Analysis of Financial Time Series

By Ruey S. Tsay. 2005

1,289 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research, Signal Processing

View Paper: https://www.semanticscholar.org/paper/c478f9c437360fb685d54caf5dcedd0d779a7a6e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Management Science and Operations Research

In Signal Processing

Citations in the corpus

The Evolution of Stock Market Efficiency Over Time: A Survey of the Empirical Literature (2011, neutral): “This has contributed to an explosive growth of nonlinearity tests to the extent that a full review is impossible (for a limited survey, see Granger and Teräsvirta, 1993; Patterson and Ashley, 2000; Tsay, 2005).”
Least absolute deviation estimation for fractionally integrated autoregressive moving average time series models with conditional heteroscedasticity (2008, neutral): “They appear to be significant at the 5% level even after 200 lags, suggesting the presence of long memory (Tsay, 2002).”
LADE-Based Inference for ARMA Models With Unspecified and Heavy-Tailed Heteroscedastic Noises (2015, positive): “…model to study the gross national product (GNP) series in the United States, Franses and Van Dijk (1996) studied several stock market indexes by AR(1)-GJR(1, 1) models, Zhu and Ling (2011) fitted a MA(3)-GARCH(1, 1) model to the world oil prices, see also Tsay (2005) for more empirical evidences.”
Heteroscedasticity and Autocorrelation Robust Structural Change Detection (2013, neutral): “The interest rate series was used in Tsay (2005) to demonstrate time series regression and stationary linear time series models.”
Estimating High Dimensional Covariance Matrices and its Applications (2011, neutral): “Related issues can be found in Bollerslev (1987), Engle (2002), Engle and Kroner (1995), Engle et al. (1990), and Engle and Sheppard (2001), Ledoit et al. (2003), and Tsay (2002).”
Extreme Value Theory and Value at Risk (2003, neutral): “Some available methods are the following: Riskmetrics, the GARCH approach, quantile estimation, and extreme value theory (see, for example, Tsay, 2001, chapter 7). Riskmetrics assumes that the continuously compounded daily return of a portfolio follows a conditional normal distribution. The GARCH ap”