Heterogeneous Information Arrivals and Return Volatility Dynamics: Uncovering the Long-Run in High Frequency Returns

Recent empirical evidence suggests that the interdaily volatility clustering for most speculative returns are best characterized by a slowly mean-reverting fractionally integrated process. Meanwhile, much shorter lived volatility dynamics are typically observed with high frequency intradaily returns. The present article demonstrates, that by interpreting the volatility as a mixture of numerous heterogeneous short-run information arrivals, the observed volatility process may exhibit long-run dependence. As such, the long-memory characteristics constitute an intrinsic feature of the return generating process, rather than the manifestation of occasional structural shifts. These ideas are confirmed by our analysis of a one-year time series of five-minute Deutschemark-U.S. Dollar exchange rates.     

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.     

Volatility Spillovers Between Stock Returns and Exchange Rate Changes: International Evidence

We investigate interdependencies between stock returns and exchange rate changes for six industrialised countries, namely the US, the UK, Japan, Germany, France and Canada, by testing for volatility spillovers using a bivariate EGARCH model. Volatility spillovers from stock returns to exchange rate changes are found for all countries except Germany. These spillovers are symmetric in nature. No evidence is found of volatility spillovers from exchange rate changes to stock returns for any country. Spillovers from stock returns to exchange rate changes have increased since October 1987. This finding is consistent with the notion that international financial markets have become increasingly integrated.     

Volatility Spillovers Between Stock Returns and Exchange Rate Changes: International Evidence

We investigate interdependencies between stock returns and exchange rate changes for six industrialised countries, namely the US, the UK, Japan, Germany, France and Canada, by testing for volatility spillovers using a bivariate EGARCH model. Volatility spillovers from stock returns to exchange rate changes are found for all countries except Germany. These spillovers are symmetric in nature. No evidence is found of volatility spillovers from exchange rate changes to stock returns for any country. Spillovers from stock returns to exchange rate changes have increased since October 1987. This finding is consistent with the notion that international financial markets have become increasingly integrated.     

Economic fundamentals and the long-run correlation between exchange rates and commodities

We examine the short-run and long-run dynamics of the correlation between exchange rate and commodity returns, and assess the extent to which the long-run correlation is determined by economic fundamentals. Our empirical analysis is based on the dynamic conditional correlation model with mixed-data sampling (DCC-MIDAS). This model separates the high-frequency from the low-frequency dynamics of volatility and correlation and allows us to relate long-run volatility and correlation to economic fundamentals. Using both economic and statistical criteria for performance evaluation, we find that economic fundamentals are important determinants of the long-run correlation between exchange rate and commodity returns.     

Understanding the price of volatility risk in carry trades

This paper investigates the cross-sectional pricing ability of the short- and long-run components of global foreign exchange (FX) volatility for carry trade returns. We find a negative and statistically significant factor risk price for the long-run component, but no significant pricing effect due to the short-run volatility component. We also document that the dynamics of the long-run component of global FX volatility are related to US macroeconomic fundamentals. Our results are robust to various parametrizations of the volatility models used to obtain the volatility components and they are invariant to alternative asset pricing testing methodologies and sample periods.     

Assessing co-ordinated Asian exchange rate regimes: Proposal for a possible move towards a common currency

This study assesses prospective Asian exchange rate regimes and finds short- and long-run currency dynamics more conducive to the introduction of a common peg based on a basket of the European euro, the United States dollar and the Japanese yen than the alternative of a United States dollar peg exchange rate regime. Exchange rate systems of 3- 4- and 5-Asian currencies are considered and the dynamics in a set of four European currencies prior to the introduction of the Euro provides benchmark evidence. The evidence for an Asian basket peg exchange rate regime is strengthened when, unlike prior studies, estimates of the long-run parameters account for time-varying volatility effects.     

Time‐Varying Volatility in the Foreign Exchange Market: New Evidence on its Persistence and on Currency Spillovers

We examine empirically the volatility of four major US dollar spot exchange rates using intraday data over 40 trading days. Using multivariate stochastic volatility models, we investigate the degree of persistence of exchange rate volatility for data sampled at different frequencies and the role of volatility spillovers across exchange rates. We find that the noise component of volatility 'aggregates out' very quickly, being dominated by the more persistent component of volatility for data sampled at 15–minute or lower frequencies. Our results also suggest that exchange rate volatility is very persistent and that cross–currency spillovers are small.     

Components of Market Risk and Return

This article proposes a flexible but parsimonious specificationof the joint dynamics of market risk and return to produce forecastsof a time-varying market equity premium. Our parsimonious volatilitymodel allows components to decay at different rates, generatesmean-reverting forecasts, and allows variance targeting. Thesefeatures contribute to realistic equity premium forecasts forthe U.S. market over the 1840–2006 period. For example,the premium forecast was low in the mid-1990s but has recentlyincreased. Although the market's total conditional variancehas a positive effect on returns, the smooth long-run componentof volatility is more important for capturing the dynamics ofthe premium. This result is robust to univariate specificationsthat condition on either levels or logs of past realized volatility(RV), as well as to a new bivariate model of returns and RV.     

Implications of market microstructure for realized variance measurement

Volatility measuring and estimation based on intra-day high-frequency data has grown in popularity during the last few years. A significant part of the research uses volatility and variance measures based on the sum of squared high-frequency returns. These volatility measures, introduced and mathematically justified in a series of papers by Andersen et al. [1999. (Understanding, optimizing, using and forecasting) realized volatility and correlation. Leonard N. Stern School Finance Department Working Paper Series, 99-061, New York University; 2000a. The distribution of realized exchange rate volatility. Journal of the American Statistical Association 96, no. 453: 42–55; 2000b. Exchange rate returns standardized by realized volatility are (nearly) Gaussian. Multinational Finance Journal 4, no. 3/4: 159–179; 2003. Modeling and forecasting realized volatility. NBER Working Paper Series 8160.] and Andersen et al. 2001a. Modeling and forecasting realized volatility. NBER Working Paper Series 8160., are referred to as ‘realized variance’. From the theory of quadratic variations of diffusions, it is possible to show that realized variance measures, based on sufficiently frequently sampled returns, are error-free volatility estimates. Our objective here is to examine realized variance measures, where well-documented market microstructure effects, such as return autocorrelation and volatility clustering, are included in the return generating process. Our findings are that the use of squared returns as a measure for realized variance will lead to estimation errors on sampling frequencies adopted in the literature. In the case of return autocorrelation, there will be systematic biases. Further, we establish increased standard deviation in the error between measured and real variance as sampling frequency decreases and when volatility is non-constant.     

An investigation of return and volatility linkages among equity markets: A study of selected European and emerging countries

This paper investigates the linkages among equity returns (based on exchange traded funds, ETF) and transmission of volatilities in the following countries: Germany, Austria, Poland, Russia and Turkey. Multivariate Autoregressive Moving Averages (MARMA) and the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) methodologies are utilized. The findings include the existence of significant co-movement of returns among countries in the sample. Also, Turkish and Russian markets were found to be more volatile than Austria, Germany and Poland. However, volatilities in Russia and Turkey do not persist very long. Finally, there is strong evidence of volatility spillovers. All of the countries in the sample, with the exception of Turkey, experience volatility spillovers from other markets. The presence of spillovers among return series and persistence of volatilities are useful to investors interested in diversifying their portfolios and to traders/fund managers who are interested in maximizing returns.     

Volatility returns with vengeance: Financial markets vs. commodities

To assess how financial markets and commodities are inter-related, this paper introduces a ‘volatility surprise’ component into the asymmetric DCC with one exogenous variable (ADCCX) framework. We develop an econometric model in which returns and volatility allow to influence pairs of assets, and derive several case studies linking commodities to stocks, bonds and currencies from 1983 to 2013. The innovative feature of our model is that these volatility spillovers are modeled consistently within the correlation dynamics of the ADCCX. We find evidence that return and volatility spillovers do exist between commodity and financial markets and that in turn, their relative impact on each other is very substantial.     

Long memory in volatility and trading volume

We use fractionally-integrated time-series models to investigate the joint dynamics of equity trading volume and volatility. Bollerslev and Jubinski (1999) show that volume and volatility have a similar degree of fractional integration, and they argue that this evidence supports a long-run view of the mixture-of-distributions hypothesis. We examine this issue using more precise volatility estimates obtained using high-frequency returns (i.e., realized volatilities). Our results indicate that volume and volatility both display long memory, but we can reject the hypothesis that the two series share a common order of fractional integration for a fifth of the firms in our sample. Moreover, we find a strong correlation between the innovations to volume and volatility, which suggests that trading volume can be used to obtain more precise estimates of daily volatility for cases in which high-frequency returns are unavailable.     

Long-memory in high-frequency exchange rate volatility under temporal aggregation

This paper applies log-periodogram estimators of the fractional difference parameter to the volatility of the US dollar exchange rate returns of 11 European currencies, and under temporal aggregation from an underlying half-hourly intra-day frequency. Particular attention is paid to the sequencing of the nonlinear transformation of returns and their temporal aggregation. The results reported confirm that long-memory in absolute returns constitutes an intrinsic and empirically significant characteristic of the exchange rates considered. At the practical level, our results lend support to the proposal that nonlinear transformation prior to temporal aggregation can return meaningful long-memory parameter estimates. Our findings also illustrate the advantages of long-memory parameter estimation based on the smoothed periodogram applied to absolute returns in controlling for noise induced by temporal aggregation in the processing of high-frequency data.     

Power transformations of absolute returns and long memory estimation

Different power transformations of absolute returns of various financial assets have been found to display different magnitudes of sample autocorrelations, a property referred to as the Taylor effect. In this paper, we consider the long memory stochastic volatility model for the returns, under which, the asymptotic rate of decay of the autocorrelations of powers of absolute returns is governed by their long memory parameter. Although the true long memory parameter of powers of absolute returns is the same across different powers, we show that the local Whittle estimator of the long memory parameter has finite-sample bias that differs across the power transformations chosen. A Monte-Carlo experiment provides evidence in support of our theoretical finding that the reported variation of the estimates of the long memory parameter for power transformations of returns could be due to finite-sample bias of the estimator. The local Whittle estimates of powers of absolute returns for the S&P500 index and the DM/USD exchange rate are also examined.     

Asymmetric volatility connectedness on the forex market

We show how bad and good volatility propagate through the forex market, i.e., we provide evidence for asymmetric volatility connectedness on the forex market. Using high-frequency, intra-day data of the most actively traded currencies over 2007–2015 we document the dominating asymmetries in spillovers that are due to bad, rather than good, volatility. We also show that negative spillovers are chiefly tied to the dragging sovereign debt crisis in Europe while positive spillovers are correlated with the subprime crisis, different monetary policies among key world central banks, and developments on commodities markets. It seems that a combination of monetary and real-economy events is behind the positive asymmetries in volatility spillovers, while fiscal factors are linked with negative spillovers.     

Price and volatility spillovers between interest rate and exchange value of the US dollar

In this paper, the dynamic relationships between interest rate and exchange value of the US dollar are studied via a multivariate Exponential Generalised Autoregressive Conditional Heteroskedasticity (EGARCH) model. In terms of price changes, movements of interest rates have positive effects on movements of exchange rates. However, changes in exchange rates do not explain changes in interest rates. Nevertheless, there exists volatility spillovers between the two markets, indicating that their second moments are related. Overall evidence suggests that these two markets have short-term dynamic interactions. The existence of volatility spillovers also suggests that the relationships between these two economic variables are not necessarily linear.     

The Stochastic Volatility of Short-Term Interest Rates: Some International Evidence

This paper estimates a stochastic volatility model of short-term riskless interest rate dynamics. Estimated interest rate dynamics are broadly similar across a number of countries and reliable evidence of stochastic volatility is found throughout. In contrast to stock returns, interest rate volatility exhibits faster mean-reverting behavior and innovations in interest rate volatility are negligibly correlated with innovations in interest rates. The less persistent behavior of interest rate volatility reflects the fact that interest rate dynamics are impacted by transient economic shocks such as central bank announcements and other macroeconomic news.     

Linkages between Indian and US financial markets: impact of global financial crisis and Eurozone debt crisis

This paper examines inter-linkages between Indian and US equity, foreign exchange and money markets using the vector autoregressive-multivariate GARCH-BEKK framework. We investigate the impact of global financial crisis (GFC) and Eurozone debt crisis (EZDC) on the conditional volatility and conditional correlation estimates derived from the multivariate GARCH model for Indian and US financial markets. Our results indicate that there is significant bidirectional causality-in-mean between the Indian stock market returns and the Rs./USD market returns, and significant unidirectional causality-in-mean from the US stock market returns to the Indian stock market returns. As regards volatility spillovers, we find that volatility in the Indian stock market rises in response to domestic as well as US financial market shocks but Indian financial market shocks do not impact the US markets. Further, impact of the recent crisis episodes on the covariance matrix is found to be significant. We find that volatility in the Indian and US financial markets significantly amplified during GFC. The conditional correlations across asset markets were significantly accentuated in the wake of the two crisis episodes. The impact of GFC on cross-market conditional correlations is higher for majority of the asset market pairs in comparison to the EZDC.     

Impact of speculation and economic uncertainty on commodity markets

We examine the interactions between commodity futures returns and five driving factors (financial speculation, exchange rate, stock market dynamics, implied volatility for the US equity market, and economic policy uncertainty). Nonlinear causality tests are implemented after controlling for cointegration and conditional heteroscedasticity in the data over the period May 1990 – April 2014. Our results show strong evidence of unidirectional linear causality from commodity returns to excess speculation for the majority of the considered commodities, in particular for agriculture commodities. This evidence casts doubt on the claim that speculation is driving food prices. We also find unidirectional linear causality from energy futures markets to exchange rates and strong evidence of nonlinear causal dependence between commodity futures returns, on the one hand, and stock market returns and implied volatility, on the other hand. Overall, the new evidence found in this paper can be utilized for policy and investment decision-making.