Témata prací (Výběr práce)Témata prací (Výběr práce)(verze: 368)
Detail práce
   Přihlásit přes CAS
Are realized moments useful for stock market returns analysis?
Název práce v češtině: Jsou realizované momenty užitečné pro analýzu výnosů akcií?
Název v anglickém jazyce: Are realized moments useful for stock market returns analysis?
Klíčová slova: Realizované momenty, skloněnost, špičatost, oceňování aktiv, akciové trhy
Klíčová slova anglicky: realized moments, skewness, kurtosis, asset pricing, stock market
Akademický rok vypsání: 2016/2017
Typ práce: diplomová práce
Jazyk práce: angličtina
Ústav: Institut ekonomických studií (23-IES)
Vedoucí / školitel: doc. PhDr. Jozef Baruník, Ph.D.
Řešitel: skrytý - zadáno vedoucím/školitelem
Datum přihlášení: 05.06.2017
Datum zadání: 05.06.2017
Datum a čas obhajoby: 19.06.2019 09:00
Místo konání obhajoby: Opletalova - Opletalova 26, O206, Opletalova - místn. č. 206
Datum odevzdání elektronické podoby:01.04.2019
Datum proběhlé obhajoby: 19.06.2019
Oponenti: prof. Ing. Evžen Kočenda, M.A., Ph.D., DSc.
 
 
 
Kontrola URKUND:
Seznam odborné literatury
Amaya, D., P., C., K, J., & A., V. (2015). Does realized skewness predict the cross-section of equity returns. Journal of Financial Economics, 118(1), 135-167.
Andersen, T., & Bollerslev, T. (1998). Answering the skeptics: yes, standard volatility models do provide accurate forecasts. International Economic Review, 39(4), 885-905.
Andersen, T., Bollerslev, T., Diebold, F., & Ebens, H. (2001). The distribution of realized stock return volatility. Journal of Financial Economics, 43-76.
Andersen, T., Bollerslev, T., Diebold, F., & Labys, P. (2001). The Distribution of Realized Exchange Rate Volatility. Journal of the American Statistical Association, 96(453), 42-55.
Andersen, T., Bollerslev, T., Diebold, F., & Labys, P. (2003). Modeling and Forecasting realized volatility. Econometrica, 71(2), 529-626.
Andersen, T., Dobrev, D., & Schaumburg, E. (2012). Jump-robust volatility estimation using nearest neighbor truncation. Journal of Econometrics, 169(1), 75-93.
Arbel, A., & Strebel, P. (1982). The neglected and small firm effects. Financial Review, 201-218.
Bakshi, G., Kapadia, N., & Madan, D. (2003). Stock return characteristics, skew laws, and the differential pricing of individual equity options. Review of Financial Studies, 16(1), 101-143.
Barberis, N., & Huang, M. (2008). Stocks as lotteries: the implications of probability weighting for security prices. American Economic Review, 98(5), 2066-2100.
Barndorff-Nielsen, O., & Shephard, N. (2004). Power and bipower variation with stochastic volatility and jumps (with discussion). Journal of Financial Econometrics, 2(1), 1-48.
Barndorff-Nielsen, O., Kinnerbrock, S., & Shepard, N. (2010). Measuring downside risk - realised semivarince. Volatility and Time Series Econometrics: Essays in Honor of Robert Engle, 117-136.
Bonato, M. (2011). Robust estimation of skewness and kurtosis in distributions with infinite higher moments. Finance Research Letters 8, 8(2), 77-87.
Bowley, A. (1920). Elements of Statistics. P. S. King & son, Limited.
Boyer, B., Mitton, T., & Vorkink, K. (2010). Expected idiosyncratic skewness. Review of Financial Studies, 23(1), 169-202.
Brunnermeier, M., Gollier, C., & Parker, J. (2007). Optimal Beliefs, Asset Prices and the Reference for Skewed Returns. American Economic Review, 97(2), 159-165.
Brys, G., Hubert, M., & Struyf, A. (2006). Robust Measures of tail weight. Computational Statistics & Data Analysis, 50(3), 733-759.
Cochrane, J. H. (2009). Asset pricing: Revised edition. Princeton university press.
Conrad, J., Dittmar, R., & Ghysels, E. (2013). Ex ante skewness and expected stock returns. Journal of Finance, 68(1), 85-124.
Corsi, F., & Reno, R. (2009). HAR volatility modelling with heterogeneous leverage and jumps. Available at SSRN 1316953.
Crow, E., & Siddiqui, M. (1967). Robust estimation of location. Journal of the American Statistical Association, 62(318), 353-389.
Diebold, F., & Mariano, R. (1995). Comparing Predictive Accuracy. Journal of Business and Economic Statistics, 13(3), 253-63.
Fama, E., & French, K. (1993). Common risk factors in returns on stocks and bonds. Journal of Financial Economics, 3-56.
Fleming, J., Kirby, C., & Ostdiek, B. (2003). The economic value of volatility timing using "realized" volatility. Journal of Financial Economics, 473-509.
Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning (Vol. 1). New York: Springer series in statistic.
Groeneveld, R., & Meeden, G. (1984). Measuring skewness and kurtosis. The Statistician, 33(4), 391-399.
Harvey, D., Leybourne, S., & Newbold, P. (1997). Testing the equality of prediction mean squared errors. International Journal of forecasting, 13(2), 281-291.
Hinkley, D. (1975). On power transformations to symmetry. Biometrika, 62(1), 101-111.
Hsieh, D. (1991). Chaos and nonlinear dynamics: application to financial markets. Journal of Finance, 46(5), 1839-1877.
Huber, P., & Ronchetti, E. (2009). Robust Statistics. New Jersey: John Wiley & Sons, Inc.
Jacod, J. (2012). Statistics and high frequency data. In: Kessler, M., Lindner, A., Sorensen, M. (Eds.). Statistical Methods for Stochastic Differential Equations, 191-310.
Jegadeesh, N. (1990). Evidence of predictable behavior of security returns. Journal of Finance, 45(3), 881-898.
Kendall, M., & Stuart, A. (1977). The Advanced Theory of Statistics. Griffin, London.
Kim, T.-H., & White, H. (2004). On more robust estimation of skewness and kurtosis. Finance Research Letters 1, 1(1), 56-73.
Lehmann, B. (1990). Fads, martingales, and market efficiency. Quarterly Journal of Economics, 105(1), 1-28.
Merton, R. (1980). On estimating the expected return on the market: an exploratory ivestigation. Journal of Financial Economics, 8(4), 323-361.
Mitton, T., & Vorkink, K. (2007). Equilibrium Underdiversification and the Preference for Skewness. Review of Financial Studies, 20(4), 1255-1288.
Moors, J. (1988). A quantile alternative for kurtosis. The Statistician 37, 37(1), 25-32. Bibliography 71
Neuberger, A. (2012). Realized skewness. Review of Financial Studies, 25(11), 3423-3455.
Neuberger, A., & Payne, R. (2018). The Skewness of the Stock Market at Long Horizons. Available at SSRN: https://ssrn.com/abstract=3173581 or http://dx.doi.org/10.2139/ssrn.3173581.
Paye, B. (2012). Deja vol: Predictive regressions for aggregate stock market volatility using macroeconomic variables. Journal of Financial Economics, 106(3), 527-546.
Rehman, Z., & Vilkov, G. (2010). Risk-neutral skewness: return predictability and its sources. Unpublished working paper. BlackRock and Frankfurt School of Finance and Management.
Rousseeuw, P., & Leroy, A. (2005). Robust regression and outlier detection. John Wiley & sons.
Schmid, F., & Trede, M. (2003). Simple tests for peakedness, fat tails and leptokurtosis based on quantiles. Computational Statistics and Data Analysis, 43(1), 1-12.
Schwert, G. (1989). Why does stock market volatility change over time? Journal of Finance, 44(5), 1115-1153.
van Zwet, W. (1968). Convex Transformations of Random Variables. Biometrical Journal, 10(1).
Xing, Y., Zhang, X., & Zhao, R. (2010). What does the individual option volatility smirk tell us about future equity returns? Journal of Financial and Quantitative Analysis, 45(3), 641 - 662.
Zeileis, A. (2004). Econometric Computing with HC and HAC Covariance Matrix Estimators. Journal of Statistical Software, 11(10), 1-17.
Zeileis, A. (2006). Object-oriented Computation of Sandwich Estimators. Journal of Statistical Software, 16(9), 1 - 16.
Zhang, Y. (2006). Individual skewness and the cross-section of average stock returns. Unpublished working paper, Yale University.
Předběžná náplň práce v anglickém jazyce
It has been a never-ending struggle to understand the properties of stock returns and be able to forecast their future development. From simple AR and MA models, through GARCH and their fractionally integrated versions, to intra-day data based models such as HAR, researchers and investors alike have been trying to analyze and forecast the behavior of returns. The question of stock return properties is of the utmost importance when it comes to portfolio or risk management.

This thesis aims to gain further understanding of stock returns on two different levels: 1) Further understand distributional properties of returns – so far, there have been various attempts to forecast future returns and future volatility. This thesis aims to explore whether it is possible to predict also realized skewness and kurtosis and gain additional insights into the distribution of returns. 2) Establish, whether current realized skewness or kurtosis can be used in forecasts of future returns or volatility, and whether disregarding skewness and kurtosis has a detrimental effect on the quality of such predictions.

The intuition behind the use of skewness and kurtosis in the financial market modelling is, that volatility should not be the only measure of risk on the market. Two distributions with same mean and variance might have completely different kurtosis: thus, when speaking about returns, the two distributions also carry a different amount of risk as a high kurtosis increases the uncertainty about the actual returns. Moreover, the distribution of returns need not be symmetric, thus making positive returns more or less likely than negative returns. Thus, additional knowledge about two moments might be very helpful in investors’ decision making.

The current literature - such as Amaya et al (2015) or Chang et al (2013) - mostly focused on cross-sectional analysis of returns and their realized moments. It is the goal of this thesis to address also the time-series forecasting of future returns using the realized moments.

Furthermore, Corsi and Reno (2009) use HAR with heterogeneous leverage and jumps to model volatility. It seems natural to ask, whether inclusion of realized skewness and kurtosis in the model would have a significant positive impact on the said model performance.

Hypotheses:

1. Hypothesis #1: realized moments can be used to forecast future returns.
2. Hypothesis #2: current realized moments are not independent of past realized moments
3. Hypothesis #3: higher kurtosis should be compensated by higher expected return
4. Hypothesis#4: realized moments improve HAR’s performance in 1 day ahead volatility forecasting
5. Hypothesis #5: realized moments improve HAR’s performance in multi-period volatility forecasts

Methodology:

The current plan is to use 5-minute intraday data for 29 most liquid US companies spanning the years 2005-2015. This dataset includes information about the prices in each of the time intervals. From this information, log returns will be calculated and subsequently used to calculate the realized measures.

The above-mentioned measures will be used for the analysis of stock market returns as described in the Motivation section of this proposal. The individual time series will be modelled using these measures and the validity of the models will be evaluated using the out of sample forecast together with a benchmarking against some of the other techniques used in financial time-series modelling, such as ARIMA or HAR for log-returns and GARCH or HAR for volatility. Where applicable, robust statistical methods shall be used to verify the estimation results. While the robust methods in time-series setting might not be wide-spread, there have been some applications even in the volatility modelling (Croux et al. (2011))

One additional possible technique to model the joint fluctuation of returns, volatility, skewness and kurtosis might be a VAR model, but I will have to explore this option further in the process of working on the thesis.
Expected Contribution:
This thesis hope to provide further insights into the behavior of stock market returns. If the results of the research suggest that the realized measures are useful for the analysis of stock returns, these measures might be used for investment decisions and risk management. Otherwise, it will be evident that some different approaches should be explored.

Outline:

1. Introduction
2. Literature Review
3. Data
4. Methodology
5. Results
6. Conclusion
 
Univerzita Karlova | Informační systém UK