Structural change in the forward discount: a Bayesian analysis of forward rate unbiasedness hypothesis

Using Bayesian methods, we reexamine the empirical evidence from Sakoulis et al. (2010) regarding structural breaks in the forward discount for G-7 countries. Our Bayesian framework allows the number and pattern of structural changes in level and variance to be endogenously determined. We find different locations of breakpoints for each currency; mostly, fewer breaks are present. We find little evidence of moving toward stationarity in the forward discount after accounting for structural change. Our findings suggest that the existence of structural change is not a viable justification for the forward discount anomaly.Bayesian method, structural change, forward discount anomaly, Gibbs-sampling

Predicting Stock Volatility Using After-Hours Information

We use realized volatilities based on after hours high frequency returns to predict next day volatility. We extend GARCH and long-memory forecasting models to include additional information: the whole night, the preopen, the postclose realized variance, and the overnight squared return. For four NASDAQ stocks (MSFT, AMGN, CSCO, and YHOO) we find that the inclusion of the preopen variance can improve the out-of-sample forecastability of the next day conditional day volatility. Additionally, we find that the postclose variance and the overnight squared return do not provide any predictive power for the next day conditional volatility. Our findings support the results of prior studies that traders trade for non-information reasons in the postclose period and trade for information reasons in the preopen period.

Long Memory versus Structural Breaks in Modeling and Forecasting Realized Volatility

In this paper, we explore the possibilities of structural breaks in the realized volatility with the observed long-memory property for the Deutschemark/Dollar, Yen/Dollar and Yen/Deutschemark spot exchange rate realized volatility. The paper finds the substantial reduction of persistence of realized volatility after removing the breaks. Our VAR-RV-Break model provides the superior predictive ability compared to most of the forecasting models when the future break is known. The VAR-RV-I(d) long memory model, however, is still the best forecasting model even when the true financial volatility series are created by structural breaks with unknown break dates and size

Essays on the volatility of macroeconomic and financial time series

Thesis (Ph. D.)--University of Washington, 2006.The essays are comprised of three chapters to investigate the structural changes and reasons of Japanese postwar macroeconomic dynamic, the structural changes and nature of exchange rate realized volatility, and the relationship between macroeconomic and financial market volatility, respectively. For each chapter, we apply advanced time-series econometrics techniques, including unknown structural break tests, Markov-switching model, long memory model, and factor model using principal component method to analyze a sequence of volatility issues with emphasis on output dynamics, monetary policy and financial market variables. In the first chapter, we exam the rising volatility of Japan's real output and its relationship with monetary policy. A few lessons we learn from Japan's case could be useful for most central bankers.In the second chapter, using high-frequency data, we explore the possibilities of structural changes and regime switching in the realized volatility of the Deutschemark/Dollar, Yen/Dollar and Yen/Deutschemark spot exchange rates with their observed long-memory property. We find the substantial reduction of persistence of realized volatility after removing the breaks and the VAR-RV-Break model provides the superior predictive ability compared to most of the forecasting models. However, the VAR-RV-I(d) long memory model is still the best forecasting model even when the true financial volatility series are created by structural breaks and we have little knowledge about break dates and size. In the third chapter, we find mixed evidence on volatility destabilization for the financial market. Twelve static factors and eight dynamic factors are calculated and explored from 140 time series data set in the U.S

Markov switching and long memory: a Monte Carlo analysis

This article finds the close relationship between long memory and some forms of Markov-switching models. The simulation results suggest: (1) when the transition probabilities are closer to unity, it is more likely to generate long memory process; (2) magnitude of regime-switching plays an important role in generating long memory; and (3) process with switching in variance (disturbance) is much less likely to explain long-memory process than switching in mean (intercept) and autoregressive coefficient. Therefore, given the observed high persistence in financial volatility data, volatility modelling by switching in mean and AR coefficient is preferred to that by switching in variance.

Forecasting the term structures of Treasury and corporate yields using dynamic Nelson-Siegel models

We extend Diebold and Li's dynamic Nelson-Siegel three-factor model to a broader empirical prospective by including the evaluation of the state space approach and by using nine different ratings for corporate bonds. We find that the dynamic Nelson-Siegel factor AR(1) model outperforms other competitors on the out-of-sample forecast accuracy, especially on the investment-grade bonds for the short-term forecast horizon and on the high-yield bonds for the long-term forecast horizon. The dynamic Nelson-Siegel factor state space model, however, becomes appealing on the high-yield bonds in the short-term forecast horizon, where the factor dynamics are more likely time-varying and parameter instability is more probable in the model specification.Term structures Treasury yields Corporate yields Nelson-Siegel model Factor model AR(1) VAR(1) Out-of-sample forecasting evaluations

Volatility Spillovers between the US and China Stock Markets: Structural Break Test with Symmetric and Asymmetric GARCH Approaches

The paper examines the short-run spillover effects of daily stock returns and volatilities between the Standard & Poor's (S&P) 500 stock index in the US and the Shanghai Stock Exchange (SSE) index in China. First, we find that a structural break occurred in the SSE stock return mean in December 2005. Second, by analyzing modified general autoregressive conditional heteroscedasticity (GARCH)(1,1)-M models, we find evidence of a symmetric and asymmetric volatility spillover effect from the US to the China stock market in the post-break period. Third, we observe the symmetric volatility spillover effect from China to the US in the post-break period.Volatility spillover, China stock market, structural break, GARCH model,

Dynamic hedging performance with the evaluation of multivariate GARCH models: evidence from KOSTAR index futures

This article examines the hedging performance of the conventional Ordinary Least Squares (OLS) model and a variety of dynamic hedging models for the in-sample and out-of-sample periods of Korean daily Korea Securities Dealers Automated Quotation (KOSDAQ) STAR (KOSTAR) index futures. We employ the rolling OLS and various popular multivariate Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models to estimate and forecast the conditional covariances and variances of KOSTAR spot and futures returns. This article finds that dynamic hedging methods outperform the conventional method for the out-of-sample period. However, the simple rolling OLS is superior to all the other popular multivariate GARCH models.