6,184 research outputs found
A closer look at the Epps effect
No full textEpps reported empirical evidence that stock correlations decrease when sampling frequency increases. This phenomenon, named Epps effect, has been observed in several markets. In this paper, the dynamics underlying the Epps effect are investigated. Using Monte Carlo simulations and the analysis of high frequency foreign exchange rate and stock price data, it is shown that the Epps effect can largely be explained by two factors: the non-synchronicity of price observations and the existing lead-lag relationship between asset prices. In order to compute co-volatilities, an original method based upon the Fourier analysis is adopted. This method performs well in estimating correlations precisely, as illustrated by simulated experiments. Being naturally embedded in the frequency domain, this estimator is well suited to the study of the Epps effect
Nonparametric estimation of stochastic volatility models
No full textThis letter introduces nonparametric estimators of the drift and diffusion coefficient of stochastic volatility models which exploit techniques for estimating integrated volatility with high-frequency data. The performance of the proposed estimators is assessed on simulations of two popular stochastic volatility models. (c) 2005 Elsevier B.V. All rights reserved
Nonparametric estimation of the diffusion coefficient of stochastic volatility models
No full textIn this paper, new fully nonparametric estimators of the diffusion coefficient of continuous time models are introduced. The estimators are based on Fourier analysis of the state variable trajectory observed and on the estimation of quadratic variation between observations by means of realized volatility. The estimators proposed are shown to be consistent and asymptotically normally distributed. Moreover, the Fourier estimator can be iterated to get a fully nonparametric estimate of the diffusion coefficient in a bivariate model in which one state variable is the volatility of the other. The estimators are shown to be unbiased in small samples using Monte Carlo simulations and are used to estimate univariate and bivariate models for interest rates
Intraday LeBaron effects
No full textWe study the relation at intraday level between serial correlation and volatility of the Standard and Poor (S&P) 500 stock index futures returns. At daily and weekly levels, serial correlation and volatility forecasts have been found to be negatively correlated (LeBaron effect). After finding a significant attenuation of the original effect over time, we show that a similar but more pronounced effect holds by using intraday measures, by such as realized volatility and variance ratio. We also test the impact of unexpected volatility, defined as the part of volatility which cannot be forecasted, on the presence of intraday serial correlation in the time series by employing a model for realized volatility based on the heterogeneous market hypothesis. We find that intraday serial correlation is negatively correlated to volatility forecasts, whereas it is positively correlated to unexpected volatility
Audiomobiles, Sculptures and Conundrums
No full textRoberto Gerhard was a pioneer of electronic music in England creating a number of substantial concert, theatre and radio works from as early as 1954. Gerhard’s electronic music is one of the richest repositories for understanding the development of the composer’s late compositional technique. Apart from the Symphony no.3, ‘Collages’, none of Gerhard’s electronic music is published. This paper will discuss aspects of Gerhard’s electronic music, focusing on Audiomobiles (1958-59) and Sculptures (1963)
Volatility estimate via Fourier analysis
No full textFrom the preface: The aim of this Thesis is to study some selected topics on volatility estimation and modeling. Recently, these topics received great attention in the financial literature, since volatility modeling is crucial in practically all financial applications, including derivatives pricing, portfolio selection and risk management. Specifically, we focus on the concept of realized volatility, which became important in the last decade mainly thanks to the increased availability of high-frequency data on practically every financial asset traded in the main marketplaces. The concept of realized volatility traces back to an early idea of Merton (1980), and basically consists in the estimation of the daily variance via the sum of squared intraday returns, see Andersen et al. (2003). The work presented here is linked to this strand of literature but an alternative estimator is adopted. This is based on Fourier analysis of the time series, hence the term Fourier estimator, which has been recently proposed by Malliavin and Mancino (2002). Moreover, we start from this result to introduce a nonparametric estimator of the diffusion coefficient.
The Thesis has two main objectives. After introducing the concept of quadratic variation and the Fourier estimator, we compare the properties of this estimator with realized volatility in a univariate and multivariate setting. This leads us to some applications in which we exploit the fact that we can regard volatility as an observable instead of a latent variable. We pursue this objective in Chapters 3 and 4. The second objective is to prove two Theorems on the estimation of the diffusion coefficient of a stochastic diffusion in a univariate setting, and this is pursued in Chapter 5. [...
Heston model: shifting on the volatility surface
No full textFitting the implied volatility surface is generally a complicated affair. Here Claudio Pacati, Roberto Renò and Manola Santilli propose a simple extension of the Heston model that allows fast and arbitrage-gree interpolation of the volatility surface with just one time dependent parameter
Arbitrary Initial Term Structure within the CIR Model: A Perturbative Solution
No full textSingle-factor interest rate models with constant coefficients are not consistent with arbitrary initial term structures. An extension which allows both arbitrary initial term structure and analytical tractability has been provided only in the Gaussian case. In this paper, within the context of the HJM methodology, an extension of the CIR model is provided which admits arbitrary initial term structure. It is shown how to calculate bond prices via a perturbative approach, and closed formulas are provided at every order. Since the parameter selected for the expansion is typically estimated to be small, the perturbative approach turns out to be adequate to our purpose. Using results on affine models, the extended CIR model is estimated via maximum likelihood on a time series of daily interest rate yields. Results show that the CIR model has to be rejected with respect to the proposed extension, and it is pointed out that the extended CIR model provides a more flexible characterization of the link between risk neutral and natural probability.
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