A New Method for Identifying the Effects of Foreign Exchange Interventions

The monetary authorities react even to intraday changes in the exchange rate; however, in most cases, intervention data is available only at a daily frequency. This temporal aggregation makes it difficult to identify the effects of interventions on the exchange rate. We propose a new method based on Markov Chain Monte Carlo simulations to cope with this endogeneity problem: We use "data augmentation" to obtain intraday intervention amounts and then estimate the efficacy of interventions using the augmented data. Applying this method to Japanese data, we find that an intervention of one trillion yen moves the yen/dollar rate by 1.7 percent, which is more than twice as large as the magnitude reported in previous studies applying OLS to daily observations. This shows the quantitative importance of the endogeneity problem due to temporal aggregation.Foreign exchange intervention, Intraday data, Markov-chain Monte Carlo method, Endogeneity problem, Temporal aggregation

Temporal Aggregation Effects on the Construction of Portfolios of Stocks or Mutual Funds through Optimization Techniques - Some Empirical and Monte Carlo Results

In this paper we test the effects of temporal aggregation (disaggregation) on the efficiency of portfolio construction using the mean variance optimization approach. Using Monte Carlo techniques and empirical data from the Athens Stocks Exchange we confirm that the use of temporally aggregated data effects very seriously the efficiency of the constructed portfolio. Especially as the degree of temporal aggregation increases the application of optimization techniques could lead to different results regarding the percentage of stocks participation, the weights and finally the total portfolio performance.Portfolio Optimization, Stocks; Temporal Aggregation; Stochastic Simulation, The Banking Sector of the Athens Stocks Exchange

The Exchange Rate and Interest Rate Differential Relationship: Evidence from Two Financial Crises

This paper examines the contemporaneous and inter-temporal interaction between real exchange rate and real interest rate differential in the two financial crises of 1997 and 2008 by using data from thirteen countries from different world regions. The empirical result shows that negative contemporaneous relationship exists in most countries. In addition, there is little evidence on a systematic inter-temporal relationship between the real interest rate differential and the real exchange rate, and an absence of consistent result in supporting a negative relationship among the thirteen economies. An extremely low change in the conditional correlation between real interest rate differential and real exchange rates can be found in small countries.Contemporaneous, inter-temporal relationship, exchange rate, interest rate differential, financial crisis

Universal Solutions in Temporal Data Exchange

During the past fifteen years, data exchange has been explored in depth and in a variety of different settings. Even though temporal databases constitute a mature area of research studied over several decades, the investigation of temporal data exchange was initiated only very recently. We analyze the properties of universal solutions in temporal data exchange with emphasis on the relationship between universal solutions in the context of concrete time and universal solutions in the context of abstract time. We show that challenges arise even in the setting in which the data exchange specifications involve a single temporal variable. After this, we identify settings, including data exchange settings that involve multiple temporal variables, in which these challenges can be overcome

TEMPORAL AGGREGATION OF AN ESTAR PROCESS: SOME IMPLICATIONS FOR PURCHASING POWER PARITY ADJUSTMENT

Nonlinear models of deviations from PPP have recently provided an important, theoretically well motivated, contribution to the PPP puzzle. Most of these studies use temporally aggregated data to empirically estimate the nonlinear models. As noted by Taylor (2001), if the true DGP is nonlinear, the temporally aggregated data could exhibit misleading properties regarding the adjustment speeds. We examine the effects of different levels of temporal aggregation on\ estimates of ESTAR models of real exchange rates. Our Monte Carlo results show that temporal aggregation does not imply the disappearance of nonlinearity and that adjustment speeds are significantly slower in temporally aggregated data than in the true DGP. Furthermore, the autoregressive structure of some monthly ESTAR estimates found in the literature is suggestive that adjustment speeds are even faster than implied by the monthly estimates.ESTAR, Real Exchange Rate, Purchasing Power Parity, Aggregation.

The temporal pattern of trading rule returns and central bank intervention: intervention does not generate technical trading rule profits

This paper characterizes the temporal pattern of trading rule returns and official intervention for Australian, German, Swiss and U.S. data to investigate whether intervention generates technical trading rule profits. High frequency data show that abnormally high trading rule returns precede German, Swiss and U.S. intervention, disproving the hypothesis that intervention generates inefficiencies from which technical rules profit. Australian intervention precedes high trading rule returns, but trading/intervention patterns make it implausible that intervention actually generates those returns. Rather, intervention responds to exchange rate trends from which trading rules have recently profited.Banks and banking, Central ; Foreign exchange ; Trade

The Distribution of Exchange Rate Volatility

Using high-frequency data on Deutschemark and Yen returns against the dollar, we construct model-free estimates of daily exchange rate volatility and correlation, covering an entire decade. In addition to being model-free, our estimates are also approximately free of measurement error under general conditions, which we delineate. Hence, for all practical purposes, we can treat the exchange rate volatilities and correlations as observed rather than latent. We do so, and we characterize their joint distribution, both unconditionally and conditionally. Noteworthy results include a simple normality-inducing volatility transformation, high contemporaneous correlation across volatilities, high correlation between correlation and volatilities, pronounced and highly persistent temporal variation in both volatilities and correlation, clear evidence of long-memory dynamics in both volatilities and correlation, and remarkably precise scaling laws under temporal aggregation.

Quantile Correlations: Uncovering temporal dependencies in financial time series

We conduct an empirical study using the quantile-based correlation function to uncover the temporal dependencies in financial time series. The study uses intraday data for the S\&P 500 stocks from the New York Stock Exchange. After establishing an empirical overview we compare the quantile-based correlation function to stochastic processes from the GARCH family and find striking differences. This motivates us to propose the quantile-based correlation function as a powerful tool to assess the agreements between stochastic processes and empirical data