Relevant bibliographies by topics / Volatility estimation

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Volatility estimation'

Author: Grafiati
Published: 4 June 2021
Last updated: 4 March 2023

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Journal articles on the topic "Volatility estimation"

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Mahatma, Yudi, and Ibnu Hadi. "Stochastic Volatility Estimation of Stock Prices using the Ensemble Kalman Filter." InPrime: Indonesian Journal of Pure and Applied Mathematics 3, no. 2 (November 10, 2021): 136–43. http://dx.doi.org/10.15408/inprime.v3i2.20256.

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AbstractVolatility plays important role in options trading. In their seminal paper published in 1973, Black and Scholes assume that the stock price volatility, which is the underlying security volatility of a call option, is constant. But thereafter, researchers found that the return volatility was not constant but conditional to the information set available at the computation time. In this research, we improve a methodology to estimate volatility and interest rate using Ensemble Kalman Filter (EnKF). The price of call and put option used in the observation and the forecasting step of the EnKF algorithm computed using the solution of Black-Scholes PDE. The state-space used in this method is the augmented state space, which consists of static variables: volatility and interest rate, and dynamic variables: call and put option price. The numerical experiment shows that the EnKF algorithm is able to estimate accurately the estimated volatility and interest rates with an RMSE value of 0.0506.Keywords: stochastic volatility; call option; put option; Ensemble Kalman Filter. AbstrakVolatilitas adalah faktor penting dalam perdagangan suatu opsi. Dalam makalahnya yang dipublikasikan tahun 1973, Black dan Scholes mengasumsikan bahwa volatilitas harga saham, yang merupakan volatilitas sekuritas yang mendasari opsi beli, adalah konstan. Akan tetapi, para peneliti menemukan bahwa volatilitas pengembalian tidaklah konstan melainkan tergantung pada kumpulan informasi yang dapat digunakan pada saat perhitungan. Pada penelitian ini dikembangkan metodologi untuk mengestimasi volatilitas dan suku bunga menggunakan metode Ensembel Kalman Filter (EnKF). Harga opsi beli dan opsi jual yang digunakan pada observasi dan pada tahap prakiraan pada algoritma EnKF dihitung menggunakan solusi persamaan Black-Scholes. Ruang keadaan yang digunakan adalah ruang keadaan yang diperluas yang terdiri dari variabel statis yaitu volatilitas dan suku bunga, dan variabel dinamis yaitu harga opsi beli dan harga opsi jual. Eksperimen numerik menunjukkan bahwa algoritma ENKF dapat secara akurat mengestimasi volatiltas dan suku bunga dengan RMSE 0.0506.Kata kunci: volatilitas stokastik; opsi beli; opsi jual; Ensembel Kalman Filter.
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Altin, Hakan. "Volatility Analysis in International Indices." International Journal of Sustainable Economies Management 11, no. 1 (January 1, 2022): 1–17. http://dx.doi.org/10.4018/ijsem.304461.

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Modeling and the estimation of volatility in financial time series are important research subjects for which ARCH family models are recommended. These models are widely used to analyze volatility and manage risk in financial assets. In this study, share indices from the BRIC countries, Europe and the United States were analyzed to determine volatility in international indices. Current data was used to examine the period 1982-2021. Within this framework, the existence of asymmetric information, the leverage effect and the permanence of shocks were examined. Estimation results show the existence of asymmetric information. Bad news affects the system more than good news. The leverage effect is also experienced. Estimation results show that the shocks affecting the system are permanent. At the last stage, static foresight estimations were conducted on the explanatory power of the estimation results. Static foresight estimations present strong and weak evidence together. All parameters are statistically significant.
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Cai, Jingwei. "Nonparametric Range-Based Double Smoothing Spot Volatility Estimation for Diffusion Models." Complexity 2020 (September 21, 2020): 1–7. http://dx.doi.org/10.1155/2020/5048925.

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We consider nonparametric spot volatility estimation for diffusion models with discrete high frequency observations. Our estimator is carried out in two steps. First, using the local average of the range-based variance, we propose a crude estimator of the spot volatility. Second, we use usual nonparametric kernel smoothing to reconstruct the volatility function from the crude estimator. By inference, we find such a double smoothing operation can effectively reduce the estimation error.
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Pandey, Ajay. "Volatility Models and their Performance in Indian Capital Markets." Vikalpa: The Journal for Decision Makers 30, no. 2 (April 2005): 27–46. http://dx.doi.org/10.1177/0256090920050203.

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Estimation and forecasting of volatility of asset returns is important in various applications related to financial markets such as valuation of derivatives, risk management, etc. Till early eighties, it was commonly assumed that the volatility of an asset is constant and estimation procedures were based on this assumption even though some of the pioneering studies on property of stock market returns did not support this assumption. Following the pioneering work of Engle and Bollerslev in eighties on developing models (ARCH/GARCH type models) to capture time-varying characteristics of volatility and other stock return properties, extensive research has been done world over in modeling volatility for estimation and forecasting. There are broadly four possible approaches for estimating and forecasting volatility. These are: Traditional Volatility Estimators— These estimators assume that ‘true’ volatility is unconditional and constant. The estimation is based on either squared returns or standard deviation of returns over a period. Extreme Value Volatility Estimators— These estimators are similar to traditional estimators except that these also incorporate high and low prices observed unlike traditional estimators which are based on closing prices of the asset. Conditional Volatility Models— These models (ARCH/GARCH type models) take into account the time-varying nature of volatility. There have been quite a few extensions of the basic conditional volatility models to incorporate ‘observed’ characteristics of asset/stock returns. Implied Volatility— In case of options, most of the parameters relevant for their valuation can be directly observed or estimated, except volatility. Volatility is, therefore, backed out from the observed option values and is used as volatility forecast. The empirical research across countries and markets has not been equivocal about the effectiveness of using these approaches. This study compares the result of the first three approaches in estimating and forecasting Nifty returns. Based on four different criteria related to bias and efficiency of the various estimators and models, this study analysed the estimation and forecasting ability of three different traditional estimators, four extreme value estimators, and two conditional volatility models. As a benchmark, it used ‘realized’ volatility estimates. The findings of this study are as follows: For estimating the volatility, the extreme value estimators perform better on efficiency criteria that the conditional volatility models. In terms of bias, conditional volatility models perform better than the extreme value estimators. As far as predictive power is concerned, extreme value estimators estimated from sample of length equal to forecast period perform better than the conditional volatility estimators in providing five-day and month ahead volatility forecasts.
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Li, Jia, Viktor Todorov, and George Tauchen. "ESTIMATING THE VOLATILITY OCCUPATION TIME VIA REGULARIZED LAPLACE INVERSION." Econometric Theory 32, no. 5 (May 25, 2015): 1253–88. http://dx.doi.org/10.1017/s0266466615000171.

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We propose a consistent functional estimator for the occupation time of the spot variance of an asset price observed at discrete times on a finite interval with the mesh of the observation grid shrinking to zero. The asset price is modeled nonparametrically as a continuous-time Itô semimartingale with nonvanishing diffusion coefficient. The estimation procedure contains two steps. In the first step we estimate the Laplace transform of the volatility occupation time and, in the second step, we conduct a regularized Laplace inversion. Monte Carlo evidence suggests that the proposed estimator has good small-sample performance and in particular it is far better at estimating lower volatility quantiles and the volatility median than a direct estimator formed from the empirical cumulative distribution function of local spot volatility estimates. An empirical application shows the use of the developed techniques for nonparametric analysis of variation of volatility.
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Van Es, Bert, Peter Spreij, and Harry Van Zanten. "Nonparametric volatility density estimation." Bernoulli 9, no. 3 (June 2003): 451–65. http://dx.doi.org/10.3150/bj/1065444813.

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Kayal, Parthajit, and G. Balasubramanian. "Excess Volatility in Bitcoin: Extreme Value Volatility Estimation." IIM Kozhikode Society & Management Review 10, no. 2 (February 28, 2021): 222–31. http://dx.doi.org/10.1177/2277975220987686.

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This article investigates the excess volatility in Bitcoin prices using an unbiased extreme value volatility estimator. We capture the time-varying nature of the excess volatility using bootstrap, multi-horizon, sub-sampling and rolling-window approaches. We observe that Bitcoin price changes are almost efficient. Although Bitcoin prices exhibit high volatility and show signs of excess volatility for a few periods, it is decreasing over time. After controlling for the outliers, we also notice that the Bitcoin market shows signs of increasing maturity. Overall, Bitcoin prices show a sign of increasing efficiency with decreasing volatility. Our findings have implications for investors making investment decisions and for regulators making policy choices.
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Woerner, Jeannette H. C. "Estimation of integrated volatility in stochastic volatility models." Applied Stochastic Models in Business and Industry 21, no. 1 (January 2005): 27–44. http://dx.doi.org/10.1002/asmb.548.

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Sanfelici, Simona, Imma Valentina Curato, and Maria Elvira Mancino. "High-frequency volatility of volatility estimation free from spot volatility estimates." Quantitative Finance 15, no. 8 (May 11, 2015): 1331–45. http://dx.doi.org/10.1080/14697688.2015.1032542.

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Ghahramani, M., and A. Thavaneswaran. "Nonlinear recursive estimation of volatility via estimating functions." Journal of Statistical Planning and Inference 142, no. 1 (January 2012): 171–80. http://dx.doi.org/10.1016/j.jspi.2011.07.006.

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Dissertations / Theses on the topic "Volatility estimation"

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Sandmann, Gleb. "Stochastic volatility : estimation and empirical validity." Thesis, London School of Economics and Political Science (University of London), 1997. http://etheses.lse.ac.uk/1456/.

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Estimation of stochastic volatility (SV) models is a formidable task because the presence of the latent variable makes the likelihood function difficult to construct. The model can be transformed to a linear state space with non-Gaussian disturbances. Durbin and Koopman (1997) have shown that the likelihood function of the general non-Gaussian state space model can be approximated arbitrarily accurately by decomposing it into a Gaussian part (constructed by the Kalman filter) and a remainder function (whose expectation is evaluated by simulation). This general methodology is specialised to the estimation of SV models. A finite sample simulation experiment illustrates that the resulting Monte Carlo likelihood estimator achieves full efficiency with minimal computational effort. Accurate values of the likelihood function allow inference within the model to be performed by means of likelihood ratio tests. This enables tests for the presence of a unit root in the volatility process to be constructed which are shown to be more powerful than the conventional unit root tests. The second part of the thesis consists of two empirical applications of the SV model. First, the informational content of implied volatility is examined. It is shown that the in- sample evolution of DEM/USD exchange rate volatility can be accurately captured by implied volatility of options. However, better forecasts of ex post volatility can be constructed from the basic SV model. This suggests that options implied volatility may not be market's best forecast of the future asset volatility, as is often assumed. Second, the regulatory claim of a destabilising effect of futures market trading on stock market volatility is critically assessed. It is shown how volume-volatility relationships can be accurately modelled in the SV framework. The variables which approximate the activity in the FT100 index futures market are found to have no influence on the volatility of the underlying stock market index.
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Gu, Ying. "Essays on volatility models using EMM estimation /." Thesis, Connect to this title online; UW restricted, 2006. http://hdl.handle.net/1773/7426.

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Lu, Shan. "Essays on volatility forecasting and density estimation." Thesis, University of Aberdeen, 2019. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=240161.

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This thesis studies two subareas within the forecasting literature: volatility forecasting and risk-neutral density estimation and asks the question of how accurate volatility forecasts and risk-neutral density estimates can be made based on the given information. Two sources of information are employed to make those forecasts: historical information contained in time series of asset prices, and forward-looking information embedded in prices of traded options. Chapter 2 tests the comparative performance of two volatility scaling laws - the square-root-of-time (√T) and an empirical law, TH, characterized by the Hurst exponent (H) - where volatility is measured by sample standard deviation of returns, for forecasting the volatility term structure of crude oil price changes and ten foreign currency changes. We find that the empirical law is overall superior for crude oil, whereas the selection of a superior model is currency-specific and relative performance substantially differs across currencies. Our results are particularly important for regulatory risk management using Value-at-Risk and suggest the use of empirical law for volatility and quantile scaling. Chapter 3 studies the predictive ability of corridor implied volatility (CIV) measure. By adding CIV measures to the modified GARCH specifications, we show that narrow and mid-range CIVs outperform the wide CIVs, market volatility index and the BlackScholes implied volatility for horizons up to 21 days under various market conditions. Results of simulated trading reinforce our statistical findings. Chapter 4 compares six estimation methods for extracting risk-neutral densities (RND) from option prices. By using a pseudo-price based simulation, we find that the positive convolution approximation method provides the best performance, while mixture of two lognormals is the worst; In addition, we show that both price and volatility jumps are important components for option pricing. Our results have practical applications for policymakers as RNDs are important indicators to gauge market sentiment and expectations.
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Marchese, Malvina. "Whittle estimation of multivariate exponential volatility models." Thesis, London School of Economics and Political Science (University of London), 2015. http://etheses.lse.ac.uk/3173/.

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The aim of this thesis is to offer some insights into two topics of some interest for time-series econometric research. The first chapter derives the rates of convergence and the asymptotic normality of the pooled OLS estimators for linear regression panel models with mixed stationary and non-stationary regressors. This work is prompted by the consideration that many economic models of interest present a mixture of I(1) and I(0) regressors, for example models for analysis of demand system or for assessment of the relationship between growth and inequality. We present results for a model where the regressors and the regressand are cointegrated. We find that the OLS estimator is asymptotically normal with convergence rates T p n and p nT for respectively the non-stationary and the stationary regressors. Phillips and Moon (1990) show that in a cointegrated regression model with non-stationary regressors, the OLS estimator converges at a rate of T p n. We find that the presence of one stationary regressor in the model does not increases the rate of convergence. All the results are derived for sequential limits, with T going to infinity followed by n; and under quite restrictive regularity conditions. Chapters 3-5 focus on parametric multivariate exponential volatility models. It has long been recognized that the volatility of stock returns responds differently to good news and bad news. In particular, while negative shocks tend to increase future volatility, positive ones of the same size will increase it by less or even decrease it. This was in fact one of the chief motivations that led Nelson (1991) to introduce the univariate EGARCH model. More recently empirical studies have found that the asymmetry is a robust feature of multivariate stock returns series as well, and several multivariate volatility models have been developed to capture it. Another important property that characterizes the dynamic evolution of volatilities is that squared returns have significant autocorrelations that decay to zero at a slow rate, consistent with the notion of long memory, where the auto-covariances are not absolutely summable. Univariate long-memory volatility models have received a great deal of attention. However, the generalization to a multivariate long-memory volatility model has not been attempted in the literature. Chapter 3 offers a detailed literature review on multivariate volatility models. Chapter 4 and 5 introduce a new multivariate exponential volatility (MEV) model which captures long-range dependence in the volatilities, while retaining the martingale difference assumption and short-memory dependence in mean. Moreover the model captures cross-assets spillover effects, leverage and asymmetry. The strong consistency and the asymptotic normality of the Whittle estimator of the parameters in the Multivariate Exponential Volatility model is established under a variety of parameterization. The results cover both the case of exponentially and hyperbolically decaying coefficients, allowing for different degrees of persistence of shocks to the conditional variances. It is shown that the rate of convergence and the asymptotic normality of the Whittle estimates do not depend on the degree of persistence implied by the parameterization as the Whittle function automatically compensates for the possible lack of square integrability of the model spectral density.
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Zhang, Yuzhao. "Essays on return predictability and volatility estimation." Diss., Restricted to subscribing institutions, 2008. http://proquest.umi.com/pqdweb?did=1666139151&sid=3&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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Luo, Ling. "High Quantile Estimation for some Stochastic Volatility Models." Thèse, Université d'Ottawa / University of Ottawa, 2011. http://hdl.handle.net/10393/20295.

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In this thesis we consider estimation of the tail index for heavy tailed stochastic volatility models with long memory. We prove a central limit theorem for a Hill estimator. In particular, it is shown that neither the rate of convergence nor the asymptotic variance is affected by long memory. The theoretical findings are verified by simulation studies.
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Hawkes, Richard Nathanael. "Linear state models for volatility estimation and prediction." Thesis, Brunel University, 2007. http://bura.brunel.ac.uk/handle/2438/7138.

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This thesis concerns the calibration and estimation of linear state models for forecasting stock return volatility. In the first two chapters I present aspects of financial modelling theory and practice that are of particular relevance to the theme of this present work. In addition to this I review the literature concerning these aspects with a particular emphasis on the area of dynamic volatility models. These chapters set the scene and lay the foundations for subsequent empirical work and are a contribution in themselves. The structure of the models employed in the application chapters 4,5 and 6 is the state-space structure, or alternatively the models are known as unobserved components models. In the literature these models have been applied in the estimation of volatility, both for high frequency and low frequency data. As opposed to what has been carried out in the literature I propose the use of these models with Gaussian components. I suggest the implementation of these for high frequency data for short and medium term forecasting. I then demonstrate the calibration of these models and compare medium term forecasting performance for different forecasting methods and model variations as well as that of GARCH and constant volatility models. I then introduce implied volatility measurements leading to two-state models and verify whether this derivative-based information improves forecasting performance. In chapter 6I compare different unobserved components models' specification and forecasting performance. The appendices contain the extensive workings of the parameter estimates' standard error calculations.
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Wang, Jian. "Real time estimation of multivariate stochastic volatility models." Thesis, University of Sheffield, 2017. http://etheses.whiterose.ac.uk/16786/.

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This thesis firstly considers a modelling framework for multivariate volatility in financial time series. As most financial returns exhibit heavy tails and skewness, we are considering a model for the returns based on the skew-t distribution, while the volatility is assumed to follow a Wishart autoregressive process. We define a new type of Wishart autoregressive process and highlight some of its properties and some of its advantages. Particle filter based inference for this model is discussed and a novel approach of estimating static parameters is provided. Furthermore, an alternative methodology for estimating higher dimension data is developed. Secondly, inspired from the idea of Ulig's Wishart process, a new Wishart-Newton model is developed. The approach combines conjugate Bayesian inference while the hyper parameters are estimated by a Newton-Raphson method and here an online volatility estimate algorithm is proposed. The two proposed models are compared with the benchmarking GO-GARCH model in both function execution time and cumulative returns of different dimensional datasets.
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Eratalay, Mustafa Hakan. "Three essays on multivariate volatility modelling and estimation." Doctoral thesis, Universidad de Alicante, 2012. http://hdl.handle.net/10045/26482.

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White, Scott Ian. "Stochastic volatility: Maximum likelihood estimation and specification testing." Thesis, Queensland University of Technology, 2006. https://eprints.qut.edu.au/16220/1/Scott_White_Thesis.pdf.

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Stochastic volatility (SV) models provide a means of tracking and forecasting the variance of financial asset returns. While SV models have a number of theoretical advantages over competing variance modelling procedures they are notoriously difficult to estimate. The distinguishing feature of the SV estimation literature is that those algorithms that provide accurate parameter estimates are conceptually demanding and require a significant amount of computational resources to implement. Furthermore, although a significant number of distinct SV specifications exist, little attention has been paid to how one would choose the appropriate specification for a given data series. Motivated by these facts, a likelihood based joint estimation and specification testing procedure for SV models is introduced that significantly overcomes the operational issues surrounding existing estimators. The estimation and specification testing procedures in this thesis are made possible by the introduction of a discrete nonlinear filtering (DNF) algorithm. This procedure uses the nonlinear filtering set of equations to provide maximum likelihood estimates for the general class of nonlinear latent variable problems which includes the SV model class. The DNF algorithm provides a fast and accurate implementation of the nonlinear filtering equations by treating the continuously valued state-variable as if it were a discrete Markov variable with a large number of states. When the DNF procedure is applied to the standard SV model, very accurate parameter estimates are obtained. Since the accuracy of the DNF is comparable to other procedures, its advantages are seen as ease and speed of implementation and the provision of online filtering (prediction) of variance. Additionally, the DNF procedure is very flexible and can be used for any dynamic latent variable problem with closed form likelihood and transition functions. Likelihood based specification testing for non-nested SV specifications is undertaken by formulating and estimating an encompassing model that nests two competing SV models. Likelihood ratio statistics are then used to make judgements regarding the optimal SV specification. The proposed framework is applied to SV models that incorporate either extreme returns or asymmetries.
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Books on the topic "Volatility estimation"

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Mancino, Maria Elvira, Maria Cristina Recchioni, and Simona Sanfelici. Fourier-Malliavin Volatility Estimation. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50969-3.

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Bishwal, Jaya P. N. Parameter Estimation in Stochastic Volatility Models. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-03861-7.

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Sandmann, G. Maximum likelihood estimation of stochastic volatility models. London: London School of Economics, Financial Markets Group, 1996.

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Aït-Sahalia, Yacine. Maximum likelihood estimation of stochastic volatility models. Cambridge, MA: National Bureau of Economic Research, 2004.

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Andersen, Torben G. Jump-robust volatility estimation using nearest neighbor truncation. Cambridge, MA: National Bureau of Economic Research, 2009.

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Andersen, Torben G. Jump-robust volatility estimation using nearest neighbor truncation. Cambridge, MA: National Bureau of Economic Research, 2009.

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Alizadeh, Sassan. High- and low-frequency exchange rate volatility dynamics: Range-based estimation of stochastic volatility models. Cambridge, MA: National Bureau of Economic Research, 2001.

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Aït-Sahalia, Yacine. Ultra high frequency volatility estimation with dependent microstructure noise. Cambridge, Mass: National Bureau of Economic Research, 2005.

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Zaffaroni, Paolo. Gaussian estimation of long-rangedependent volatility in asset prices. London: Suntory Centre, 1997.

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Aït-Sahalia, Yacine. Ultra high frequency volatility estimation with dependent microstructure noise. Cambridge, MA: National Bureau of Economic Research, 2005.

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Book chapters on the topic "Volatility estimation"

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Tompkins, Robert. "Volatility Estimation." In Options Explained2, 95–152. London: Palgrave Macmillan UK, 1994. http://dx.doi.org/10.1007/978-1-349-13636-0_4.

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Privault, Nicolas. "Volatility Estimation." In Introduction to Stochastic Finance with Market Examples, 277–98. 2nd ed. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003298670-9.

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Board, John, Alfonso Dufour, Yusuf Hartavi, Charles Sutcliffe, and Stephen Wells. "Volatility Estimation." In Risk and Trading on London’s Alternative Investment Market, 49–53. London: Palgrave Macmillan UK, 2015. http://dx.doi.org/10.1057/9781137361301_7.

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Gugushvili, Shota, Frank van der Meulen, Moritz Schauer, and Peter Spreij. "Nonparametric Bayesian Volatility Estimation." In 2017 MATRIX Annals, 279–302. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04161-8_19.

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Mancino, Maria Elvira, Maria Cristina Recchioni, and Simona Sanfelici. "Estimation of Integrated Volatility." In SpringerBriefs in Quantitative Finance, 13–30. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50969-3_3.

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Mancino, Maria Elvira, Maria Cristina Recchioni, and Simona Sanfelici. "Estimation of Instantaneous Volatility." In SpringerBriefs in Quantitative Finance, 31–47. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50969-3_4.

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Choe, Geon Ho. "Numerical Estimation of Volatility." In Universitext, 457–67. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-25589-7_25.

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Bos, Charles S. "Relating Stochastic Volatility Estimation Methods." In Handbook of Volatility Models and Their Applications, 147–74. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118272039.ch6.

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Bartolucci, Francesco, and Giovanni De Luca. "Estimation of Stochastic Volatility Models." In Applied Optimization, 541–56. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-3613-7_27.

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Tompkins, Robert. "Advanced Issues in Volatility Estimation." In Options Explained2, 153–93. London: Palgrave Macmillan UK, 1994. http://dx.doi.org/10.1007/978-1-349-13636-0_5.

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Conference papers on the topic "Volatility estimation"

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Lleshaj, Llesh. "Volatility Estimation of Euribor and Equilibrium Forecasting." In 7th International Scientific Conference ERAZ - Knowledge Based Sustainable Development. Association of Economists and Managers of the Balkans, Belgrade, Serbia, 2021. http://dx.doi.org/10.31410/eraz.2021.171.

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Euribor rates (Euro Interbank Offered Rate) rates are considered to be the most important reference rates in the European money market. The interest rates do provide the basis for the price and interest rates of all kinds of financial products like interest rate swaps, interest rate futures, saving accounts and mortgages. Since September 2014, this index has per­formed with negative rates. In recent years, several European central banks have imposed negative interest rates on commercial banks, as the only way to stimulate their nations’ economies. Under these circumstances, the purpose of this study is to estimate the gap of the negative rates which are still increasing constantly. This fact puts in question the financial stability in many countries and the effect of monetary policy on stimulating economic growth around European countries. According to the daily data 2016 - 2021, this study has analyzed the volatility of the Euribor index related to efficient market hypothesis and volatility clustering. Applying advanced volatility econometric methods, GARCH volatility models are derived and the long-run equilibrium is predicted. Practical Implications are related to the empiri­cal impacts that ought to be taken into consideration by the banking sector and other financial institutions to make decisions with the Euribor index.
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Choi, Stanley, Gang Dong, and Kin Keung Lai. "Option Implied Volatility Estimation: A Computational Intelligent Approach." In 2011 Fourth International Joint Conference on Computational Sciences and Optimization (CSO). IEEE, 2011. http://dx.doi.org/10.1109/cso.2011.197.

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Shahidi, Reza, and Eric W. Gill. "Volatility Modeling for Ocean Significant Wave Height Estimation." In 2021 IEEE 19th International Symposium on Antenna Technology and Applied Electromagnetics (ANTEM). IEEE, 2021. http://dx.doi.org/10.1109/antem51107.2021.9518814.

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Smith, Robert Elliott, and Muhammad Shakir Hussain. "Hybrid metaheuristic particle filters for stochastic volatility estimation." In the fourteenth international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2330163.2330324.

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Luna, Ivette, and Rosangela Ballini. "Online estimation of stochastic volatility for asset returns." In 2012 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr). IEEE, 2012. http://dx.doi.org/10.1109/cifer.2012.6327788.

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Douglas, Craig C., Hyoseop Lee, and Dongwoo Sheen. "Parallelized Local Volatility Estimation Using GP-GPU Hardware Acceleration." In 2010 International Conference on Information Science and Applications. IEEE, 2010. http://dx.doi.org/10.1109/icisa.2010.5480362.

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"Estimation of NOI Growth, Volatility and Clustering by MSA." In 11th European Real Estate Society Conference: ERES Conference 2004. ERES, 2004. http://dx.doi.org/10.15396/eres2004_523.

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Bahaludin, Hafizah, and Mimi Hafizah Abdullah. "Comparison of volatility function technique for risk-neutral densities estimation." In PROCEEDINGS OF THE 24TH NATIONAL SYMPOSIUM ON MATHEMATICAL SCIENCES: Mathematical Sciences Exploration for the Universal Preservation. Author(s), 2017. http://dx.doi.org/10.1063/1.4995905.

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CHOY, S. T. B., and C. M. CHAN. "BAYESIAN ESTIMATION OF STOCHASTIC VOLATILITY MODEL VIA SCALE MIXTURES DISTRIBUTIONS." In Proceedings of the Hong Kong International Workshop on Statistics in Finance. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2000. http://dx.doi.org/10.1142/9781848160156_0011.

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Troiano, Luigi, Elena Mejuto, and Pravesh Kriplani. "An alternative estimation of market volatility based on fuzzy transform." In 2017 Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS). IEEE, 2017. http://dx.doi.org/10.1109/ifsa-scis.2017.8023316.

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Reports on the topic "Volatility estimation"

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Ait-Sahalia, Yacine, and Robert Kimmel. Maximum Likelihood Estimation of Stochastic Volatility Models. Cambridge, MA: National Bureau of Economic Research, June 2004. http://dx.doi.org/10.3386/w10579.

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Fernández-Villaverde, Jesús, and Juan Rubio-Ramírez. Macroeconomics and Volatility: Data, Models, and Estimation. Cambridge, MA: National Bureau of Economic Research, December 2010. http://dx.doi.org/10.3386/w16618.

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Andersen, Torben, Dobrislav Dobrev, and Ernst Schaumburg. Jump-Robust Volatility Estimation using Nearest Neighbor Truncation. Cambridge, MA: National Bureau of Economic Research, November 2009. http://dx.doi.org/10.3386/w15533.

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Balke, Nathan, and Robert Gordon. The Estimation of Prewar GNP Volatility, 1869-1938. Cambridge, MA: National Bureau of Economic Research, August 1986. http://dx.doi.org/10.3386/w1999.

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Kristensen, Dennis, and Shin Kanaya. Estimation of stochastic volatility models by nonparametric filtering. Institute for Fiscal Studies, March 2015. http://dx.doi.org/10.1920/wp.cem.2015.0915.

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Alizadeh, Sassan, Michael Brandt, and Francis Diebold. High- and Low-Frequency Exchange Rate Volatility Dynamics: Range-Based Estimation of Stochastic Volatility Models. Cambridge, MA: National Bureau of Economic Research, March 2001. http://dx.doi.org/10.3386/w8162.

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Ait-Sahalia, Yacine, Per Mykland, and Lan Zhang. Ultra High Frequency Volatility Estimation with Dependent Microstructure Noise. Cambridge, MA: National Bureau of Economic Research, May 2005. http://dx.doi.org/10.3386/w11380.

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Koo, Bonsoo, and Oliver Linton. Let's get LADE: robust estimation of semiparametric multiplicative volatility models. Institute for Fiscal Studies, March 2013. http://dx.doi.org/10.1920/wp.cem.2013.1113.

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Creal, Drew, and Jing Cynthia Wu. Estimation of Affine Term Structure Models with Spanned or Unspanned Stochastic Volatility. Cambridge, MA: National Bureau of Economic Research, May 2014. http://dx.doi.org/10.3386/w20115.

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Stambaugh, Robert. Estimating Conditional Expectations when Volatility Fluctuates. Cambridge, MA: National Bureau of Economic Research, August 1993. http://dx.doi.org/10.3386/t0140.

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