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Application of Iterated Filtering for Parametric Estimation of Instantaneous Variance in the Case of Non-Gaussian Ornstein-Uhlenbeck Stochastic Volatility Processes

100%
Szczepocki P.
Przegląd Statystyczny
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2019
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vol. 66
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issue 1
51-68
EN
The article presents a method for parametric estimation of instantaneous variance in the case of non-Gaussian Ornstein-Uhlenbeck stochastic volatility process by means of the iterated filtering and realized variance estimator. The method is applied to realized variance of S&P500 index data. Empirical application is accompanied with simulation study to examine performance of the estimation technique.
2
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Application of iterated filtering to stochastic volatility models based on non-Gaussian Ornstein-Uhlenbeck process

100%
Szczepocki P.
Statistics in Transition new series
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2020
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vol. 21
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issue 2
173-187
EN
Barndorff-Nielsen and Shephard (2001) proposed a class of stochastic volatility models in which the volatility follows the Ornstein-Uhlenbeck process driven by a positive Levy process without the Gaussian component. The parameter estimation of these models is challenging because the likelihood function is not available in a closed-form expression. A large number of estimation techniques have been proposed, mainly based on Bayesian inference. The main aim of the paper is to present an application of iterated filtering for parameter estimation of such models. Iterated filtering is a method for maximum likelihood inference based on a series of filtering operations, which provide a sequence of parameter estimates that converges to the maximum likelihood estimate. An application to S&P500 index data shows the model perform well and diagnostic plots for iterated filtering ensure convergence iterated filtering to maximum likelihood estimates. Empirical application is accompanied by a simulation study that confirms the validity of the approach in the case of Barndorff-Nielsen and Shephard's stochastic volatility models.
3
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On detection of homogeneous segments of observations in financial time series

100%
Sarnowski W., Wrocław University of Technology I. o. M. a. I.
Acta Universitatis Lodziensis. Folia Oeconomica
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2008
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vol. 216
EN
The aim of this article is to present financial data modelling in presence of stochastic disorders. Change-point analysis is applied. We adapt universal method of change-point detection for disorder in parameters of GARCH processes. A comparison of the model fitted to whole sample with models built on homogenous data subset is made.
PL
Praca podejmuje zagadnienie modelowania finansowych szeregów czasowych w obecności rozregulowań struktury probabilistycznej. Zmiany wykrywane są za pomocą uniwersalnej metody detekcji zaadaptowanej do wykrywania rozregulowań w parametrach procesów typu GARCH. Przeprowadzona została statystyczna analiza jakości modeli uwzględniających wykryte zaburzenia z modelami, które zakładają iż ciąg danych ma jednorodną strukturę probabilistyczną.
4
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One-Period Joint Forecasts of Polish Inflation, Unemployment and Interest Rate Using Bayesian VEC-MSF Models

88%
Pajor A.
Central European Journal of Economic Modelling and Econometrics
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2019
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issue 1
5
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Bayesian SVLEDEJ Model for Detecting Jumps in Logarithmic Growth Rates of One Month Forward Gas Contract Prices

75%
Kostrzewski M.
Central European Journal of Economic Modelling and Econometrics
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2016
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issue 3
6
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A Bayesian Analysis of Exogeneity in Models with Latent Variables

75%
Pajor A.
Central European Journal of Economic Modelling and Econometrics
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2011
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issue 2
7
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Bayesian Analysis of a Regime Switching in-Mean Effect for the Polish Stock Market

75%
Kwiatkowski Ł.
Central European Journal of Economic Modelling and Econometrics
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2011
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issue 4
8
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The Bayesian Methods of Jump Detection: The Example of Gas and EUA Contract Prices

75%
Kostrzewski M.
Central European Journal of Economic Modelling and Econometrics
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2019
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issue 2
9
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Which Option Pricing Model Is the Best? HF Data for Nikkei 225 Index Options

75%
Kokoszczyński R., Sakowski P., Ślepaczuk R.
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2017
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vol. 4
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issue 51
EN
In this study, we analyse the performance of option pricing models using 5-minutes transactional data for the Japanese Nikkei 225 index options. We compare 6 different option pricing models: the Black (1976) model with different assumptions about the volatility process (realized volatility with and without smoothing, historical volatility and implied volatility), the stochastic volatility model of Heston (1993) and the GARCH(1,1) model. To assess the model performance, we use median absolute percentage error based on differences between theoretical and transactional options prices. We present our results with respect to 5 classes of option moneyness, 5 classes of option time to maturity and 2 option types (calls and puts). The Black model with implied volatility (BIV) comes as the best and the GARCH(1,1) as the worst one. For both call and put options, we observe the clear relation between average pricing errors and option moneyness: high error values for deep OTM options and the best fit for deep ITM options. Pricing errors also depend on time to maturity, although this relationship depend on option moneyness. For low value options (deep OTM and OTM), we obtained lower errors for longer maturities. On the other hand, for high value options (ITM and deep ITM) pricing errors are lower for short times to maturity. We obtained similar average pricing errors for call and put options. Moreover, we do not see any advantage of much complex and time-consuming models. Additionally, we describe liquidity of the Nikkei225 option pricing market and try to compare the results we obtain here with a detailed study for Polish emerging option market (Kokoszczyński et al. 2010b).
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