Témata prací (Výběr práce)Témata prací (Výběr práce)(verze: 336)
Detail práce
   Přihlásit přes CAS
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.
 
Univerzita Karlova | Informační systém UK