We show that dependence on foreign energy can increase economic instability by raising the likelihood of equilibrium indeterminacy, hence making fluctuations driven by self- fulfilling expectations easier to occur.
We study the interaction of multiple large economies in dynamic stochastic general equilibrium. Each economy has a monetary policymaker that attempts to control the economy through the use of a linear nominal interest rate feedback rule.
Shleifer and Vishny (1997) pointed out some of the practical and theoretical problems associated with assuming that rational risk-arbitrage would quickly drive asset prices back to long-run equilibrium.
We use multivariate regime switching vector autoregressive models to characterize the time-varying linkages among the Irish stock market, one of the top world performers of the 1990s, and the US and UK stock markets.
We revisit the risk-return relation using the component GARCH model and international daily MSCI stock market data. In contrast with the previous evidence obtained from weekly and monthly data, daily data show that the relation is positive in almost all markets and often statistically significant. Likelihood ratio tests reject the standard GARCH model in favor of the component GARCH model, which strengthens the evidence for a positive risk-return tradeoff.
We show that predictable covariances between means and variances of stock returns may have a first order effect on portfolio composition. In an international asset menu that includes both European and North American small capitalization equity indices, we find that a three-state, heteroskedastic regime switching VAR model is required to provide a good fit to weekly return data and to accurately predict the dynamics in the joint density of returns.
This paper analyzes immigration and outsourcing in a general-equilibrium model of international factor mobility. In our model, legal immigration of skilled labor is controlled through a quota, while outsourcing is determined both by the firms in response to market conditions and through policy-imposed barriers.
This paper proposes a new tractable approach to solving asset allocation problems in situations with a large number of risky assets which pose problems for standard approaches. Investor preferences are assumed to be defined over moments of the wealth distribution such as its mean, variance, skew and kurtosis.
Most intervention studies have been silent on the assumed structure of the economic system—implicitly imposing implausible assumptions—despite the fact that inference depends crucially on such issues. This paper identifies the cross-effects of intervention and the level of exchange rates using the likely timing of intervention, macroeconomic announcements as instruments and the nonlinear structure of the intervention reaction function.
Two recent strands of research have contributed to our understanding of the effects of foreign exchange intervention: 1) the use of high frequency data; 2) the use of event studies to evaluate the effects of intervention. This article surveys recent empirical studies of the effect of foreign exchange intervention and analyzes the implicit assumptions and limitations of such work.
This paper shows that a relatively high level of average U.S. industry- or firm-level idiosyncratic stock volatility is usually associated with a future appreciation in the U.S. dollar. For most foreign currencies, the relation is statistically significant in both in sample and out-of-sample tests, even after we use a bootstrap procedure to explicitly account for data mining.
This paper develops a two-country OLG model under the assumption that investors are on a Bayesian learning path. While investors from both countries receive identical information flows, domestic investors start off with less precise prior beliefs concerning foreign fundamentals.
This paper argues that major governments should actively manage their foreign exchange portfolios to maximize the risk-adjusted return to the taxpayer by exploiting long-term, fundamental based predictability in floating exchange rates.
Changes in costs faced by firms have direct implications for their price-cost margins. Knowing how prices respond to such cost changes is crucial for understanding how individual markets function and, in turn, for understanding the macroeconomy.
Dynamic general equilibrium models predict high cross-country consumption correlations, whereas the data show that output correlations tend to be higher. Spectral decomposition reveals that this ranking varies across frequency bands, with consumption correlations often exceeding output correlations at higher frequencies.
Consistent with findings in other markets, implied volatility is a biased predictor of the realized volatility of gold futures. No existing explanation—including a price of volatility risk—can completely explain the bias, but much of this apparent bias can be explained by persistence and estimation error in implied volatility
We examine exchange rate pass-through into U.S. import prices for 29 manufacturing industries using eight exchange rate indexes. These indexes vary by the number currencies included; whether the weight on each currency is based on total trade with the United States or solely imports; and, whether the weights vary by industry. Our results indicate that pass-through is generally incomplete but varies across industries.
Research has consistently found that implied volatility is a conditionally biased predictor of realized volatility across asset markets. This paper evaluates explanations for this bias in the market for options on foreign exchange futures.
For many years after the seminal work of the Meese and Rogoff (1983a), conventional wisdom held that exchange rates could not be forecast from monetary fundamentals. Monetary models of exchange rate determination were generally unable to beat even a naïve no-change model in out-of-sample forecasting.
This paper merges the literature on technical trading rules with the literature on Markov switching to develop economically useful trading rules. The Markov models' out-of sample, excess returns modestly exceed those of standard technical rules and are profitable over the most recent subsample.
This article investigates the use of genetic programming to forecast out-of-sample daily volatility in the foreign exchange market. Forecasting performance is evaluated relative to GARCH(1,1) and RiskMetrics models for two currencies, DEM and JPY.