Volatility and Skewness Spillover Effects: Multiresolution Analysis
Název práce v češtině: | Míra Přelévání Volatility a Šikmosti: Multirozkladová analýza |
---|---|
Název v anglickém jazyce: | Volatility and Skewness Spillover Effects: Multiresolution Analysis |
Klíčová slova: | Volatilita, Šikmost, Přelévání, Waveletová analýza |
Klíčová slova anglicky: | Volatility , Skewness, Spillovers, Wavelet analysis |
Akademický rok vypsání: | 2019/2020 |
Typ práce: | diplomová práce |
Jazyk práce: | angličtina |
Ústav: | Institut ekonomických studií (23-IES) |
Vedoucí / školitel: | Mgr. Lukáš Vácha, Ph.D. |
Řešitel: | skrytý - zadáno a potvrzeno stud. odd. |
Datum přihlášení: | 18.10.2019 |
Datum zadání: | 18.10.2019 |
Datum potvrzení stud. oddělením: | 18.10.2019 |
Datum a čas obhajoby: | 05.02.2020 09:00 |
Místo konání obhajoby: | Opletalova - Opletalova 26, O206, Opletalova - místn. č. 206 |
Datum odevzdání elektronické podoby: | 08.01.2020 |
Datum proběhlé obhajoby: | 05.02.2020 |
Oponenti: | doc. PhDr. Jozef Baruník, Ph.D. |
Kontrola URKUND: |
Seznam odborné literatury |
Aloui, C. & B. Hkiri (2014): “Co-movements of gcc emerging stock markets:
New evidence from wavelet coherence analysis.” Economic Modelling 36: pp. 421–431. Amaya, D., P. Christoffersen, K. Jacobs, & A. Vasquez (2015): “Does realized skewness predict the cross-section of equity returns?” Journal of Financial Economics 118(1): pp. 135–167. Andersen, T. G., T. Bollerslev, F. X. Diebold, & H. Ebens (2001): “The distribution of realized stock return volatility.” Journal of financial economics 61(1): pp. 43–76. Ané, T. & H. Geman (2000): “Order flow, transaction clock, and normality of asset returns.” The Journal of Finance 55(5): pp. 2259–2284. Ang, A. & G. Bekaert (2002): “International asset allocation with regime shifts.” The Review of Financial Studies 15(4): pp. 1137–1187. Ang, A. & J. Chen (2002): “Asymmetric correlations of equity portfolios.” Journal of financial Economics 63(3): pp. 443–494. Antonakakis, N., I. Chatziantoniou, & G. Filis (2014): “Dynamic spillovers of oil price shocks and economic policy uncertainty.” Energy Economics 44: pp. 433–447. Arouri, M. E. H., J. Jouini, & D. K. Nguyen (2012): “On the impacts of oil price fluctuations on european equity markets: Volatility spillover and hedging effectiveness.” Energy Economics 34(2): pp. 611–617. Awartani, B. & A. I. Maghyereh (2013): “Dynamic spillovers between oil and stock markets in the gulf cooperation council countries.” Energy Economics 36: pp. 28–42. Ayala, A. & S. Blazsek (2018): “Equity market neutral hedge funds and the stock market: an application of score-driven copula models.” Applied Economics 50(37): pp. 4005–4023. Baker, S. R., N. Bloom, & S. J. Davis (2016): “Measuring economic policy uncertainty.” The quarterly journal of economics 131(4): pp. 1593–1636. Barberis, N. & R. Thaler (2003): “A survey of behavioral finance.” Handbook of the Economics of Finance 1: pp. 1053–1128. Barndorff-Nielsen, O. E., S. Kinnebrock, & N. Shephard (2008): “Measuring downside risk-realised semivariance.” CREATES Research Paper (2008-42). Barndorff-Nielsen, O. E. & N. Shephard (2002): “Estimating quadratic variation using realized variance.” Journal of Applied econometrics 17(5): pp. 457–477. Barndorff-Nielsen, O. E. & N. Shephard (2004): “Measuring the impact of jumps in multivariate price processes using bipower covariation.” Technical report, Discussion paper, Nuffield College, Oxford University. Bartram, S. M., S. J. Taylor, & Y.-H. Wang (2007): “The euro and european financial market dependence.” Journal of Banking & Finance 31(5): pp. 1461–1481. Baruník, J. & E. Kočenda (2019): “Total, asymmetric and frequency connectedness between oil and forex markets.” . Barunik, J., E. Kočenda, & L. Vácha (2015): “Volatility spillovers across petroleum markets.” The Energy Journal pp. 309–329. Baruník, J., E. Kočenda, & L. Vácha (2016a): “Asymmetric connectedness on the us stock market: Bad and good volatility spillovers.” Journal of Financial Markets 27: pp. 55–78. Baruník, J., E. Kočenda, & L. Vácha (2016b): “Gold, oil, and stocks: Dynamic correlations.” International Review of Economics & Finance 42: pp. 186–201. Baruník, J., E. Kočenda, & L. Vácha (2017): “Asymmetric volatility connectedness on the forex market.” Journal of International Money and Finance 77: pp. 39–56. Baruník, J. & T. Křehlík (2018): “Measuring the frequency dynamics of financial connectedness and systemic risk.” Journal of Financial Econometrics 16(2): pp. 271–296. Baruník, J., L. Vácha, & L. Krištoufek (2011): “Comovement of central european stock markets using wavelet coherence: Evidence from highfrequency data.” Technical report, IES Working Paper. Basta, M. (2000): Wavelet Transformand its Application in the Analysis of Economic and Financial Time Series. Ph.D. thesis, PhD Thesis, Ph. D. dissertation, University of Economics, Prague. Bekaert, G. & C. R. Harvey (1997): “Emerging equity market volatility.” Journal of Financial economics 43(1): pp. 29–77. Bekaert, G., R. J. Hodrick, & D. A. Marshall (1994): “The implications of first-order risk aversion for asset market risk premiums.” Technical report, National Bureau of Economic Research. Bernardi, M. & L. Catania (2019): “Switching generalized autoregressive score copula models with application to systemic risk.” Journal of Applied Econometrics 34(1): pp. 43–65. Black, F. (1976): “The pricing of commodity contracts.” Journal of financial economics 3(1-2): pp. 167–179. Blasques, F., S. J. Koopman, & A. Lucas (2014): “Maximum likelihood estimation for generalized autoregressive score models.” Technical report, Tinbergen Institute Discussion Paper. Boldanov, R., S. Degiannakis, & G. Filis (2016): “Time-varying correlation between oil and stock market volatilities: Evidence from oil-importing and oil-exporting countries.” International Review of Financial Analysis 48: pp. 209–220. Bollerslev, T. (1986): “Generalized autoregressive conditional heteroskedasticity.” Journal of econometrics 31(3): pp. 307–327. Bollerslev, T. et al. (1990): “Modelling the coherence in short-run nominal exchange rates: a multivariate generalized arch model.” Review of Economics and statistics 72(3): pp. 498–505. Boubaker, H. & S. A. Raza (2017): “A wavelet analysis of mean and volatility spillovers between oil and brics stock markets.” Energy Economics 64: pp. 105–117. Bredin, D., T. Conlon, & V. Potì (2015): “Does gold glitter in the longrun? gold as a hedge and safe haven across time and investment horizon.” International Review of Financial Analysis 41: pp. 320–328. Brooks, C., S. P. Burke, S. Heravi, & G. Persand (2005): “Autoregressive conditional kurtosis.” Journal of Financial Econometrics 3(3): pp. 399–421. Büyükşahin, B. & M. A. Robe (2014): “Speculators, commodities and crossmarket linkages.” Journal of International Money and Finance 42: pp. 38– 70. Campbell, J. Y. & L. Hentschel (1992): “No news is good news: An asymmetric model of changing volatility in stock returns.” Journal of financial Economics 31(3): pp. 281–318. Cappiello, L., R. F. Engle, & K. Sheppard (2006): “Asymmetric dynamics in the correlations of global equity and bond returns.” Journal of Financial econometrics 4(4): pp. 537–572. Chang, C.-L., M. McAleer, & R. Tansuchat (2013): “Conditional correlations and volatility spillovers between crude oil and stock index returns.” The North American Journal of Economics and Finance 25: pp. 116–138. Chiang, T. C., B. N. Jeon, & H. Li (2007): “Dynamic correlation analysis of financial contagion: Evidence from asian markets.” Journal of International Money and finance 26(7): pp. 1206–1228. Christie, A. A. (1982): “The stochastic behavior of common stock variances: Value, leverage and interest rate effects.” Journal of financial Economics 10(4): pp. 407–432. Cifter, A. (2011): “Value-at-risk estimation with wavelet-based extreme value theory: Evidence from emerging markets.” Physica A: Statistical Mechanics and its Applications 390(12): pp. 2356–2367. Ciner, C. (2001): “Energy shocks and financial markets: nonlinear linkages.” Studies in Nonlinear Dynamics & Econometrics 5(3). Creal, D., S. J. Koopman, & A. Lucas (2013): “Generalized autoregressive score models with applications.” Journal of Applied Econometrics 28(5): pp. 777–795. Creti, A., Z. Ftiti, & K. Guesmi (2014): “Oil price and financial markets: Multivariate dynamic frequency analysis.” Energy policy 73: pp. 245–258. Dacorogna, M. M., U. A. Müller, R. J. Nagler, R. B. Olsen, & O. V. Pictet (1993): “A geographical model for the daily and weekly seasonal volatility in the foreign exchange market.” Journal of International Money and Finance 12(4): pp. 413–438. Dajcman, S., M. Festic, & A. Kavkler (2012): “European stock market comovement dynamics during some major financial market turmoils in the period 1997 to 2010–a comparative dcc-garch and wavelet correlation analysis.” Applied Economics Letters 19(13): pp. 1249–1256. Daubechies, I. (1992): Ten lectures on wavelets, volume 61. Siam. Di Matteo, T. (2007): “Multi-scaling in finance.” Quantitative finance 7(1): pp. 21–36. Dickey, D. A. & W. A. Fuller (1979): “Distribution of the estimators for autoregressive time series with a unit root.” Journal of the American statistical association 74(366a): pp. 427–431. Diebold, F. X. & K. Yilmaz (2009): “Measuring financial asset return and volatility spillovers, with application to global equity markets.” The Economic Journal 119(534): pp. 158–171. Diebold, F. X. & K. Yilmaz (2012): “Better to give than to receive: Predictive directional measurement of volatility spillovers.” International Journal of Forecasting 28(1): pp. 57–66. Égert, B. & E. Kočenda (2007): “Interdependence between eastern and western european stock markets: Evidence from intraday data.” Economic Systems 31(2): pp. 184–203. Égert, B. & E. Kočenda (2011): “Time-varying synchronization of european stock markets.” Empirical Economics 40(2): pp. 393–407. Engle, R. (2002): “Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models.” Journal of Business & Economic Statistics 20(3): pp. 339–350. Engle, R. (2009): Anticipating correlations: a new paradigm for risk management. Princeton University Press. Engle, R. F. & K. F. Kroner (1995): “Multivariate simultaneous generalized arch.” Econometric theory 11(1): pp. 122–150. Engle, R. F. & V. K. Ng (1993): “Measuring and testing the impact of news on volatility.” The journal of finance 48(5): pp. 1749–1778. Engle, R. F. & J. R. Russell (1998): “Autoregressive conditional duration: a new model for irregularly spaced transaction data.” Econometrica pp. 1127–1162. Eun, C. S. & S. Shim (1989): “International transmission of stock market movements.” Journal of financial and quantitative Analysis 24(2): pp. 241– 256. Ewing, B. T. & F. Malik (2013): “Volatility transmission between gold and oil futures under structural breaks.” International Review of Economics & Finance 25: pp. 113–121. Fama, E. F., L. Fisher, M. C. Jensen, & R. Roll (1969): “The adjustment of stock prices to new information.” International economic review 10(1): pp. 1–21. Fama, E. F. & K. R. French (1988): “Dividend yields and expected stock returns.” Journal of financial economics 22(1): pp. 3–25. Fernández, C. & M. F. Steel (1998): “On bayesian modeling of fat tails and skewness.” Journal of the American Statistical Association 93(441): pp. 359–371. Fernandez, V. (2006): “The capm and value at risk at different time-scales.” International Review of Financial Analysis 15(3): pp. 203–219. Fernandez, V. P. (2005): “The international capm and a wavelet-based decomposition of value at risk.” Studies in Nonlinear Dynamics & Econometrics 9(4). Filis, G., S. Degiannakis, & C. Floros (2011): “Dynamic correlation between stock market and oil prices: The case of oil-importing and oil-exporting countries.” International Review of Financial Analysis 20(3): pp. 152–164. French, K. R., G. W. Schwert, & R. F. Stambaugh (1987): “Expected stock returns and volatility.” Journal of financial Economics 19(1): pp. 3–29. Fr`yd, L. (2018): “Asymetrie během finančních krizí: asymetrické volatilita převyšuje døuležitost asymetrické korelace.” Politická ekonomie 66(3): pp. 302–329. Ftiti, Z., K. Guesmi, & I. Abid (2016): “Oil price and stock market comovement: What can we learn from time-scale approaches?” International review of financial analysis 46: pp. 266–280. Galagedera, D. T. U. & E. A. Maharaj (2008): “Wavelet timescales and conditional relationship between higher-order systematic co-moments and portfolio returns.” Quantitative Finance 8(2): pp. 201–215. Gençay, R., F. Selcuk, & B. Whitcher (2005): “Multiscale systematic risk.” Journal of International Money and Finance 24(1): pp. 55–70. Gençay, R., F. Selçuk, & B. J. Whitcher (2001): An introduction to wavelets and other filtering methods in finance and economics. Elsevier. Giot, P. & S. Laurent (2003): “Value-at-risk for long and short trading positions.” Journal of Applied Econometrics 18(6): pp. 641–663. Gjika, D. & R. Horvath (2013): “Stock market comovements in central europe: Evidence from the asymmetric dcc model.” Economic Modelling 33: pp. 55–64. Glosten, L. R., R. Jagannathan, & D. E. Runkle (1993): “On the relation between the expected value and the volatility of the nominal excess return on stocks.” The journal of finance 48(5): pp. 1779–1801. Hansen, B. E. (1994): “Autoregressive conditional density estimation.” International Economic Review pp. 705–730. Harvey, A. C. (2013): Dynamic models for volatility and heavy tails: with applications to financial and economic time series, volume 52. Cambridge University Press. Harvey, C. R. & A. Siddique (1999): “Autoregressive conditional skewness.” Journal of financial and quantitative analysis 34(4): pp. 465–487. Harvey, C. R. & A. Siddique (2000): “Conditional skewness in asset pricing tests.” The Journal of Finance 55(3): pp. 1263–1295. Hashmi, A. R. & A. S. Tay (2007): “Global regional sources of risk in equity markets: Evidence from factor models with time-varying conditional skewness.” Journal of international Money and Finance 26(3): pp. 430–453. Hiemstra, C. & J. D. Jones (1994): “Testing for linear and nonlinear granger causality in the stock price-volume relation.” The Journal of Finance 49(5): pp. 1639–1664. Horvath, R. & P. Poldauf (2012): “International stock market comovements: what happened during the financial crisis?” Global Economy Journal 12(1): p. 1850252. Huang, S., H. An, X. Gao, & X. Sun (2017): “Do oil price asymmetric effects on the stock market persist in multiple time horizons?” Applied energy 185: pp. 1799–1808. In, F., S. Kim, & R. Faff (2010): “Explaining mispricing with fama–french factors: new evidence from the multiscaling approach.” Applied Financial Economics 20(4): pp. 323–330. Jammazi, R. & C. Aloui (2012): “Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling.” Energy Economics 34(3): pp. 828–841. Jondeau, E. & M. Rockinger (2006): “The copula-garch model of conditional dependencies: An international stock market application.” Journal of international money and finance 25(5): pp. 827–853. Kenourgios, D., A. Samitas, & N. Paltalidis (2011): “Financial crises and stock market contagion in a multivariate time-varying asymmetric framework.” Journal of International Financial Markets, Institutions and Money 21(1): pp. 92–106. Khalfaoui, R., M. Boutahar, & H. Boubaker (2015): “Analyzing volatility spillovers and hedging between oil and stock markets: Evidence from wavelet analysis.” Energy Economics 49: pp. 540–549. Koop, G., M. H. Pesaran, & S. M. Potter (1996): “Impulse response analysis in nonlinear multivariate models.” Journal of econometrics 74(1): pp. 119–147. Kraus, A. & R. H. Litzenberger (1976): “Skewness preference and the valuation of risk assets.” The Journal of finance 31(4): pp. 1085–1100. Kristoufek, L. (2013): “Fractal markets hypothesis and the global financial crisis: Wavelet power evidence.” Scientific reports 3: p. 2857. Kroner, K. F. & V. K. Ng (1998): “Modeling asymmetric comovements of asset returns.” The review of financial studies 11(4): pp. 817–844. León, Á., G. Rubio, & G. Serna (2005): “Autoregresive conditional volatility, skewness and kurtosis.” The Quarterly Review of Economics and Finance 45(4-5): pp. 599–618. Ling, S. & M. McAleer (2003): “Asymptotic theory for a vector arma-garch model.” Econometric theory 19(2): pp. 280–310. Liu, X., H. An, S. Huang, & S. Wen (2017): “The evolution of spillover effects between oil and stock markets across multi-scales using a wavelet-based garch–bekk model.” Physica A: Statistical Mechanics and its Applications 465: pp. 374–383. Lo, A. W. & A. C. MacKinlay (1988): “Stock market prices do not follow random walks: Evidence from a simple specification test.” The review of financial studies 1(1): pp. 41–66. Lombardi, M. J. & I. Van Robays (2011): “Do financial investors destabilize the oil price?” . Maghyereh, A. I., B. Awartani, & E. Bouri (2016): “The directional volatility connectedness between crude oil and equity markets: New evidence from implied volatility indexes.” Energy Economics 57: pp. 78–93. Mallat, S. (1999): A wavelet tour of signal processing. Elsevier. Mallat, S. G. (1989): “A theory for multiresolution signal decomposition: the wavelet representation.” IEEE Transactions on Pattern Analysis & Machine Intelligence (7): pp. 674–693. McAleer, M., F. Chan, S. Hoti, & O. Lieberman (2008): “Generalized autoregressive conditional correlation.” Econometric Theory 24(6): pp. 1554– 1583. Mensi, W., M. Beljid, A. Boubaker, & S. Managi (2013): “Correlations and volatility spillovers across commodity and stock markets: Linking energies, food, and gold.” Economic Modelling 32: pp. 15–22. Miller, J. I. & R. A. Ratti (2009): “Crude oil and stock markets: Stability, instability, and bubbles.” Energy Economics 31(4): pp. 559–568. Mitton, T. & K. Vorkink (2007): “Equilibrium underdiversification and the preference for skewness.” The Review of Financial Studies 20(4): pp. 1255–1288. Müller, U. A., M. M. Dacorogna, R. D. Davé, R. B. Olsen, O. V. Pictet, & J. E. Von Weizsäcker (1997): “Volatilities of different time resolutions—analyzing the dynamics of market components.” Journal of Empirical Finance 4(2-3): pp. 213–239. Müller, U. A., M. M. Dacorogna, R. D. Davé, O. V. Pictet, R. B. Olsen, & J. R. Ward (1993): “Fractals and intrinsic time: A challenge to econometricians.” Unpublished manuscript, Olsen & Associates, Zürich . Nelson, D. B. (1991): “Conditional heteroskedasticity in asset returns: A new approach.” Econometrica: Journal of the Econometric Society pp. 347–370. Ng, A. (2000): “Volatility spillover effects from japan and the us to the pacific– basin.” Journal of international money and finance 19(2): pp. 207–233. Niţoi, M. & M. M. Pochea (2019): “Time-varying dependence in european equity markets: A contagion and investor sentiment driven analysis.” Economic Modelling . Patton, A. J. (2006): “Modelling asymmetric exchange rate dependence.” International economic review 47(2): pp. 527–556. Percival, D. B. & A. T. Walden (2000): Wavelet methods for time series analysis, volume 4. Cambridge university press. Percival, D. P. (1995): “On estimation of the wavelet variance.” Biometrika 82(3): pp. 619–631. Pesaran, H. H. & Y. Shin (1998): “Generalized impulse response analysis in linear multivariate models.” Economics letters 58(1): pp. 17–29. Pesaran, M. H. (2015): Time series and panel data econometrics. Oxford University Press. Peters, E. E. (1989): “Fractal structure in the capital markets.” Financial Analysts Journal 45(4): pp. 32–37. Priestley, M. & H. Tong (1973): “On the analysis of bivariate nonstationary processes.” Journal of the Royal Statistical Society: Series B (Methodological) 35(2): pp. 153–166. Ramsey, J. B. (2002): “Wavelets in economics and finance: Past and future.” Studies in Nonlinear Dynamics & Econometrics 6(3). Reboredo, J. C. & M. A. Rivera-Castro (2013): “A wavelet decomposition approach to crude oil price and exchange rate dependence.” Economic Modelling 32: pp. 42–57. Reboredo, J. C. & M. A. Rivera-Castro (2014): “Wavelet-based evidence of the impact of oil prices on stock returns.” International Review of Economics & Finance 29: pp. 145–176. Reinhart, C. M. & K. S. Rogoff (2008): “Is the 2007 us sub-prime financial crisis so different? an international historical comparison.” American Economic Review 98(2): pp. 339–44. Rodriguez, J. C. (2007): “Measuring financial contagion: A copula approach.” Journal of empirical finance 14(3): pp. 401–423. Rubio, F., M. Steel et al. (2015): “Bayesian modelling of skewness and kurtosis with two-piece scale and shape distributions.” Electronic Journal of Statistics 9(2): pp. 1884–1912. Russell, J. R. (1999): “Econometric modeling of multivariate irregularlyspaced high-frequency data.” Manuscript, GSB, University of Chicago . Sadorsky, P. (2012): “Correlations and volatility spillovers between oil prices and the stock prices of clean energy and technology companies.” Energy Economics 34(1): pp. 248–255. Segal, G., I. Shaliastovich, & A. Yaron (2015): “Good and bad uncertainty: Macroeconomic and financial market implications.” Journal of Financial Economics 117(2): pp. 369–397. Sharpe, W. F. (1964): “Capital asset prices: A theory of market equilibrium under conditions of risk.” The journal of finance 19(3): pp. 425–442. Singh, P., B. Kumar, & A. Pandey (2010): “Price and volatility spillovers across north american, european and asian stock markets.” International Review of Financial Analysis 19(1): pp. 55–64. Syllignakis, M. N. & G. P. Kouretas (2011): “Dynamic correlation analysis of financial contagion: Evidence from the central and eastern european markets.” International Review of Economics & Finance 20(4): pp. 717–732. Tan, Z., J. Zhang, J. Wang, & J. Xu (2010): “Day-ahead electricity price forecasting using wavelet transform combined with arima and garch models.” Applied Energy 87(11): pp. 3606–3610. Tang, G. Y. & W. C. Shum (2003): “The relationships between unsystematic risk, skewness and stock returns during up and down markets.” International Business Review 12(5): pp. 523–541. Tang, L.-B., L.-X. Tang, & H.-Y. Sheng (2009): “Forecasting volatility based on wavelet support vector machine.” Expert Systems with Applications 36(2): pp. 2901–2909. Thaler, R. H., A. Tversky, D. Kahneman, & A. Schwartz (1997): “The effect of myopia and loss aversion on risk taking: An experimental test.” The Quarterly Journal of Economics 112(2): pp. 647–661. Torrence, C. & G. P. Compo (1998): “A practical guide to wavelet analysis.” Bulletin of the American Meteorological society 79(1): pp. 61–78. Vacha, L. & J. Barunik (2012): “Co-movement of energy commodities revisited: Evidence from wavelet coherence analysis.” Energy Economics 34(1): pp. 241–247. Vacha, L., K. Janda, L. Kristoufek, & D. Zilberman (2013): “Time– frequency dynamics of biofuel–fuel–food system.” Energy Economics 40: pp. 233–241. Weron, A. & R. Weron (2000): “Fractal market hypothesis and two powerlaws.” Chaos, Solitons & Fractals 11(1-3): pp. 289–296. White, H. (1996): Estimation, inference and specification analysis. 22. Cambridge university press. Wooldridge, J. M. (1994): “Estimation and inference for dependent processes.” Handbook of econometrics 4: pp. 2639–2738. Wu, G. (2001): “The determinants of asymmetric volatility.” The review of financial studies 14(3): pp. 837–859. Yousefi, S., I.Weinreich, & D. Reinarz (2005): “Wavelet-based prediction of oil prices.” Chaos, Solitons & Fractals 25(2): pp. 265–275. Zhang, D. (2008): “Oil shock and economic growth in japan: A nonlinear approach.” Energy Economics 30(5): pp. 2374–2390. Zhang, D. (2017): “Oil shocks and stock markets revisited: measuring connectedness from a global perspective.” Energy Economics 62: pp. 323–333. Zhou, X., W. Zhang, & J. Zhang (2012): “Volatility spillovers between the chinese and world equity markets.” Pacific-Basin Finance Journal 20(2): pp. 247–270. |
Předběžná náplň práce |
Tato práce zkoumá míra přelévání volatility a šikmosti mezi sedmi světovými
akciovými indexy a WTI ropou za předpokladu existence heterogenních investorů. Vzorek dat pokrývá období od ledna 1990 do června 2016. Otázky řešené v práci jsou dvojí. Za prvé, pro obě veličiny – volatilitu i šikmost – se testuje, zdali se míra přelévání liší v závislosti na délce investičních horizontů. Za druhé je testováno, zdali má zahrnutí šikmosti do modelu vliv na odhad míry přelévání volatility. Pro analýzu investičních horizontů je použita waveletová transformace. Odhad podmíněných momentů byl proveden za pomocí modelu GAS, schopného dynamizovat statické parametry zešikmeného Studentova t rozdělení. Empirické výsledky naznačují, že trhy nespojuje pouze přelévání volatility, ale je přítomné i přelevání šikmosti. Důležitým zjištěním je, že zahrnutí šikmosti do modelu nemá vliv na velikost trasmise volatility. Dále bylo zjištěno, že velikost přelevání obou momentů závisí na investičním horizontu, kdy delší investiční horizont je spojen se silnějším efektem. Výsledky zároveň potvrzují, že finační krize v roce 2008 měla zásadní vliv na strukturu finančních trhů. Od roku 2008 je prokazatelně silnější efekt přelévání jak v případě volatility, tak šikmosti, a tento vztah platí i pro dílčí investiční horizonty. |
Předběžná náplň práce v anglickém jazyce |
The thesis investigates volatility and skewness spillover effects among seven
world stock indices and WTI oil under the assumption of the presence of heterogeneous investors. The data sample covers the period from January 1990 to July 2016. The questions addressed in the thesis are twofold: firstly, the dependency of the spillover effect for both the moments—volatility and skewness—on different investments horizons is tested. Further, it is measured whether the inclusion of skewness into has an impact on the volatility spillovers. The decomposition to the different investment horizons is performed by the wavelet transformation. Conditional volatility and skewness were estimated by GAS model, which is capable to dynamize static parameters from Skewed t distribution. Empirical results suggest significant spillover effects from both volatility and skewness. Another important result is that skewness has a non-significant impact on the volatility spillover effects. Further, it has been found that spillover effects for both the moments are time-scale dependent: the higher investment horizons are associated with higher spillover effects. Additionally, our results support the evidence of the significant impact of the financial crisis in 2008 on the structure of markets. From 2008, there are stronger volatility and skewness spillover effects on the aggregated returns as well as decomposed returns. |