http://homepage.ntu.edu.tw/~ntuperc/docs/publication/2002_25_Lin.pdf

Unit root tests in panel data: asymptotic and finite-sample properties

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root?

Preface1Difference Equations12Lag Operators253Stationary ARMA Processes434Forecasting725Maximum Likelihood Estimation1176Spectral Analysis1527Asymptotic Distribution Theory1808Linear Regression Models2009Linear Systems of Simultaneous Equations23310Covariance-Stationary Vector Processes25711Vector Autoregressions29112Bayesian Analysis35113The Kalman Filter37214Generalized Method of Moments40915Models of Nonstationary Time Series43516Processes with Deterministic Time Trends45417Univariate Processes with Unit Roots47518Unit Roots in Multivariate Time Series54419Cointegration57120Full-Information Maximum Likelihood Analysis of Cointegrated Systems63021Time Series Models of Heteroskedasticity65722Modeling Time Series with Changes in Regime677A Mathematical Review704B Statistical Tables751C Answers to Selected Exercises769D Greek Letters and Mathematical Symbols Used in the Text786Author Index789Subject Index792

https://sisis.rz.htw-berlin.de/inh2007/12357004.pdf

Time series analysis

Any series of observations ordered along a single dimension, such as time, may be thought of as a time series. The emphasis in time series analysis is on studying the dependence among observations at different points in time. What distinguishes time series analysis from general multivariate analysis is precisely the temporal order imposed on the observations. Many economic variables, such as GNP and its components, price indices, sales, and stock returns are observed over time. In addition to being interested in the contemporaneous relationships among such variables, we are often concerned with relationships between their current and past values, that is, relationships over time.

Time Series Analysis

https://www.federalreserve.gov/pubs/ifdp/2006/871/ifdp871.pdf

Real-Time Price Discovery in Global Stock, Bond and Foreign Exchange Markets

https://www.nber.org/system/files/working_papers/t0264/t0264.pdf

Long memory and regime switching

Using a new dataset consisting of six years of real-time exchange rate quotations, macroeconomic expectations, and macroeconomic realizations (announcements), we characterize the conditional means of U.S. dollar spot exchange rates versus German Mark, British Pound, Japanese Yen, Swiss Franc, and the Euro. In particular, we find that announcement surprises (that is, divergences between expectations and realizations, or “news”) produce conditional mean jumps; hence high-frequency exchange rate dynamics are linked to fundamentals. The details of the linkage are intriguing and include announcement timing and sign effects. The sign effect refers to the fact that the market reacts to news in an asymmetric fashion: bad news has greater impact than good news, which we relate to recent theoretical work on information processing and price discovery.

/pdf/micro-effects-of-macro-announcements-real-time-price-3nxacy4m9v.pdf

Micro Effects of Macro Announcements: Real-Time Price Discovery in Foreign Exchange *

The theoretical and empirical econometric literatures on long memory and regime switching have evolved largely independently, as the phenomena appear distinct. We argue, in contrast, that they are intimately related, and we substantiate our claim in several environments, including a simple mixture model, Engle and Lee's (1999) stochastic permanent break model, and Hamilton's (1989) Markov switching model. In particular, we show analytically that stochastic regime switching is easily confused with long memory, even asymptotically, so long as only a small' amount of regime switching occurs, in a sense that we make precise. A Monte Carlo analysis supports the relevance of the theory and produces additional insights.

Long Memory and Regime Switching

We propose using the price range in the estimation of stochastic volatility models. We show theoretically, numerically, and empirically that range-based volatility proxies are not only highly efficient, but also approximately Gaussian and robust to microstructure noise. Hence range-based Gaussian quasi-maximum likelihood estimation produces highly efficient estimates of stochastic volatility models and extractions of latent volatility. We use our method to examine the dynamics of daily exchange rate volatility and find the evidence points strongly toward two-factor models with one highly persistent factor and one quickly mean-reverting factor. VOLATILITY IS A CENTRAL CONCEPT in finance, whether in asset pricing, portfolio choice, or risk management. Not long ago, theoretical models routinely assumed constant volatility (e.g., Merton (1969), Black and Scholes (1973)). Today, however, we widely acknowledge that volatility is both time varying and predictable (e.g., Andersen and Bollerslev (1997)), and stochastic volatility models are commonplace. Discrete- and continuous-time stochastic volatility models are extensively used in theoretical finance, empirical finance, and financial econometrics, both in academe and industry (e.g., Hull and White (1987), Heston (1993), Bates (1996), Ghysels, Harvey, and Renault (1996), Jarrow (1998), Duffie, Pan, and Singleton (2000)). Unfortunately, the estimation of stochastic volatility models has proved quite difficult. The Gaussian quasi-maximum likelihood estimation (QMLE) approach of Harvey, Ruiz, and Shephard (1994), which initially seemed appeal

/pdf/range-based-estimation-of-stochastic-volatility-models-2wazowaj3w.pdf

Francis X. Diebold

Papers

Real-Time Price Discovery in Global Stock, Bond and Foreign Exchange Markets

Long memory and regime switching

Micro Effects of Macro Announcements: Real-Time Price Discovery in Foreign Exchange *

Long Memory and Regime Switching

Range-Based Estimation of Stochastic Volatility Models