PL EN


Journal
2016 | 4 (54) | 9-35
Article title

The Polish contribution to financial econometrics. A review of methods and applications

Content
Title variants
PL
Polski wkład w ekonometrię finansową. Przegląd metod i zastosowań
Languages of publication
EN
Abstracts
EN
Since 1982 the term “financial econometrics” has been present in the enormous literature that covers both methodologies and empirical analyses of the processes observed on the financial markets. The purpose of the presented paper is to indicate the milestones in financial econometrics and their usefulness and to show the contribution of the research from Poland into its development. ‘Pure’ financial econometrics methods are of special interest. The paper is directed at reviewing the recent methodologies and their applications. We focused on the contribution of Polish researchers into financial econometrics over the years, considering both the methodology and the applications. Some of the indicated publications are cited quite often, including international quotations, others are not very popular due to the language of the publication or the local reach of the journal, although many of them can be considered in line with the achievements that are presented in international empirical publications.
Journal
Year
Issue
Pages
9-35
Physical description
Contributors
References
  • Acerbi C., 2002, Spectral measures of risk: A coherent representation of subjective risk aversion, Journal of Banking and Finance, 26, pp. 1505-1518.
  • Acerbi C., Tasche D., 2002, On the coherence of expected shortfall, Journal of Banking & Finance, 267, pp. 1487-1503.
  • Adcock C.J., Shutes K., 2012, On the multivariate extended skewnormal, normal-exponential, and normal-gamma distributions, Journal of Statistical Theory and Practice, 6(4), pp. 636-664.
  • Admati A.R., Pfleiderer P., 1988, A theory of intraday patterns: volume and price, The Review of Financial Studies, 1, pp. 3-40.
  • Aït-Sahalia, Y., 2004, Disentangling diffusion from jumps, Journal of Financial Economics, 74(3), pp. 487-528.
  • Ait-Sahalia Y., Hansen L.P. (eds), 2004, Handbook of Financial Econometrics, North-Holland, Elsevier Science.
  • Aït-Sahalia Y., Yu J., 2009, High frequency market microstructure noise estimates and liquidity measures, The Annals of Applied Statistics, 3, pp. 422-457.
  • Aït-Sahalia Y., Mykland P., Zhang L., 2011, Ultra high frequency volatility estimation with dependent microstructure noise, Journal of Econometrics, 160, pp. 160-175.
  • Andersen L., Andersen J., 2000, Jump-diffusion models: volatility smile fitting and numerical methods for pricing, Rev. Derivatives Research, 4, pp. 231-262.
  • Andersen T.G., Bollerslev T., 1997, Heterogeneous information arrivals and return volatility dynamics: uncovering the long-run in high frequency returns, Journal of Finance, 52, pp. 975-1005.
  • Angelidis T., Degiannakis S., 2006, Backtesting VaR Models: An Expected Shortfall Approach, Working Papers, University of Crete, Athens University of Economics and Business.
  • Appelbaum D., 2004, Levy Processes and Stochastic Calculus, Cambridge University Press.
  • Araujo Santos P., Fraga Alves I., Hammoudeh S., 2013, High quantiles estimation with quasi-PORT and DPOT: an application to Value-at-Risk for financial variables, North American Journal of Economics and Finance, 26, pp. 487-496.
  • Artzner P., Delbaen F., Eber J.M., Heath D., 1999, Coherent measures of risk, Mathematical Finance, 9, pp. 203-228.
  • Asai M., McAleer M., Yu J., 2006, Multivariate stochastic volatility: a review, Econometric Reviews, 25, pp. 145-175.
  • Azzalini A., Genton M.G., 2008, Robust likelihood methods based on the skew-t and related distributions, International Statistical Review, 76, pp. 106-129.
  • Baba Y., Engle R.F., Kraft D., Kroner K.F., 1990, Multivariate simultaneous generalized GARCH, UCSD, Department of Economics, mimeo.
  • Bagehot W., 1971, The only game in town, Financial Analyst Journal, 22(27), pp. 12-14.
  • Balkema A.A., De Haan L., 1974, Residual life time at great age, Annals of Probability, 2(5), pp. 792-804.
  • Barndorff-Nielsen O.E., Shephard N., 2006, Econometrics of testing for jumps in financial economics using bipower variation, Journal of Financial Econometrics, 4(1), pp. 1-30.
  • Baruch S., Karolyi A.G., Lemmon M.L., 2007, Multimarket trading and liquidity: theory and evidence, The Journal of Finance, 62(5), pp. 2169-2200.
  • Bauwens L., Giot P., 2000, The logarithmic ACD model: an application to the bid-ask quote process of three NYSE stocks, Annuls of Economy and Statistic, 60, pp. 117-149.
  • Bauwens L., Veredas D., 2004, The stochastic conditional duration model: a latent variable model for the analysis of financial durations, Journal of Econometrics, 119, pp. 381-412.
  • Bauwens L., Laurent S., 2005, A new class of multivariate skew densities, with applications to GARCH Models, Journal of Business and Economic Statistics, 23, pp. 346-354.
  • Będowska-Sójka B., 2014, Wpływ informacji na ceny instrumentów finansowych. Analiza danych śróddziennych, Wyd. Uniwersytetu Ekonomicznego w Poznaniu, Poznań.
  • Będowska-Sójka B., 2015, Daily VaR Forecasts with Realized Volatility and GARCH models, Argumenta Oeconomica, 34, 1, pp. 157-173.
  • Będowska-Sójka B., Kliber A., 2010, Realized volatility versus GARCH and stochastic volatility models. The evidence from the WIG20 index and the EUR/PLN Foreign Exchange Market, Przegląd Statystyczny, 4, pp. 105-127.
  • Bień K., 2006a, Zaawansowane specyfikacje modeli ACD – prezentacja oraz przykład zastosowania, Przegląd Statystyczny, 53(1), pp. 90-108.
  • Bień K., 2006b, Model ACD – podstawowa specyfikacja i przykład zastosowania, Przegląd Statystyczny, 53(3), pp. 83-97.
  • Bień K., Nolte I., Pohlmeier W., 2007, An inflated multivariate integer count hurdle model: an application to bid and ask quote dynamics, Journal of Applied Econometrics, 26, pp. 549-714.
  • Bień K., 2010, Przepływ zleceń a kurs walutowy – badanie mikrostruktury rynku międzybankowego, Bank i Kredyt, 41(5), pp. 5-40.
  • Bień-Barkowska K., 2011, Distribution choice for the asymmetric ACD models, Dynamic Econometric Models, 11, pp. 55-72.
  • Bień-Barkowska K., 2014a, Capturing order book dynamics in the interbank EUR/PLN spot market, Emerging Markets Finance and Trade, 50, pp. 93-117.
  • Bień-Barkowska K., 2014b, Every move you make, every step you take, I’ll be watching you – the quest for hidden orders in the interbank FX spot market, Bank i Kredyt, 45(3), pp. 197-224.
  • Bień-Barkowska K., 2015, Econometric modeling of inter-order durations, Acta Physica Polonica, A, 127, A-7.
  • Bień-Barkowska K., 2016, Mikrostruktura rynku. Ekonometryczne modelowanie dynamiki procesu transakcyjnego, Oficyna Wydawnicza SGH.
  • Bollerslev T., 1986, Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics, 31(3), pp. 307-327.
  • Bollerslev T., 2001, Financial econometrics: Past developments and future challenges, Journal of Econometrics, 100(1), pp. 41-51.
  • Bollerslev T., Chou R.Y., Kroner K.F., 1992, ARCH modelling in finance. A review of the theory and empirical evidence, Journal of Econometrics, 52.
  • Bollerslev T., Mikkelsen H.O., 1996, Modeling and pricing long memory in stock market volatility, Journal of Econometrics, 73(1), pp. 151-184.
  • Boss M., Krenn G., Puhr C., Summer M., 2006, Systemic risk monitor: A model for systemic risk analysis and stress testing of banking systems, Financial Stability Report, 11, pp. 83-95.
  • Borowski K., 2014, Finanse behawioralne. Modele, Diffin, Warszawa.
  • Brownlees Ch., Engle R., Kelly B., 2011/12, A practical guide to volatility forecasting through calm and storm, The Journal of Risk 14(2), pp. 3-22.
  • Brzeszczyński J., Kelm R., 2002, Ekonometryczne modele rynków finansowych. Modele kursów giełdowych i kursów walutowych, WIG Press.
  • Brzeziński J., 2003, Metodologia badań psychologicznych, Wydawnictwo Naukowe PWN.
  • Burzała M., 2014, Wybrane metody badania efektów zarażania na rynku kapitałowym, Wyd. UEP, Poznań.
  • Cai Z., Hong Y., 2009, Some recent developments in nonparametric finance, Advances in Econometrics, 25, pp. 379-432.
  • Campbell J.Y., Lo A.W., MacKinlay A.C., 1997, The Econometrics of Financial Markets, Princeton University Press.
  • Candelon B., Joëts M., Tokpavi S., 2013, Testing for granger causality in distribution tails: an application to oil markets integration, Economic Modelling, 31, pp. 276-285.
  • Caporale G.M., Pittis N., Spagnolo N., 2002, Testing for causality-in-variance: an application the East Asian markets, International Journal of Finance and Economics, 7, pp. 236-245.
  • Chang C.L., Allen D., McAleer M., 2013, Recent developments in financial economics and econometrics: An overview, The North American Journal of Economics and Finance, 26, pp. 217-226.
  • Cherubini U., Luciano E., Vecchiato W., 2006, Copula Methods in Finance, J. Wiley.
  • Cheung Y.W., Ng L.K., 1996, A causality in variance test and its application to financial market prices, Journal of Econometrics, 72, pp. 33-48.
  • Chib S., Nardari F., Shephard N., 2006, Analysis of high dimensional multivariate stochastic volatility models, Journal of Econometrics, 134( 2), pp. 341-371.
  • Chlebus M., 2016, EWS-GARCH: New Regime Switching Approach To Forecast Value-At-Risk, Working Papers 2016-06, Faculty of Economic Sciences, University of Warsaw.
  • Cizek P., Härdle W., Weron R., (eds), 2011, Statistical Tools for Finance and Insurance, Extended and Revised Edition with Codes in Matlab and R, 2nd edition, Springer-Verlag, Berlin.
  • Clark P.K., 1973, A subordinated stochastic process model with fixed variance for speculative prices, Econometrica, 41, pp. 135-156.
  • Cont R., 2001, Empirical properties of asset returns: stylized facts and statistical issues, Quantitative Finance, 1(2), pp. 223-236.
  • Dacey R., Zielonka P., 2008, A detailed prospect theory explanation of the disposition effect, The Journal of Behavioral Finance, 9(1), pp. 43-50.
  • Dacorogna M., Ulrich M., Richard O., Oliveier P., 2001, Defining efficiency in heterogeneous markets, Quantitative Finance, 1, 198-201.
  • Danielsson J., 1994, Stochastic volatility in asset prices: estimation with simulated maximum likelihood, Journal of Econometrics, 64, pp. 375-400.
  • Degiannakis S., Floros C., Livada A., 2012, Evaluating Value-At-Risk models before and after the financial crisis of 2008, Managerial Finance, 38 (4), pp. 436-452.
  • Delavande A. Giné X., McKenzie D., 2010, Measuring Subjective Expectations in Developing Countries: A Critical Review and New Evidence, mimeo.
  • Doman M., 2011, Mikrostruktura giełd papierów wartościowych, Wydawnictwo UE, Poznań.
  • Doman M., Doman R., 2004a, Ekonometryczne modelowanie dynamiki polskiego rynku finansowego, Wyd. AE, Poznań.
  • Doman M., Doman R., 2004b, Stochastic volatility models of exchange rates in Central European countries: effects of exchange rate Regimes, Przegląd Statystyczny, 4, pp. 37-55.
  • Doman M., Doman R., 2004c, Testowanie nieliniowości w zwrotach indeksów GPW w Warszawie, [in:] Matematyka w ekonomii, E. Panek (ed.), Wyd. AE, Poznań, pp. 179-192.
  • Doman M., Doman R., 2005, Dynamika zmienności stóp procentowych WIBOR i WIBID, Zeszyty Naukowe AE Poznań, 52.
  • Doman M., Doman R., 2009a, Forecasting the end-of-the-day realized variance, Metody Ilościowe w Badaniach Ekonomicznych, 10, pp. 67-75.
  • Doman M., Doman R., 2009b, Modelowanie zmienności i ryzyka: metody ekonometrii finansowej, Wolters Kluwer business, Kraków.
  • Doman M., Doman R., 2010, Dependencies between price duration, volatility, volume and return on the Warsaw Stock Exchange, Journal of Modern Accounting and Auditing, 6(10), pp. 27-38.
  • Doman M., Doman R., 2014, Dynamic linkages in the Pairs (GBP/EUR, USD/EUR) and (GBP/USD, EUR/USD): how do they change during a day?, Central European Journal of Economic Modelling and Econometrics (CEJEME), 6(1), pp. 33-56.
  • Doman R., 2011, Zastosowania kopuli w modelowaniu dynamiki zależności na rynkach finansowych, Wydawnictwo UEP, Poznań.
  • Doman R., Doman M., 2010, Copula based impulse response analysis of linkages between stock markets, Available at SSRN: http://ssrn.com/abstract=1615108.
  • Dowd K., Cotter J., Sorwar G., 2008, Spectral risk measures: properties and limitations, Journal of Financial Services Research, 34, pp. 61-75.
  • Easley D., Kiefer N., O’Hara M., Paperman J., 1996, Liquidity. Information and infrequently traded stocks, Journal of Finance, 51, pp. 1405-1436.
  • Easley D., Kiefer N., O’Hara M., 1997, One day in the life of a very common stock, Review of Financial Studies, 10, pp. 805-835.
  • Embrechts P., Klüppelberg C., Mikosch T., 2003, Modelling Extremal Events for Insurance and Finance, Springer, Berlin.
  • Engle R.F., 1982, Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica, 50.
  • Engle R.F., 2000, The econometrics of ultra-high-frequency data, Econometrica, 68, pp. 1-22.
  • Engle R., 2012, Forecasting intraday volatility in the US equity market. Multiplicative component GARCH, Journal of Financial Econometrics, 10(1), pp. 54-83.
  • Engle R.F., Ito T., Lin W-L., 1990, Meteor showers or heat waves? Heterokedastic intra-daily volatility in the foreign exchange market, Econometrica 58, pp. 525-542.
  • Engle R.F., Russell J.R., 1998, Autoregressive conditional duration: A new model for irregularly spaced transaction data, Econometrica, 66, 1127-1162.
  • Engle R.F., 2000, The econometrics of ultra-high-frequency data, Econometrica, vol. 68, pp. 1-22.
  • Engle R.F., Lange J., 1997, Measuring, forecasting and explaining time varying liquidity in the stock market, Tech. Rep. National Bureau of Economic Research.
  • Engle R.F., Russell J.R., 1998, Autoregressive conditional duration: A new model for irregularly spaced transaction data, Econometrica, 66, pp. 1127-1162.
  • Kraft D.F., Engle R.F., 1983, Autoregressive Conditional Heteroskedasticity in Multiple Time Series Models, Department of Economics, UC San Diego, mimeo.
  • Engle R.F., Kroner K.F., 1995, Multivariate simultaneous generalized ARCH, Econometric Theory, 11(01), pp. 122-150.
  • Fałdziński M., 2014, Teoria wartości ekstremalnych w ekonometrii finansowej, Wyd. UMK, Toruń.
  • Fałdziński M., Osińska M., 2016, Volatility estimators in econometric analysis of risk transfer on capital markets, Dynamic Econometric Models, forthcoming.
  • Fałdziński M., Osińska M., Zdanowicz T., 2012a, Econometric analysis of the risk transfer on capital markets. A case of China, Argumenta Oeconomica, 229, pp. 139-164.
  • Fałdziński M., Osińska M., Zdanowicz T., 2012b, Detecting risk transfer in financial markets using different risk measures, Central European Journal of Economic Modelling and Econometrics CEJEME, 14, pp. 45-64.
  • Fama E., 1970, Efficient capital markets: A review of theory and empirical work, Journal of Finance, 25(2), pp. 383-417.
  • Fan J., 2004, An Introduction to Financial Econometrics, Department of Operation Research and Financial Engineering, Princeton University, Princeton.
  • Fan J., 2005, A selective overview of nonparametric methods in financial econometrics, Statistical Science, 20(4), pp. 317-337.
  • Fiszeder P., 2001, Zastosowanie modeli GARCH w analizie krótkookresowych zależności pomiędzy Warszawską Giełdą Papierów Wartościowych a międzynarodowymi rynkami akcji, Przegląd Statystyczny, 48(3-4), pp. 345-364.
  • Fiszeder P., 2009, Modele klasy GARCH w empirycznych badaniach finansowych, UMK, Toruń.
  • Fiszeder P., Perczak G., 2013, A new look at variance estimation based on low, high and closing prices taking into account the drift, Statistica Neerlandica, 67(4), pp. 456-481,
  • Fiszeder P., Perczak G., 2016, Low and high prices can improve volatility forecasts during periods of turmoil, International Journal of Forecasting, pp. 398-410.
  • Fiszeder P., Romański J., 2001, Modelling Polish Stock Returns with GARCH Models, [in:] Macromodels 2000: Proceedings of the Twenty Seventh International Conference Macromodels, W. Welfe (ed.), Absolwent, Łódź.
  • Frank N., 2009, Linkages between Asset Classes During the Financial Crisis. Accounting for Market Microstructure Noise and Non-Synchronous Trading Oxford-Man Institute of Quantitative Finance and Department of Economics, University of Oxford, U.K.
  • Francq C., Zakoian J.M., 2011, GARCH Models: Structure, Statistical Inference and Financial Applications, John Wiley & Sons.
  • Garman M.B., 1976, Market microstructure, Journal of Financial Economics, 1976, vol. 3(3), pp. 257-275.
  • Garman M.B., Klass M.J., 1980, On the estimation of security price volatilities from historical data, Journal of Business, 53, pp. 67-78.
  • Gębka B., Serwa D., 2015, The elusive nature of motives to trade: evidence from international stock markets, International Review of Financial Analysis, 39, pp. 147-157.
  • Giller G.L., 2005, A Generalized Error Distribution, mimeo.
  • Giot P., 2001, Time transformations. Intraday data. and volatility models, Journal of Computational Finance, 4, pp. 93-109.
  • Ghysels E., Jasiak J., 1998, GARCH for irregularly spaced financial data: the ACD-GARCH model, Studies in Nonlinear Dynamics & Econometrics, vol. 2, no. 4. pp. 1-19.
  • Gourieroux C., 1997, ARCH Models and Financial Applications, Springer.
  • Górka, J., 2012, Modele klasy Sign RCA GARCH: własności i zastosowanie w finansach, Wydawnictwo Naukowe Uniwersytetu Mikołaja Kopernika, Toruń.
  • Górka J., Osińska M., 2003, Analiza spektralna stóp zwrotu z inwestycji w akcje, Nasz Rynek Kapitałowy, 3(147), pp. 138-143.
  • Górka J., Osińska M., 2007, Istota procesów STUR i ich identyfikacja, [in:] Procesy STUR: modelowanie i zastosowanie do finansowych szeregów czasowych, M. Osińska (ed.), Towarzystwo Naukowe Organizacji i Kierownictwa „Dom Organizatora”, pp. 35-74.
  • Górka J., Kwiatkowski J., Osińska M., 2007, Zastosowanie procesów STUR do modelowania finansowych szeregów czasowych, [in:] Procesy STUR: modelowanie i zastosowanie do finansowych szeregów czasowych, M. Osińska (ed.), Towarzystwo Naukowe Organizacji i Kierownictwa Dom Organizatora, Toruń, pp. 131-162.
  • Granger C.W.J., 1969, Investigating causal relations by econometric models and cross-spectral methods, Econometrica, 373, pp. 424-438.
  • Granger C., Joyeux R., 1980, An introduction to long-memory time series models and fractional differencing, Journal of Time Series Analysis, 1, pp. 15-29.
  • Granger C.W., Swanson N.R., 1997, An introduction to stochastic unit-root processes, Journal of Econometrics, 80(1), pp. 35-62.
  • Grossman S.J., Stiglitz J.J., 1980, On the Impossibility of Informationally Efficient Markets, American Economic Review, 70, pp. 393-408.
  • Gurgul H., 2012, Analiza zdarzeń na rynkach akcji: wpływ informacji na ceny papierów wartościowych, Wolters Kluwer, Warszawa.
  • Gurgul H., Wójtowicz, T., 2008, FIGARCH models and long memory, Statistics in Transition, 9(2), pp. 297-310.
  • Haffner C.M, Herwatz H., 2004, Testing for causality in variance using multivariate GARCH Models, Metrika, 67, pp. 219-239.
  • Hansen L.P., 2013, Challenges in Identifying and Measuring Systemic Risk, NBER Chapters, [in:] Risk Topography: Systemic Risk and Macro Modeling, pp. 15-30.
  • Hansen P.R., 2005, An test for superior predictive ability, Journal of Business and Economic Statistics, 23, pp. 365-380.
  • Hansen P.R., Lunde A., Nason J.M., 2003, Choosing the best volatility models: the model confidence set approach, Oxford Bulletin of Economics and Statistics, 65, pp. 839-861.
  • Harmantzis F.C., Miao L., Chien Y., 2006, Empirical study of value at risk and expected shortfall models with heavy tails, Journal of Risk Finance, 7(2), pp. 117-126.
  • Hasbrouck J., 2002, Stalking the “efficient price” in market microstructure specifications: an overview, Journal of Financial Markets, 5, pp. 329-339.
  • Heston S.L., 1993, A closed solution for options with stochastic volatility, with application to bond and currency options, Review of Financial Studies, 6(2), pp. 327-343.
  • Hong Y., 2001, A test for volatility spillover with applications to exchange rates, Journal of Econometrics, 103(1-2), pp. 183-224.
  • Hong Y., Liu Y., Wang S., 2009, Granger causality in risk and detection of extreme risk spillover between financial markets, Journal of Econometrics, 150(2), pp. 271-287.
  • Hui H., 2009, Effect of Financial News on Investors – Trading Behavior, City University of Hong Kong WP.
  • Ibrahim B.M., Brzeszczyński J., 2009, Inter-regional and region-specific transmission of international stock market returns: the role of foreign information, Journal of International Money and Finance, 28, pp. 322-343.
  • Jabłecki J., Kokoszczyński R., Sakowski P., Ślepaczuk R., Wójcik P., 2012, Pomiar i modelowanie zmienności – przegląd literatury, Ekonomia, Uniwersytet Warszawski, 31, pp. 22-55.
  • Jajuga K., 2005, Copula approach in two-stock portfolio analysis, Prace Naukowe Akademii Ekonomicznej we Wrocławiu, 1088, pp. 203-209.
  • Jajuga K., 2006, Application of extreme value analysis in portfolio analysis, Prace Naukowe Akademii Ekonomicznej we Wrocławiu, 1133, pp. 130-138.
  • Jajuga K., 2007, 25 lat ekonometrii finansowej, Zeszyty Naukowe Uniwersytetu Szczecińskiego, Finanse. Rynki finansowe. Ubezpieczenia, 6, pp. 91-100.
  • Jajuga K., 2014, Systemic Risk and Financial Stability in Insurance: Macroprudential Policy Concerns, [in:] Macroprudential Supervision in Insurance: Theoretical and Practical Aspects, J. Monkiewicz and M. Małecki (eds), Palgrave Macmillan UK, London, pp. 136-152.
  • Jajuga K., Papla D., 2005, Extreme Value Analysis and Copulas, [in:] Statistical Tools in Finance and Insurance, P. Cizek, W. Hardle, R. Weron (eds.), Springer.
  • Jajuga K., Papla D., 2006, Copula Functions in Model Based Clustering, [in:] From Data and Information Analysis to Knowledge Engineering, Springer, Berlin, pp. 606-613.
  • Kahneman D., Tversky A., 1979, Prospect theory: an analysis of decision under risk, Econometrica, 47(2), pp. 263-292.
  • Kliber A., 2009, Stopy procentowe i kursy walutowe. Zależność i powiązania w gospodarkach środkowoeuropejskich, Wolters Kluwer.
  • Kliber A., 2016, The leverage effect puzzle: the case of European sovereign credit default swap market, Review of Derivatives Research, 19(3), pp. 217-235.
  • Kliber A., Kliber P., 2010, Zależności między kursami walut środkowoeuropejskich w okresie kryzysu 2008, Przegląd Statystyczny, 57(1), pp. 3-16.
  • Kliber A., Łęt B., Rutkowska A., 2016, Socio-demographic characteristics of investors in the Warsaw Stock Exchange – how they influence the investment decision, Bank i Kredyt, 47(2), pp. 91-118.
  • Kliber P., 2013, Zastosowanie procesów dyfuzji ze skokami do modelowania polskiego rynku finansowego, Wydawnictwo Uniwersytetu Ekonomicznego w Poznaniu, Poznań.
  • Kline R.B., 2005, Principles and Practice of Structural Equation Modeling, The Guilford Press.
  • Kokoszczyński R., Sakowski, P., Ślepaczuk, R., 2010, Midquotes or Transactional Data? The Comparison of Black Model on HF Data, Faculty of Economic Sciences Working Papers, University of Warsaw.
  • Konarski R., 2010, Modele równań strukturalnych. Teoria i praktyka, PWN, Warszawa.
  • Kostrzewski M., 2012, On the existence of jumps in financial time series, Acta Physica Polonica B, 10(43), pp. 2001-2021.
  • Kostrzewski M., 2013, Bayesian inference for the jump-diffusion model with M jumps, Communications in Statistics – Theory and Methods, 43(18), pp. 3955-3985.
  • Kostrzewski M., 2015, Bayesian DEJD model and detection of asymmetry in jump sizes, Central European Journal of Economic Modelling and Econometrics (CEJEME), 7(1), pp. 43-70.
  • Kraft D.F., Engle R.F., 1982, Autoregressive Conditional Heteroskedasticity in Multiple Time Series Models, Department of Economics, UC San Diego, mimeo.
  • Kuziak K., 2013, Vulnerability of Copula-VaR to Misspecification of Margins and Dependence Structure, [in:] Algorithms from and for Nature and Life, Lausen B., Van den Poel D., Ultsh A. (eds.), Studies in Classification, Data Analysis, and Knowledge Organization, Springer, pp. 387-395.
  • Kwiatkowski J., 2000, Bayesian analysis of long memory and persistence using ARFIMA models with an application to Polish stock market, Dynamic Econometric Models, 4, pp. 199-210.
  • Kwiatkowski J., Osiewalski J., 2002, Modele ARFIMA: podstawowe własności i analiza bayesowska, Przegląd Statystyczny, 49(2), 105-122.
  • Kyle A., 1985, Continuous auctions and insider trading, Econometrica, 22, pp. 477-498.
  • Kyle A.S., Obizhaeva A.A., 2016, Market microstructure invariance: empirical hypotheses, Econometrica, 84(4), pp. 1345-1404.
  • Lampenius N., Zickar M.J., 2005, Development and validation of a model and measure of financial risk-taking, The Journal of Behavioral Finance, 6.
  • Lua B., Song X., Li X., 2010, Bayesian analysis of multi-group nonlinear structural equation models with application to behavioral finance, Quantitative Finance, 10.
  • Lunde A., Hansen P.R., 2005, A forecast comparison of volatility models: does anything beat a GARCH(1,1)?, Journal of Applied Econometrics, 20(7), pp. 873-889.
  • MacKinlay A.C., 1997, Event studies in economics and finance, Journal of Economic Literature, 35, pp. 13-39.
  • Majewska J., Trzpiot G., 2013, The Power of Tail Independence Tests in Extreme Value Models. An Application for Stock Exchange Markets, Proceedings of the 59th World Statistics Congress of the International Statistical Institute, ISI2013 Hong Kong, The Hague, The Netherlands.
  • Mandelbrot B., 1963, The variation of certain speculative prices, The Journal of Business, 36( 4), pp. 394-419.
  • Manganelli S., 2005, Duration, volume and volatility impact of trades, Journal of Financial Markets, 8, pp. 377- 399.
  • McDonald J.B., Xu Y.J., 1995, A generalization of beta distribution with applications, Journal of Econometrics, 66, pp. 133-152.
  • McNeil J.A., Frey F., 2000, Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach, Journal of Empirical Finance, 7, pp. 271-300.
  • Nelson D., 1991, Conditional heteroskedasticity in asset returns: a new approach, Econometrica, 59, pp. 347-370.
  • Nicholls D.F., Quinn B.G., 2012, Random coefficient autoregressive models: an introduction: an introduction, vol. 11, Springer Science & Business Media.
  • Manganelli S., 2005, Duration, volume and volatility impact of trades, Journal of Financial Markets, 8, pp. 377-399.
  • O’Hara M., 1995, Market Microstructure, Blackwell Publishers, Cambridge, UK.
  • Orzeszko W., 2005, Identyfikacja i prognozowanie chaosu deterministycznego w ekonomicznych szeregach czasowych, Fundacja Promocji i Akredytacji Kierunków Ekonomicznych, Warszawa.
  • Orzeszko W., 2016, Nieparametryczna identyfikacja nieliniowości w finansowych i ekonomicznych szeregach czasowych, Wydawnictwo Naukowe UMK, Toruń.
  • Osiewalski J., 2009, New hybrid models of multivariate volatility a Bayesian perspective, Przegląd Statystyczny, 56, pp. 15-22.
  • Osiewalski J., Osiewalski K., 2012, Missing observations in daily returns – Bayesian inference within the MSF-SBEKK model, Central European Journal of Economic Modelling and Econometrics, 4, pp. 169-197.
  • Osiewalski J., Osiewalski K., 2013, A long-run relationship between daily prices on two markets: the Bayesian VAR2-MSF-SBEKK Model, Central European Journal of Economic Modelling and Econometrics, 5, pp. 65-83.
  • Osiewalski J., Pajor A., 2009, Bayesian analysis for hybrid MSF-SBEKK models of multivariate volatility, Central European Journal of Economic Modelling and Econometrics, vol. 1, issue 2, pp. 179-202.
  • Osiewalski J., Pajor A., 2010, Bayesian Value-at-Risk for a portfolio: multi- and univariate approaches using MSF-SBEKK models, Central European Journal of Economic Modelling and Econometrics, 2(4), pp. 253-277.
  • Osiewalski J., Pajor A., 2012, Bayesian Value-at-Risk and expected shortfall for a large portfolio multi- and univariate approaches, Acta Physica Polonica A(121), B101-B109.
  • Osiewalski J., Pipień M, 2003a, Bayesian analysis and option pricing in univariate GARCH models with asymmetries and GARCH-in-mean effects, Przegląd Statystyczny, 50, pp. 5-29.
  • Osiewalski J., Pipień M., 1999, Bayesowskie testowanie modeli GARCH i IGARCH, Przegląd Statystyczny, 461, pp. 5-23.
  • Osiewalski J., Pipień M., 2002, Multivariate t-GARCH Models: Bayesian analysis for Exchange Rates, Modelling Economies in Transition. Proceedings of the Sixth AMFET Conference, ed. W. Welfe, Łódź, pp. 151-167.
  • Osiewalski J., Pipień M., 2003b, Bayesian estimation of a bivariate BEKK-GARCH process – the conditional ECM framework, Prace Naukowe Akademii Ekonomicznej we Wrocławiu, 1006, pp. 161-172.
  • Osiewalski J., Pipień M., 2004a, Bayesian pricing of an European call option using a GARCH model with asymmetries, Acta Universitatis Lodziensis – Folia Oeconomica, 177, pp. 219-238.
  • Osiewalski J., Pipień M., 2004b, Bayesian comparison of bivariate ARCH-type models for the main exchange rates in Poland, Journal of Econometrics, 123, pp. 371-391.
  • Osińska M., 2006, Ekonometria finansowa, PWE, Warszawa.
  • Osińska M. (ed.), 2007, Procesy STUR – modelowanie i zastosowanie do finansowych szeregów czasowych, Wyd. Dom Organizatora, TNOiK, Toruń.
  • Osińska M., 2008, Ekonometryczna analiza zależności przyczynowych, Wydawnictwo Naukowe UMK, Toruń.
  • Osińska M., 2010, Quality of the forecasts of currencies exchange rates volatilities, International Research Journal of Finance and Economics, 60.
  • Osińska M., 2011, On the interpretation of causality in Granger’s sense, Dynamic Econometric Models, 11, pp. 129-139.
  • Osińska M., Fałdziński M., 2007, GARCH and SV models with application of the extreme value theory, Dynamic Econometric Models, vol. X, Toruń, pp. 45-52.
  • Osińska M., Hyżyk S., 2000, Wykorzystanie zmienności wariancji warunkowej do opisu i prognozowania kursów walutowych, Acta Universitatis Nicolai Copernici, Ekonomia XXX.
  • Osińska M., Kwiatkowski J., 2003, Procesy zawierające stochastyczne pierwiastki jednostkowe – identyfikacja i zastosowanie. Dynamiczne modele ekonometryczne, Wyd. UMK, Toruń.
  • Osińska M., Orzeszko W., 2006, Detecting Nonlinear Causality at Financial Markets, [in:] Financial Markets. Principles of Modeling, Forecasting and Decision-Making, W. Milo, P. Wdowiński (eds), Lodz, pp. 259-276.
  • Osińska M., Pietrzak M.B., Żurek M., 2011, Ocena wpływu czynników behawioralnych i rynkowych na postawy inwestorów indywidualnych na polskim rynku kapitałowym za pomocą modelu SEM, Przegląd Statystyczny, 3-4, pp. 175-194.
  • Osińska M., Romański J., 1994, Econometric Analysis of the Warsaw Stock Exchange, Research memorandum of the ACE Project, University of Leicester.
  • Pajor A., 2003, Procesy zmienności stochastycznej SV w bayesowskiej analizie finansowych szeregów czasowych, Monografie: Prace Doktorskie, nr 2, Wyd. AE w Krakowie.
  • Pajor A., 2010, Wielowymiarowe procesy wariancji stochastycznej w ekonometrii finansowej ujęcie bayesowskie, Wyd. UEK Kraków.
  • Papla D., Piontek K., 2009, Zastosowanie rozkładów a-stabilnych i funkcji powiązań (copula) w obliczaniu wartości zagrożonej (VaR), [in:] Wyzwania współczesnych finansów, K. Jajuga (ed.), Uni- wersytet Ekonomiczny we Wrocławiu, Wrocław.
  • Parkinson M., 1980, The extreme value method for estimating the variance of the rate of return, Journal of Business, 53, pp. 61-65.
  • Pickands J., 1975, Statistical inference using extreme order statistics, Annals of Statistics, 3(1), pp. 119-131.
  • Piontek K., 2004, Zastosowanie modeli klasy ARCH do opisu własności szeregu stóp zwrotu indeksu WIG, Prace Naukowe AE we Wrocławiu nr 1021, Ekonometria, 14, pp. 152-169.
  • Piontek K., 2007a, Pomiar i testowanie skośności rozkładów stóp zwrotu instrumentów finansowych, PN AE we Wrocławiu, 1169, Taksonomia 14, Klasyfikacja i analiza danych – teoria i zastosowania, pp. 122-130.
  • Piontek K., 2007b, Przegląd i porównanie metod oceny modeli VaR Matematyczne i ekonometryczne metody oceny ryzyka finansowego, PN AE w Katowicach, P. Chrzan (ed.), pp. 113-124.
  • Pipień M., 2004a, GARCH Processes with Skewed-t and Stable Conditional Distribution. Dynamic Bayesian Comparison for WIBOR Interest Rates, Proceedings of the 34-th International Conference Macromodels, Welfe A., W. Welfe (eds.), Łódź.
  • Pipień M., 2004b, Procesy GARCH o warunkowym rozkładzie skośnym-t lub a-stabilnym. Bayesowskie porównanie mocy wyjaśniającej, [in:] Metody ilościowe w naukach ekonomicznych, Welfe A. (ed.), Wydawnictwo SGH, Warszawa.
  • Pipień M., 2005a, Procesy GARCH o warunkowym skośnym-t lub a-stabilnym rozkładzie prawdopodobieństwa. Bayesowskie porównanie mocy wyjaśniającej, [in:] Metody ilościowe w naukach ekonomicznych, Piąte warsztaty doktorskie z zakresu Ekonometrii i Statystyki, A. Welfe (ed.), Szkoła Główna Handlowa, Warszawa, pp. 187-211.
  • Pipień M., 2005b, Wykorzystanie rozkładów predyktywnych w prognozie VaR i rezerw kapitałowych związanych z ryzykiem rynkowym, Materiały IX Konferencji „Dynamiczne modele ekonometryczne”, 6-8 września 2005, UMK, Toruń, pp. 83-91.
  • Pipień M., 2006a, Wnioskowanie bayesowskie w ekonometrii finansowej, Seria Specjalna: Monografie nr 176, Wydawnictwo Akademii Ekonomicznej w Krakowie, Kraków.
  • Pipień M., 2006b, Bayesian comparison of GARCH processes with skewness mechanism in conditional distributions, Acta Physica Polonica B. 37(11), pp. 3105-3121.
  • Pipień M., 2007, Bayesowskie porównanie procesów GARCH o rozkładach warunkowych dopuszczających grube ogony i asymetrię, [in:] Metody ilościowe w naukach ekonomicznych, Welfe A. (ed.), Wyd. SGH, Warszawa.
  • Pipień M., 2008a, Introducing skewness into conditionally fat tailed GARCH processes: a Bayesian comparison, Przegląd Statystyczny, 552, pp. 89-116.
  • Pipień M., 2008b, On the empirical importance of the conditional skewness in modelling the relationship between risk and return, Acta Physica Polonica A, 114(3), pp. 517-524.
  • Pluciennik P., 2010, Forecasting financial processes by using diffusion models, Dynamic Econometric Models, 10, pp. 51-60.
  • Pluciennik P., Kliber A., Kliber P., Piwnicka M., Paluszak G., 2013, Wpływ światowego kryzysu gospodarczego 2007-2009 na rynek międzybankowy w Polsce, National Bank of Poland Working Papers, 288, pp. 5-128.
  • Rogers L.C.G., Satchell S.E., 1991, Estimating variance from high, low and closing prices, Annals of Applied Probability, 1, pp. 504-512.
  • Rubaszek M., Serwa D., 2009, Analiza kursu walutowego, [in:] Analiza kursu walutowego, Marcinkowska-Lewandowska (ed.), Wyd. Beck, Warszawa.
  • Ruiz E., 1994, Quasi-maximum likelihood estimation of stochastic volatility models, Journal of Econometrics, 63(1), pp. 289-306.
  • Russell J.R., 1999, Econometric modeling of multivariate irregularly-spaced high-frequency data, Manuscript, GSB, University of Chicago.
  • Stawicki J., 2004, Wykorzystanie łańcuchów Markowa w analizie rynku kapitałowego, Uniwersytet Mikołaja Kopernika, Toruń.
  • Stawicki J., Janiak E., Müller-Frączek I., 1998, Fractional differencing of time series: Hurst exponent, fractal dimension, Dynamic Econometric Models, 3, pp. 17-25.
  • Strawiński P., Ślepaczuk R., 2008, Analysis of high frequency data on the warsaw stock exchange in the context of efficient market hypothesis, Journal of Applied Economic Sciences, 3(5), pp. 306-319.
  • Syczewska E.M., 2007, Ekonometryczne modele kursów walutowych, Monografie i Opracowania, Szkoła Główna Handlowa, Warszawa.
  • Szyszka A., 2009, Finanse behawioralne. Nowe podejście do inwestowania na rynku kapitałowym, Wyd. UEP, Poznań.
  • Ślepaczuk R., Zakrzewski G., 2009, High-Frequency and Model-Free Volatility Estimators, Faculty of Economic Sciences Working Papers, University of Warsaw, 13(23).
  • Talaga L., Zieliński Z., 1986, Analiza spektralna w modelowaniu ekonometrycznym, PWN, Warszawa.
  • Theodossiou P., 1998, Financial data and the skewed generalized T distribution, Management Science, 44, pp. 1650-1661.
  • Trzpiot G., 2012, Ekstremalna regresja kwantylowa, Studia Ekonomiczne Uniwersytetu Ekonomicznego w Katowicach – Zeszyty Naukowe Wydziałowe nr 91, Katowice, pp. 11-20.
  • Tsay R., 2005, Analysis of Financial Time Series, Wiley, New York.
  • Tyszka T., Zielonka P., 2002, Expert judgments: Financial analysts versus weather forecasters, The Journal of Psychology and Financial Markets, 3(3), pp. 152-160
  • Xekalaki E., Degiannakis S., 2009, ARCH models for Financial Applications, Wiley.
  • Wang X.L., Shi K., Fan H.X., 2006, Chinese stock markets, psychological mechanisms, investment behaviors, risk perception, individual investors, Journal of Economic Psychology, 27.
  • Vašíček O., 1977, An equilibrium characterisation of the term structure, Journal of Financial Economics, 5(2), pp. 177-188.
  • Zieliński Z., 1979, Analiza dynamiki i rytmiczności zjawisk gospodarczych, PWN, Warszawa.
  • Zielonka P., 2011, Giełda i psychologia: behawioralne aspekty inwestowania na rynku papierów wartościowych, CeDeWu, Warszawa.
  • Żurek M., 2016, Inklinacje behawioralne na rynkach kapitałowych w świetle modeli SEM, Wydawnictwo Naukowe UMK, Toruń.
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