Distribution and Dynamics of Central-European Exchange Rates: Evidence from Intraday Data

This paper investigates the behavior of the EUR/CZK, EUR/HUF and EUR/PLN spot exchange rates in the period 2002–2008, using 5-minute intraday data. The authors find that daily returns on the corresponding exchange rates scaled by model-free estimates of daily realized volatility are approximately normally distributed and independent over time. On the other hand, daily realized variances exhibit substantial positive skewness and very persistent, long-memory type of dynamics. The authors estimate a simple three-equation model for daily returns, realized variance and the time-varying volatility of realized variance. The model captures all salient features of the data very well and can be successfully employed for constructing point, as well as density forecasts for future volatility. The authors also discuss some issues associated with measuring volatility from the noisy high-frequency data and employ a simple correction that accounts for the distortions present in our dataset.intraday data, realized variance, return and volatility distributions, heterogeneous autoregressive model

Seasonality and Non-Trading Effect on Central European Stock Markets (in English)

This paper investigates seasonality and non-trading effects on central European stock markets within the framework of a periodic autoregressive model for both the mean and the volatility of stock returns. The authors find significant day-of-week effects in the mean of returns on the Czech PX-D and the Polish WIG indices, and significant seasonality in the volatility of the Hungarian BUX index. Similarly, the authors´ empirical results indicate the presence of the non-trading effect in the mean of WIG stock returns. The seasonal patterns in central European stock indices cannot, however, be attributed to any particular day-of-week effect.conditional heteroskedasticity, day-of-week effect, non-trading effect, seasonality

Model-Free Estimation of Large Variance Matrices

This paper introduces a new method for estimating large variance matrices. Starting from the orthogonal decomposition of the sample variance matrix, we exploit the fact that orthogonal matrices are never ill-conditioned and therefore focus on improving the estimation of the eigenvalues. We estimate the eigenvectors from just a fraction of the data, then use them to transform the data into approximately orthogonal series that we use to estimate a well-conditioned matrix of eigenvalues. Our estimator is model-free: we make no assumptions on the distribution of the random sample or on any parametric structure the variance matrix may have. By design, it delivers well-conditioned estimates regardless of the dimension of problem and the number of observations available. Simulation evidence show that the new estimator outperforms the usual sample variance matrix, not only by achieving a substantial improvement in the condition number (as expected), but also by much lower error norms that measure its deviation from the true variance.variance matrices, ill-conditioning, mean squared error, mean absolute deviations, resampling

Funding constraints and liquidity in two-tiered OTC markets

Using primary dealer transactional data from the government bond (gilt) market in the United Kingdom, we identify a new channel through which dealer funding constraints may impair liquidity in two-tiered OTC markets. The key finding is that funding constraints also inhibit dealers' ability to accommodate each others' trade requests in the inter-dealer segment, which limits their collective ability to manage inventories and share risk. As a result, funding constraints end up compromising liquidity above and beyond any direct effects caused by dealers' inability to accommodate client trade requests due to their individual balance sheet constraints

Volatility transmission in emerging European foreign exchange markets

This paper studies the dynamics of volatility transmission between Central European (CE) currencies and the EUR/USD foreign exchange using model-free estimates of daily exchange rate volatility based on intraday data. We formulate a flexible yet parsimonious parametric model in which the daily realized volatility of a given exchange rate depends both on its own lags as well as on the lagged realized volatilities of the other exchange rates. We find evidence of statistically significant intra-regional volatility spillovers among the CE foreign exchange markets. With the exception of the Czech and, prior to the recent turbulent economic events, Polish currencies, we find no significant spillovers running from the EUR/USD to the CE foreign exchange markets. To measure the overall magnitude and evolution of volatility transmission over time, we construct a dynamic version of the Diebold-Yilmaz volatility spillover index and show that volatility spillovers tend to increase in periods characterized by market uncertainty.http://deepblue.lib.umich.edu/bitstream/2027.42/133036/1/wp1020.pd

Implicit Intraday Interest Rate in the UK Unsecured Overnight Money Market

This paper estimates the intraday value of money implicit in the UK unsecured overnight money market. Using transactions data on overnight loans advanced through the UK large value payments system CHAPS in 2003-2009, we find a positive and economically significant intraday interest rate. While the implicit intraday interest rate is quite small pre-crisis, it increases more than tenfold during the financial crisis of 2007-2009. The key interpretation is that an increase in implicit intraday interest rate reects the increased opportunity cost of pledging collateral intraday and can be used as an indicator to gauge the stress of the payment system. We obtain qualitatively similar estimates of the intraday interest rate by using quoted intraday bid and offer rates and confirm that our results are not driven by the intraday variation in the bid-ask spread.publishedVersio

Předvídatelnost výnosů aktiv : empirický rozbor středoevropských kapitálových trhů

Cílem této diplomové práce je analýza prediktibility výnosů akcií na středoevropských kapitálových trzích. V první kapitole je ukázáno, že v období 1996:1 až 2002:2 bylo možno předikovat výnosy i volatilitu hlavních akciových indexů českého, maďarského a polského trhu pomocí časových řad historických výnosů. V druhé kapitole je aplikována Johansenova kointegrační analýza na týdenní ceny akciových indexů českého, polského, maďarského a německého kapitálového trhu v období 1998:1 až 2002:12. Z analýzy vyplývá, že čtyři výše uvedené trhy jsou kointegrované, pokud používáme ceny vyjádřené v národních měnách, přičemž kointegraci nebylo možno prokázat pro ceny vyjádřené v Euru. V obou případech lze ale předikovat výnosy národních indexů pomocí zpožděných hodnot výnosů alespoň jednoho z ostatních indexů. Třetí kapitola se zabývá vlivem nesynchronního obchodování na prediktibilitu výnosů akcií. Na základě zobecněného ekonometrického modelu nesynchronního obchodování (Lo a MacKinlay, 1990) je ukázáno, že nesynchronní obchodování vnáší do časových řad výnosů akcií zdánlivou autokorelaci. Poslední čtvrtá kapitola je zaměřena na posouzení ekonomického významu prediktibility výnosů polských akcií. Porovnává se zde výkonnost dynamické obchodní strategie založené na maximálně predikovatelném portfoliu s pasivní investiční strategií. Z analýzy polských akcií vyplývá, ze prediktibilita výnosů byla v období 2000:1 až 2002:12 ekonomicky významná, tj. bylo možno dosáhnout nadprůměrného výnosu po započtení transakčních nákladů.The aim of this thesis is to investigate the predictability of Central-European common stock returns. In the first chapter, using weekly data on the Czech, Hungarian and Polish major value-weighted indices in the period 1996:1 to 2002:12, it is shown that the index returns contain predictable components and that both the mean and volatility can be forecasted from the time-series of historical returns. In Chapter 2, the Johansen cointegration analysis is applied to the weekly data on the Czech, Hungarian, Polish and German equity market index price in the period 1998:1 to 2002:12. The results indicate that the four considered markets are cointegrated when prices are expressed in local currencies, whereas no cointegration was found for prices in term of Euro. In both cases, there is significant cross-country predictability, i.e. lagged returns from one market can be used to predict returns from at least one other market. The third chapter is dedicated to studying the impact of nonsynchronous trading on the predictability of stock returns. The Lo and MacKinlay (1990) econometric model of nonsynchronous trading is generalized to allow for an autocorrelated common factor. Finally, in Chapter 4 the economic significance of stock return predictability is evaluated. A dynamic trading strategy based on the maximally predictable portfolio is developed for monthly returns on Polish stocks in the time-period 2000:1 to 2002:12 and its performance evaluated using various market timing measures. The results imply economically significant predictability in the Polish stock returns.Institut ekonomických studiíFakulta sociálních vě

Dependence Structure and Portfolio Diversification on Central European Stock Markets

This paper studies the dependence structure on Central European, German and UK stock markets within the framework of a semiparametric copula model for weekly stock index return pairs. Although the linear correlation is much lower, we find similar degree of lower tail dependence as between returns on stocks indices representing developed markets. We show in a simulation exercise that the implications of the estimated nonlinear dependencies for portfolio selection and risk management may be not only statisticaly but also economicaly important.dependence structure; tail dependence; portfolio selection; risk measures