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16
Value–at–Risk Prediction: A Comparison of Alternative Strategies
 J. Financ. Econometr
"... Given thegrowingneed formanaging financial risk, riskpredictionplays an increasing role inbanking and finance. In this studywecompare theoutofsample performance of existing methods and some new models for predicting valueatrisk (VaR) in a univariate context. Usingmore than 30 years of the daily ..."
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Cited by 17 (1 self)
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Given thegrowingneed formanaging financial risk, riskpredictionplays an increasing role inbanking and finance. In this studywecompare theoutofsample performance of existing methods and some new models for predicting valueatrisk (VaR) in a univariate context. Usingmore than 30 years of the daily return data on theNASDAQ Composite Index, we find that most approaches perform inadequately, although several models are acceptable under current regulatory assessment rules for model adequacy. A hybrid method, combining a heavytailed generalized autoregressive conditionally heteroskedastic (GARCH) filter with an extreme value theorybased approach, performs best overall, closely followed by a variant on a filtered historical simulation, and a newmodel based on heteroskedastic mixture distributions. Conditional autoregressive VaR (CAViaR) models perform inadequately, though an extension to a particular CAViaR model is shown to outperform the others.
Stochastic Volatility in General Equilibrium,”working paper
, 2005
"... The connections between stock market volatility and returns are studied within the context of a general equilibrium framework. The framework rules out a priori any purely statistical relationship between volatility and returns by imposing uncorrelated innovations. The main model generates a twofact ..."
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Cited by 16 (1 self)
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The connections between stock market volatility and returns are studied within the context of a general equilibrium framework. The framework rules out a priori any purely statistical relationship between volatility and returns by imposing uncorrelated innovations. The main model generates a twofactor structure for stock market volatility along with timevarying risk premiums on consumption and volatility risk. It also generates endogenously a dynamic leverage effect (volatility asymmetry), the sign of which depends upon the magnitudes of the risk aversion and the intertemporal elasticity of substitution parameters.
TimeVarying Quantiles
, 2006
"... A timevarying quantile can be
tted to a sequence of observations by formulating a state space model and iteratively applying a suitably modi
ed signal extraction algorithm. Quantiles estimated in this way provide information on various aspects of a time series, including dispersion, asymmetry an ..."
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Cited by 4 (0 self)
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A timevarying quantile can be
tted to a sequence of observations by formulating a state space model and iteratively applying a suitably modi
ed signal extraction algorithm. Quantiles estimated in this way provide information on various aspects of a time series, including dispersion, asymmetry and, for
nancial applications, value at risk. Estimates of the quantiles at the end of the series are the basis for forecasting. As such they o¤er an alternative to conditional quantile autoregressions and, at the same time, give some insight into their structure and potential drawbacks.
Continuous time approximations to GARCH and stochastic volatility models
 AND MIKOSCH, TH. (EDS.), HANDBOOK OF FINANCIAL TIME SERIES
, 2008
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Stochastic Volatility Models for Ordinal Valued Time Series with Application to Finance
"... In this paper we introduce a new class of models, called OSV, by combining an ordinal response model and the idea of stochastic volatility. Corresponding time series occur in highfrequency finance when the stocks are traded on a coarse grid. For parameter estimation we develop an efficient Grouped ..."
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Cited by 3 (3 self)
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In this paper we introduce a new class of models, called OSV, by combining an ordinal response model and the idea of stochastic volatility. Corresponding time series occur in highfrequency finance when the stocks are traded on a coarse grid. For parameter estimation we develop an efficient Grouped Move Multigrid Monte Carlo (GMMGMC) sampler. This sampler is based on a scale transformation group, whose elements operate on the random samples of a certain conditional distribution. Also volatility estimates are provided. For illustration, we apply our new model class to price changes of the IBM stock. Dependencies on covariates are quantified and compared with theoretical results for such processes.
Modelling U.K. Inflation Uncertainty 1958−2006
, 2008
"... Abstract Robert Engle’s celebrated article that introduced the concept of autoregressive conditional heteroskedasticity (ARCH) included an application to UK inflation, 195877. This paper updates the estimation of his model and investigates its stability in the light of the well documented changes i ..."
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Abstract Robert Engle’s celebrated article that introduced the concept of autoregressive conditional heteroskedasticity (ARCH) included an application to UK inflation, 195877. This paper updates the estimation of his model and investigates its stability in the light of the well documented changes in policy towards inflation, 19582006. A simple autoregressive model with structural breaks in mean and variance, constant within subperiods (and with no unit roots), provides a preferred representation of the observed heteroskedasticity. Several measures of inflation forecast uncertainty are presented; these illustrate the difficulties presented by instability, not only for point forecasts but also, receiving increased attention nowadays, their uncertainty.
Modelbased measurement of actual volatility in highfrequency data
"... Please send questions and/or remarks of nonscientific ..."
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Time reversal invariance in finance
, 2007
"... Time reversal invariance can be summarised as follows: no difference can be measured if a sequence of events is run forward or backward in time. Because price time series are dominated by a randomness that hides possible structures and orders, the existence of time reversal invariance requires care ..."
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Time reversal invariance can be summarised as follows: no difference can be measured if a sequence of events is run forward or backward in time. Because price time series are dominated by a randomness that hides possible structures and orders, the existence of time reversal invariance requires care to be investigated. Different statistics are constructed with the property to be zero for time series which are time reversal invariant; they all show that highfrequency empirical foreign exchange prices are not invariant. The same statistics are applied to mathematical processes that should mimic empirical prices. Monte Carlo simulations show that only some ARCH processes with a multitimescales structure can reproduce the empirical findings. A GARCH(1,1) process can only reproduce some asymmetry. On the other hand, all the stochastic volatility type processes are time reversal invariant. This clear difference related to the process structures gives some strong selection criterion for processes.
Unobserved Components Models in Economics and Finance THE ROLE OF THE KALMAN FILTER IN TIME SERIES ECONOMETRICS
"... Economic time series display features such as trend, seasonal, and cycle that we do not observe directly from the data. The cycle is of particular interest to economists as it is a measure of the fluctuations in economic activity. An unobserved components model attempts to capture the features of a ..."
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Economic time series display features such as trend, seasonal, and cycle that we do not observe directly from the data. The cycle is of particular interest to economists as it is a measure of the fluctuations in economic activity. An unobserved components model attempts to capture the features of a time series by assuming that they follow stochastic processes that, when put together, yield the observations. The aim of this article is thus to illustrate the use of unobserved components models in economics and finance and to show how they can be used for forecasting and policy making. Setting up models in terms of components of interest helps in model building; see the discussions in [1] and [2] for a comparison with alternative approaches. A detailed treatment of unobserved components models is given in [3]. The statistical treatment of unobserved components models is based on the statespace form. The unobserved Digital Object Identifier 10.1109/MCS.2009.934465 components, which depend on the state vector, are related to the observations by a measurement equation. The Kalman filter is the basic recursion for estimating the state, and hence the unobserved components, in a linear statespace model (see “Kalman Filter”). The estimates, which are based on current and past observations, can be used to make predictions. Backward recursions yield smoothed estimates of components at each point in time based on past, current, and future observations. A set of onestepahead prediction errors, called innovations, is produced by the Kalman filter. In a Gaussian model, the innovations can be used to construct a likelihood function that can be maximized numerically with respect to unknown parameters in the system; see [4]. Once the parameters are estimated, the innovations can be used to construct test statistics that are designed to assess how well the model fits. The STAMP package [5] embodies a modelbuilding procedure in which test statistics are produced as part of the output.