. International Journal of Forecasting, 25(2), 304–327. https://doi.org/10.1016/j.ijforecast.2009.01.008
- Shephard, N. and Sheppard, K. (2010). Realising the future: forecasting with high‐frequency‐based volatility (HEAVY) models. Journal of Applied Econometrics, 25(2), 197-231. https://doi.org/10.1002/jae.1158
- Welch, I. and Goyal, A. (2008). A comprehensive look at the empirical performance of equity premium prediction. The Review of Financial Studies, 21(4), 1455–1508. Retrieved from https://www.jstor.org/
- West, K.D., Edison, H.J. and Cho, D. (1993). A utility-based comparison of some models of exchange rate volatility. Journal of International Economics, 35(1-2), 23-45. https://doi.org/10.1016/0022-1996(93)90003-G
- Zhang, L., Mykland, P.A. and Ait-Sahalia, Y. (2005). A tale of two time scales: Determining integrated volatility with noisy high-frequency data. Journal of the American Statistical Association, 100(472), 1394-1411. https://doi.org/10.1198/016214505000000169