Time series models : in econometrics, finance and other fields
01 Jun 1997-Journal of the American Statistical Association (Chapman & Hall)-Vol. 92, Iss: 438, pp 799
Abstract: Statistical Aspects of ARCH and Scholastic Volatility Likelihood-Based Inference for Cointegration of Some Non-Stationary Time Series Forecasting in Macroeconomics Longitudinal Panel Data: An Overview of Current Methodology
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TL;DR: In this paper, the authors proposed a method for estimating Value at Risk (VaR) and related risk measures describing the tail of the conditional distribution of a heteroscedastic financial return series.
1,721 citations
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TL;DR: In this article, the most important developments in multivariate ARCH-type modeling are surveyed, including model specifications, inference methods, and the main areas of application in financial econometrics.
Abstract: This paper surveys the most important developments in multivariate ARCH-type modelling. It reviews the model specifications, the inference methods, and the main areas of application of these models in financial econometrics.
1,629 citations
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TL;DR: The asymptotic validity of three easy-to-implement alternative bootstrap proposals for stationary autoregressive processes with m.i.s.d. errors is established, finding them to be more accurate in small samples than the conventional large-sample approximation based on robust standard errors.
591 citations
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TL;DR: In this paper, the authors investigated the empirical characteristics of investor risk aversion over equity return states by estimating a daily semi-parametric pricing kernel, allowing measurement of time-variation in riskaversion over S&P500 return states.
Abstract: This paper investigates the empirical characteristics of investor risk aversion over equity return states by estimating a daily semi-parametric pricing kernel. The two key features of this estimator are: (1) the functional form of the pricing kernel is estimated semi-parametrically, instead of being prespecified and (2) the pricing kernel is re-estimated on a daily basis, allowing measurement of time-variation in riskaversion over equity return states.Important empirical findings of the paper are as follows. Constant relative risk aversion over S&P500 return states is rejected in favor of a model in which relative risk aversion is stochastic. Empirical relative risk aversion over equity return states is found to be positively autocorrelated and positively correlated with the spread between implied and objective volatilities. In addition, the constant relative risk aversion (power utility) pricing kernel is found to underestimate the value of payoffs in large negative return states.An option hedging methodology is developed as a test of the predictive information in the empirical pricing kernel and its associated state probability model. The results of hedging performance tests for out-of-the-money S&P500 index put options indicate that time-varying risk aversion over equity return states is an important factor affecting option prices.
559 citations
Cites background from "Time series models : in econometric..."
...Surveys of this literature include Ghysels, Harvey, and Renault (1996) and Shephard (1996)....
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