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Bootstrapping financial time series
- Journal of Economic Surveys
, 2002
"... It is well known that time series of returns are characterized by volatility clus-tering and excess kurtosis. Therefore, when modelling the dynamic behavior of returns, inference and prediction methods, based on independent and/or Gaussian observations may be inadequate. As bootstrap methods are not ..."
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Cited by 15 (3 self)
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It is well known that time series of returns are characterized by volatility clus-tering and excess kurtosis. Therefore, when modelling the dynamic behavior of returns, inference and prediction methods, based on independent and/or Gaussian observations may be inadequate. As bootstrap methods are not, in general, based on any particular assumption on the distribution of the data, they are well suited for the analysis of returns. This paper reviews the appli-cation of bootstrap procedures for inference and prediction of …nancial time series. In relation to inference, bootstrap techniques have been applied to ob-tain the sample distribution of statistics for testing, for example, autoregressive dynamics in the conditional mean and variance, unit roots in the mean, frac-tional integration in volatility and the predictive ability of technical trading rules. On the other hand, bootstrap procedures have been used to estimate the distribution of returns which is of interest, for example, for Value at Risk
Tests for breaks in the conditional co-movements of asset returns. Working paper. http://www.unc.edu/˜eghysels
- Econometrica
, 1998
"... Abstract: We propose procedures designed to uncover structural breaks in the co-movements of financial markets. A reduced form approach is introduced that can be considered as a two-stage method for reducing the dimensionality of multi-variate heteroskedastic conditional volatility models through ma ..."
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Cited by 6 (0 self)
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Abstract: We propose procedures designed to uncover structural breaks in the co-movements of financial markets. A reduced form approach is introduced that can be considered as a two-stage method for reducing the dimensionality of multi-variate heteroskedastic conditional volatility models through marginalization. The main advantage is that one can use returns normalized by volatility filters that are purely data-driven and construct general conditional covariance dynamic spec-ifications. The main thrust of our procedure is to examine change-points in the co-movements of normalized returns. The tests allow for strong and weak depen-dent as well as leptokurtic processes. We document, using a ten year period of two representative high frequency FX series, that regression models with non-Gaussian errors adequately describe their co-movements. Change-points are detected in the conditional covariance of the DM/US $ and YN/US $ normalized returns over the decade 1986-1996. Key words and phrases: Change-point tests, conditional covariance, high-frequency financial data, multivariate GARCH models. 1.
A local dynamic conditional correlation model
, 2006
"... Abstract: This paper introduces the idea that the variances or correlations in financial returns may all change conditionally and slowly over time. A multi-step local dynamic conditional correlation model is proposed for simultaneously modelling these components. In particular, the local and conditi ..."
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Cited by 5 (0 self)
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Abstract: This paper introduces the idea that the variances or correlations in financial returns may all change conditionally and slowly over time. A multi-step local dynamic conditional correlation model is proposed for simultaneously modelling these components. In particular, the local and conditional correlations are jointly estimated by multivariate kernel regression. A multivariate k-NN method with variable bandwidths is developed to solve the curse of dimension problem. Asymptotic properties of the estimators are discussed in detail. Practical performance of the model is illustrated by applications to foreign exchange rates.
MODELLING INTRA-DAILY VOLATILITY BY FUNCTIONAL DATA ANALYSIS: AN EMPIRICAL APPLICATION TO THE SPANISH STOCK MARKET.
, 2009
"... We propose recent functional data analysis techniques to study the intra-daily volatility. In particular, the volatility extraction is based on functional principal components and the volatility prediction on functional AR(1) models. The estimation of the corresponding parameters is carried out usin ..."
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We propose recent functional data analysis techniques to study the intra-daily volatility. In particular, the volatility extraction is based on functional principal components and the volatility prediction on functional AR(1) models. The estimation of the corresponding parameters is carried out using the functional equivalent to OLS. We apply these ideas to the empirical analysis of the IBEX35 returns observed each _ve minutes. We also analyze the performance of the proposed functional AR(1) model to predict the volatility along a given day given the information in previous days for the intra-daily volatility for the firms in the IBEX35 Madrid stocks index.
Investigating Daily Naira/Dollar Exchange Rate Volatility: A Modeling using GARCH and Asymmetric Models
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ABSTRACT FRANCISCO CHAMÚ MORALES: Estimation of Max-Stable Processes Using Monte Carlo Methods with Applications to Financial Risk Assessment
, 2005
"... (Under the direction of Richard L. Smith) Multivariate extreme value theory is concerned with the joint distribution of extremes of multiple random variables. The theory is used in a number of areas such as finance and environmental science. For example, empirical observations suggest that extreme e ..."
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(Under the direction of Richard L. Smith) Multivariate extreme value theory is concerned with the joint distribution of extremes of multiple random variables. The theory is used in a number of areas such as finance and environmental science. For example, empirical observations suggest that extreme events in financial time series occur in clusters and are dependent across different assets. It is possible to characterize the extremal behavior of a multivariate stationary time series in terms of a limiting max-stable process. Our approach for the statistical modeling of max-stable processes is based on Moving Maxima (MM) processes, and a multivariate extension known as Multivariate Maxima of Moving Maxima (M4) processes. This work is concerned with developing Monte Carlo methods for filtering, prediction, and parameter estimation of M4 processes. The model is a state-space representation, where the state is an unobserved M4 process, and the observed process is a nonlinear transformation of the state with additive Gaussian noise. Our contributions can be divided in three areas. First, we show that two special cases of moving maxima processes, which we refer to as MM(1) and MM(2) processes, are second-
AN OVERVIEW OF PROBABILISTIC AND TIME SERIES MODELS IN FINANCE
"... Abstract In this paper, we partially review probabilistic and time series models in finance. Both discrete and continuous-time models are described. The characterization of the No-Arbitrage paradigm is extensively studied in several financial market contexts. As the probabilistic models become more ..."
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Abstract In this paper, we partially review probabilistic and time series models in finance. Both discrete and continuous-time models are described. The characterization of the No-Arbitrage paradigm is extensively studied in several financial market contexts. As the probabilistic models become more and more complex to be realistic, the Econometrics needed to estimate them are more difficult. Conse-quently, there is still much research to be done on the link between probabilistic and time series models.
Bootstrap Prediction for Returns and Volatilities in GARCH Models
"... A new bootstrap procedure to obtain prediction densities of re-turns and volatilities of GARCH processes is proposed. Financial market participants have shown an increasing interest in prediction in-tervals as measures of uncertainty. Furthermore, accurate predictions of volatilities are critical fo ..."
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A new bootstrap procedure to obtain prediction densities of re-turns and volatilities of GARCH processes is proposed. Financial market participants have shown an increasing interest in prediction in-tervals as measures of uncertainty. Furthermore, accurate predictions of volatilities are critical for many financial models. The advantages of the proposed method are that it allows to incorporate parameter un-certainty and does not rely on distributional assumptions. The finite sample properties are analysed by an extensive Monte Carlo simula-tion. Finally, the technique is applied to the Madrid Stock Market index, IBEX-35.
Outliers . . . Autoregressive Heteroscedasticity in Time Series
, 2001
"... This paper reviews the literature on GARCH-type models proposed to represent the dynamic evolution of conditional variances. Effects of level outliers on the diagnostic and estimation of GARCH models are also studied. Both outliers and conditional heteroscedasticity can generate time series with exc ..."
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This paper reviews the literature on GARCH-type models proposed to represent the dynamic evolution of conditional variances. Effects of level outliers on the diagnostic and estimation of GARCH models are also studied. Both outliers and conditional heteroscedasticity can generate time series with excess kurtosis and autocorrelated squared observations. Consequently, both phenomena can be confused. However, since outliers are generated by unexpected events and the conditional variances are predictable, it is important to identify which one is producing the observed features in the data. We compare two alternative procedures for dealing with the simultaneous presence of outliers and conditional heteroscedasticity in time series. The first one is to clean the series of outliers before fitting a GARCH model. The second is to estimate first the GARCH model and then to clean of outliers by using the residuals adjusted by its conditional variance. It is shown that both approaches may result in different estimated conditional variances.
International Review of Financial Analysis
"... The forecasting of large price movements in economics and finance is surely a difficult, while at the same time, central issue in financial management. This explains why the prediction of risk—frequently associated with the prediction of return volatility—has been the subject of a vast number of pap ..."
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The forecasting of large price movements in economics and finance is surely a difficult, while at the same time, central issue in financial management. This explains why the prediction of risk—frequently associated with the prediction of return volatility—has been the subject of a vast number of papers within the empiricalAutoregressive conditional tail behavior and
