Real Time Detection of Structural Breaks in GARCH Models

A sequential Monte Carlo method for estimating GARCH models subject to an unknown number of structural breaks is proposed. Particle filtering techniques allow for fast and efficient updates of posterior quantities and forecasts in real time. The method conveniently deals with the path dependence problem that arises in these type of models. The performance of the method is shown to work well using simulated data. Applied to daily NASDAQ returns, the evidence favors a partial structural break specification in which only the intercept of the conditional variance equation has breaks compared to the full structural break specification in which all parameters are subject to change. The empirical application underscores the importance of model assumptions when investigating breaks. A model with normal return innovations results in strong evidence of breaks; while more flexible return distributions such as t-innovations or a GARCH-jump mixture model still favors breaks but indicates much more uncertainty regarding the time and impact of them.Econometric and statistical methods; Financial markets

Foreign Exchange Intervention and Exchange Rate Volatility in Peru

Flexible exchange rate experience in Peru has been accompanied by frequent official interventions in the form of foreign exchange purchases or sales. Monetary authority pursues reducing excess volatility in the exchange rate through its direct intervention. However, in recent years, this intervention has concentrated in US dollars purchases, apparently signaling a bias towards defending a given exchange rate level (not necessarily fixed). For the period 1994 - 2007, this document assesses consistency of the empirical evidence with the goal of reducing exchange rate volatility. Thus, it uses univariate and multivariate time series models subject to stochastic shifts to study currency pressures. Results suggest consistency with the reduced-volatility goal. Nonetheless, in line with other studies, factors such as the foreign exchange gap with respect to its trend also induce foreign exchange intervention.Foreign Exchange Intervention, Exchange Rate Volatility, Markov-Switching Models.

Real Time Detection of Structural Breaks in GARCH Models

A sequential Monte Carlo method for estimating GARCH models subject to an unknown number of structural breaks is proposed. Particle filtering techniques allow for fast and efficient updates of posterior quantities and forecasts in real-time. The method conveniently deals with the path dependence problem that arises in these type of models. The performance of the method is shown to work well using simulated data. Applied to daily NASDAQ returns, the evidence favors a partial structural break specification in which only the intercept of the conditional variance equation has breaks compared to the full structural break specification in which all parameters are subject to change. The empirical application underscores the importance of model assumptions when investigating breaks. A model with normal return innovations result in strong evidence of breaks; while more flexible return distributions such as t-innovations or a GARCH-jump mixture model still favor breaks but indicate much more uncertainty regarding the time and impact of them.particle filter, GARCH model, change point, sequential Monte Carlo

A Stochastic Volatility Model With Realized Measures for Option Pricing

Based on the fact that realized measures of volatility are affected by measurement errors, we introduce a new family of discrete-time stochastic volatility models having two measurement equations relating both observed returns and realized measures to the latent conditional variance. A semi-analytical option pricing framework is developed for this class of models. In addition, we provide analytical filtering and smoothing recursions for the basic specification of the model, and an effective MCMC algorithm for its richer variants. The empirical analysis shows the effectiveness of filtering and smoothing realized measures in inflating the latent volatility persistence—the crucial parameter in pricing Standard and Poor’s 500 Index options

Regime-Switching and the Estimation of Multifractal Processes

We propose a discrete-time stochastic volatility model in which regimeswitching serves three purposes. First, changes in regimes capture low frequency variations, which is their traditional role. Second, they specify intermediate frequency dynamics that are usually assigned to smooth autoregressive processes. Finally, high frequency switches generate substantial outliers. Thus, a single mechanism captures three important features of the data that are typically addressed as distinct phenomena in the literature. Maximum likelihood estimation is developed and shown to perform well in finite sample. We estimate on exchange rate data a version of the process with four parameters and more than a thousand states. The estimated model compares favorably to earlier specifications both in- and out-of-sample. Multifractal forecasts slightly improve on GARCH(1,1) at daily and weekly intervals, and provide considerable gains in accuracy at horizons of 10 to 50 days.

Tests of an International Capital Asset Pricing Model with Stocks and Government Bonds and Regime Switching Prices of Risk and Intercepts

The paper tests a conditional multivariate International Capital Asset Pricing Model for US, Japanese and European stocks and government bonds, covering the period 1993-2001. Time variation in the prices of market and currency risk is modelled by means of synchronous regime switching. The paper also explores the statistical significance and time variation of asset specific intercept terms, again using synchronous regime switching. The prices of risk are found to be highly time varying. The price of market risk is statistically significant, and the international CAPM risk premia are validated, although currency risk premia are not statistically significant. However, the intercept terms are typically large and significant, implying an overall rejection of the international CAPM, and suggesting that additional, unidentified pricing factors contribute to return expectations.CAPM; Multivariate GARCH-in-Mean

Declining Output Volatility in Germany: Impulses, Propagation, and the Role of Monetary Policy

This paper investigates the effect of economic integration on the ability of firms to maintain a collusive understanding about staying out of each other's markets. The paper distinguishes among different types of trade costs: ad valorem, unit, fixed. It is shown that for a sufficient reduction of ad valorem trade costs, a cartel supported by collusion on either quantities or prices will be weakened, thus integration is pro-competitive. If integration consists of a reductions in unit (fixed) trade costs a price setting cartel is strengthened (unaffected), while a quantity setting one is weakened.Output; Volatility; Monetary policy; Markov switching model; State space model; Spectral analysis; DSGE model

Regime-Switching and the Estimation of Multifractal Processes

We propose a discrete-time stochastic volatility model in which regime switching serves three purposes. First, changes in regimes capture low frequency variations, which is their traditional role. Second, they specify intermediate frequency dynamics that are usually assigned to smooth autoregressive processes. Finally, high frequency switches generate substantial outliers. Thus, a single mechanism captures three important features of the data that are typically addressed as distinct phenomena in the literature. Maximum likelihood estimation is developed and shown to perform well in finite sample. We estimate on exchange rate data a version of the process with four parameters and more than a thousand states. The estimated model compares favorably to earlier specifications both in- and out-of-sample. Multifractal forecasts slightly improve on GARCH(1,1) at daily and weekly intervals, and provide considerable gains in accuracy at horizons of 10 to 50 days.