Combining long memory and level shifts in modeling and forecasting the volatility of asset returns

We propose a parametric state space model of asset return volatility with an accompanying estimation and forecasting framework that allows for ARFIMA dynamics, random level shifts and measurement errors. The Kalman filter is used to construct the state-augmented likelihood function and subsequently to generate forecasts, which are mean- and path-corrected. We apply our model to eight daily volatility series constructed from both high-frequency and daily returns. Full sample parameter estimates reveal that random level shifts are present in all series. Genuine long memory is present in high-frequency measures of volatility whereas there is little remaining dynamics in the volatility measures constructed using daily returns. From extensive forecast evaluations, we find that our ARFIMA model with random level shifts consistently belongs to the 10% Model Confidence Set across a variety of forecast horizons, asset classes, and volatility measures. The gains in forecast accuracy can be very pronounced, especially at longer horizons

Combining long memory and level shifts in modeling and forecasting the volatility of asset returns

We propose a parametric state space model of asset return volatility with an accompanying estimation and forecasting framework that allows for ARFIMA dynamics, random level shifts and measurement errors. The Kalman filter is used to construct the state-augmented likelihood function and subsequently to generate forecasts, which are mean and path-corrected. We apply our model to eight daily volatility series constructed from both high-frequency and daily returns. Full sample parameter estimates reveal that random level shifts are present in all series. Genuine long memory is present in most high-frequency measures of volatility, whereas there is little remaining dynamics in the volatility measures constructed using daily returns. From extensive forecast evaluations, we find that our ARFIMA model with random level shifts consistently belongs to the 10% Model Confidence Set across a variety of forecast horizons, asset classes and volatility measures. The gains in forecast accuracy can be very pronounced, especially at longer horizons

Chaos and residual correlations in pinned disordered systems

We study, using functional renormalization (FRG), two copies of an elastic system pinned by mutually correlated random potentials. Short scale decorrelation depend on a non trivial boundary layer regime with (possibly multiple) chaos exponents. Large scale mutual displacement correlation behave as $|x-x'|^{2 \zeta - \mu}$, the decorrelation exponent $\mu$ proportional to the difference between Flory (or mean field) and exact roughness exponent $\zeta$. For short range disorder $\mu >0$ but small, e.g. for random bond interfaces $\mu = 5 \zeta - \epsilon$, $\epsilon=4-d$, and $\mu = \epsilon (\frac{(2 \pi)^2}{36} - 1)$ for the one component Bragg glass. Random field (i.e long range) disorder exhibits finite residual correlations (no chaos $\mu = 0$) described by new FRG fixed points. Temperature and dynamic chaos (depinning) are discussed.Comment: 5 page

A passivity based control methodology for flexible joint robots with application to a simplified shuttle RMS arm

The main goal is to develop a general theory for the control of flexible robots, including flexible joint robots, flexible link robots, rigid bodies with flexible appendages, etc. As part of the validation, the theory is applied to the control law development for a test example which consists of a three-link arm modeled after the shoulder yaw joint of the space shuttle remote manipulator system (RMS). The performance of the closed loop control system is then compared with the performance of the existing RMS controller to demonstrate the effectiveness of the proposed approach. The theoretical foundation of this new approach to the control of flexible robots is presented and its efficacy is demonstrated through simulation results on the three-link test arm

Empirical Evidence on Feedback Trading in Mature and Emerging Stock Markets

We investigate the hypothesis that some participants in mature and emerging capital markets engage in feedback trading. The analysis is based on the Shiller-Sentana-Wadhwani noise trader model. It has the attractive property that it yields testable implications about the presence of positive and negative feedback traders in stock markets. This theoretical framework, together with an asymmetric GARCH-type model, allows us to draw conclusions about whether differences exist between mature and emerging capital markets in terms of the degree of feedback trading. The empirical results show that positive and negative feedback trading strategies exist in both types of markets but are more pronounced in emerging stock markets than in their mature counterparts. Hence, non-fundamental trading strategies seems to play a more important role in emerging relative to mature stock markets.feedback trading; return autocorrelation; emerging capital markets in central and eastern european contries; asymmetric GARCH models

Classification of General Sequences by Frame-Related Operators

This note is a survey and collection of results, as well as presenting some original research. For Bessel sequences and frames, the analysis, synthesis and frame operators as well as the Gram matrix are well-known, bounded operators. We investigate these operators for arbitrary sequences, which in general lead to possibly unbounded operators. We characterize various classes of sequences in terms of these operators and vice-versa. Finally, we classify these sequences by operators applied on orthonormal bases

Asset Prices as Indicators of Euro Area Monetary Policy: An Empirical Assessment of Their Role in a Taylor Rule

This paper estimates forward-looking and forecast-based Taylor rules for France, Germany, Italy, and the euro area. Performing extensive tests for over-identifying restrictions and instrument relevance, we find that asset prices can be highly relevant as instruments in policy rules. While asset prices improve Taylor rule estimates, different assets prove most relevant across countries and this result could be seen as complicating the tasks of the European Central Bank. Encompassing tests show that forecast-based outperform forward-looking Taylor rules. A policy implication is that central banks ought to release their own forecasts and the basis upon which they are generated.Monetary policy reaction functions, Asset prices, Instruments, European Central Bank

Policy Words and Policy Deeds: The ECB and the Euro

This paper examines the role of the ECB communication activities on daily Eurodollar exchange rate and interest rates. We estimate the relationship between monetary policy and the exchange rate using a technique that explicitly recognizes the joint determination of both the levels and volatilities of these variables. We also consider more traditional estimation strategies as a test of the robustness of our main results. We introduce a new indicator of ECB communications policies that focuses on what the ECB says about the future economic outlook for the euro area along five different economic dimensions. The impact of ECB communications policies is more apparent in the time series framework than in the heteroskedasticity estimator approach. Time series estimates reveal that interest rate changes generally have a much larger impact on exchange rate movements, and their volatility, than do ECB verbal pronouncements. Previous studies that conclude that news effects are significant at the daily frequency may have reached such a conclusion because the measurement of news was too highly aggregated. The endogeneity of the exchange rate-interest rate relationship is more apparent when the proxy for monetary policy is the euro area-US differential than when any other proxy for monetary policy is employed.Central bank communication, Eurodollar exchange rate

Trading Behavior During Stock Market Downturns: The Dow, 1915 - 2004

Stock markets periodically experience sharp falls with some referred to as outright crashes. The extant literature has generally resorted to survey type evidence to determine the behavior of investors during such episodes. These kind of studies come to the conclusion that fundamentals play little role in explaining sharp stock market downturns as in October 1987. We know of no econometric study that asks whether feedback, momentum or trend chasing type behavior might explain the behavior of large stock market downturns. Resorting to a feedback trader model, we estimate a variety of asymmetric GARCH-type models. Based on daily data on the Dow Jones Industrial Average index since 1915 we find that there is evidence of positive feedback trading during episodes of stock market crashes. Hence, the econometric evidence is broadly consistent with findings based on surveys. --