Suchergebnisse

A simple framework is proposed based on variance decompositions from approximating vector autoregressions to define, measure and monitor network connectedness, and these methods are applied in financial and macroeconomic contexts. In financial markets, for example, the interest is in connections among different assets, asset classes, or portfolios, as well as the stocks of individual institutions, and the objects connected are typically returns or return volatilities. Similarly, in macroeconomics the interest is in cross-country real output connections (that is, the global business cycle).

Understanding the dynamic evolution of the yield curve is critical to many financial tasks, including pricing financial assets and their derivatives, managing financial risk, allocating portfolios, structuring fiscal debt, conducting monetary policy, and valuing capital goods. Unfortunately, most yield curve models tend to be theoretically rigorous but empirically disappointing, or empirically successful but theoretically lacking. In this book, Francis Diebold and Glenn Rudebusch propose two extensions of the classic yield curve model of Nelson and Siegel that are both theoretically rigorou.

"We introduce the financial economics of market microstructure into the financial econometrics of asset return volatility estimation. In particular, we use market microstructure theory to derive the cross-correlation function between latent returns and market microstructure noise, which feature prominently in the recent volatility literature. The cross-correlation at zero displacement is typically negative, and cross-correlations at nonzero displacements are positive and decay geometrically. If market makers are sufficiently risk averse, however, the cross-correlation pattern is inverted. Our results are useful for assessing the validity of the frequently-assumed independence of latent price and microstructure noise, for explaining observed cross-correlation patterns, for predicting as-yet undiscovered patterns, and for making informed conjectures regarding improved volatility estimation methods"--National Bureau of Economic Research web site

"We provide a simple and intuitive measure of interdependence of asset returns and/or volatilities. In particular, we formulate and examine precise and separate measures of return spillovers and volatility spillovers. Our framework facilitates study of both non-crisis and crisis episodes, including trends and bursts in spillovers, and both turn out to be empirically important. In particular, in an analysis of nineteen global equity markets from the early 1990s to the present, we find striking evidence of divergent behavior in the dynamics of return spillovers vs. volatility spillovers: Return spillovers display a gently increasing trend but no bursts, whereas volatility spillovers display no trend but clear bursts"--National Bureau of Economic Research web site

This book uses the methods of statistical time-series analysis to characterize the stochastic structure of seven major dollar spot exchange rates, at both weekly and monthly frequencies, during the recent floating-rate regime 1973-1985. While the conditional-mean behaviour of each exchange rate is close to a random walk, the conditional variances are found to have strongly time-varying volatility. Models of autoregressive conditional heteroskedasticity (ARCH) are estimated and used to explain unconditional exchange-rate leptokurtosis (as well as convergence to normality under temporal aggregation), and to provide superior interval predictors. The results are extended to real exchange rates and deviations from purchasing power parity