Subsampling realised kernels

In a recent paper we have introduced the class of realised kernel estimators of the increments of quadratic variation in the presence of noise. We showed that this estimator is consistent and derived its limit distribution under various assumptions on the kernel weights. In this paper we extend our analysis, looking at the class of subsampled realised kernels and we derive the limit theory for this class of estimators. We find that subsampling is highly advantageous for estimators based on discontinuous kernels, such as the truncated kernel. For kinked kernels, such as the Bartlett kernel, we show that subsampling is impotent, in the sense that subsampling has no effect on the asymptotic distribution. Perhaps surprisingly, for the efficient smooth kernels, such as the Parzen kernel, we show that subsampling is harmful as it increases the asymptotic variance. We also study the performance of subsampled realised kernels in simulations and in empirical work.     

Picking funds with confidence

We present a new approach to selecting actively managed mutual funds that uses both portfolio holdings and fund return information to eliminate funds with predicted inferior performance through a sequence of pairwise fund comparisons. Our methodology determines both the number of skilled funds and their identities, and locates funds with substantially higher risk-adjusted returns than those identified by conventional alpha-ranking methods. We find strong evidence of time-series variation in both the number of funds identified as superior using our approach, as well as in their performance across different economic states.     

Multivariate realised kernels: Consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading

We propose a multivariate realised kernel to estimate the ex-post covariation of log-prices. We show this new consistent estimator is guaranteed to be positive semi-definite and is robust to measurement error of certain types and can also handle non-synchronous trading. It is the first estimator which has these three properties which are all essential for empirical work in this area. We derive the large sample asymptotics of this estimator and assess its accuracy using a Monte Carlo study. We implement the estimator on some US equity data, comparing our results to previous work which has used returns measured over 5 or 10 min intervals. We show that the new estimator is substantially more precise.     

Integrated Covariance Estimation using High-frequency Data in the Presence of Noise

We analyze the effects of nonsynchronicity and market microstructurenoise on realized covariance type estimators. Hayashi and Yoshida(2005) propose a simple estimator that resolves the problemof nonsynchronicity and is unbiased and consistent for the integratedcovariance in the absence of noise. When noise is present, however,we find that this estimator is biased, and show how the biascan be corrected for. Ultimately, we propose a subsampling versionof the bias-corrected estimator which improves its efficiency.Empirically, we find that the usual assumption of a martingaleprice process plus an independently and identically distributed(i.i.d.) noise does not describe the dynamics of the observedprice process across stocks, which confirms the practical relevanceof our general noise specification and the estimation techniqueswe propose. Finally, a simulation experiment is carried outto complement the theoretical results.     

Intraday volatility responses to monetary policy events

This paper explores the effects of US monetary policy events on intraday volatility in the US equity markets. We examine Federal Open Market Committee (FOMC) announcements as well as real-time changes in market expectations about future policy announcements and their impact on the intraday volatility dynamics of the S&P 500 index. The analysis shows elevated intraday volatility following FOMC announcements through the market close, with a spike at the time of the announcement. We then differentiate the volatility spike by modeling an asymmetric response based on the direction of the actual target rate change. Our results suggest that the size of the volatility spike is dependent on the direction of the rate change, with expansionary monetary policy actions having a larger spike than contractionary policy actions. The duration of these volatility spikes is relatively short-lived, with the spike dampening out within 15 minutes. A more lasting impact is, however, documented for real-time changes in market expectations where the volatility spike tends to persist for at least one hour.     

REALIZED BETA GARCH: A MULTIVARIATE GARCH MODEL WITH REALIZED MEASURES OF VOLATILITY

We introduce a multivariate generalized autoregressive conditional heteroskedasticity (GARCH) model that incorporates realized measures of variances and covariances. Realized measures extract information about the current levels of volatilities and correlations from high‐frequency data, which is particularly useful for modeling financial returns during periods of rapid changes in the underlying covariance structure. When applied to market returns in conjunction with returns on an individual asset, the model yields a dynamic model specification of the conditional regression coefficient that is known as the beta. We apply the model to a large set of assets and find the conditional betas to be far more variable than usually found with rolling‐window regressions based exclusively on daily returns. In the empirical part of the paper, we examine the cross‐sectional as well as the time variation of the conditional beta series during the financial crises. Copyright © 2014 John Wiley & Sons, Ltd.     

Value, Business and Globalisation – Sketching a Critical Conceptual Framework

Value is a basic concept in economics, ethics and sociology. Locke made labour the source of value, whereas Smith referred to an ideal exchange and Kant specified that commodities only have a market price, no intrinsic value. One can distinguish two modern concepts of value, an economic one trying to explain value in terms of utility, interest or preferences, and an ideal one considering values as ends in themselves. On this basis, Durkheim constructed his theory of value, which was developed by his followers Mauss and Bouglé and further by Bataille. Their line of thought makes it possible to develop a conceptual framework, which can be used to criticise neo-liberalism, big business and the effects of globalisation, while at the same time defending the moral value of business and giving an interpretation of the anti-globalisation protests.     

Choosing the Best Volatility Models: The Model Confidence Set Approach*

This paper applies the model confidence set (MCS) procedure of Hansen, Lunde and Nason (2003) to a set of volatility models. An MCS is analogous to the confidence interval of a parameter in the sense that it contains the best forecasting model with a certain probability. The key to the MCS is that it acknowledges the limitations of the information in the data. The empirical exercise is based on 55 volatility models and the MCS includes about a third of these when evaluated by mean square error, whereas the MCS contains only a VGARCH model when mean absolute deviation criterion is used. We conduct a simulation study which shows that the MCS captures the superior models across a range of significance levels. When we benchmark the MCS relative to a Bonferroni bound, the latter delivers inferior performance.     

A Realized Variance for the Whole Day Based on Intermittent High-Frequency Data

We consider the problem of deriving an empirical measure ofdaily integrated variance (IV) in the situation where high-frequencyprice data are unavailable for part of the day. We study threeestimators in this context and characterize the assumptionsthat justify their use. We show that the optimal combinationof the realized variance and squared overnight return can bedetermined, despite the latent nature of IV, and we discussthis result in relation to the problem of combining forecasts.Finally, we apply our theoretical results and construct fouryears of daily volatility estimates for the 30 stocks of theDow Jones Industrial Average.