Browsing by Author "Patton, AJ"
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Item Open Access Asymptotic Inference about Predictive Accuracy Using High Frequency Data(Economic Research Initiatives at Duke (ERID) Working Paper, 2013-07-06) Li, J; Patton, AJThis paper provides a general framework that enables many existing inference methods for predictive accuracy to be used in applications that involve forecasts of latent target variables. Such applications include the forecasting of volatility, correlation, beta, quadratic variation, jump variation, and other functionals of an underlying continuous-time process. We provide primitive conditions under which a "negligibility" result holds, and thus the asymptotic size of standard predictive accuracy tests, implemented using a high-frequency proxy for the latent variable, is controlled. An extensive simulation study verifies that the asymptotic results apply in a range of empirically relevant applications, and an empirical application to correlation forecasting is presented.Item Open Access Comparing Possibly Misspecified Forecasts(Journal of Business and Economic Statistics, 2019-01-01) Patton, AJ© 2019, © 2019 American Statistical Association. Recent work has emphasized the importance of evaluating estimates of a statistical functional (such as a conditional mean, quantile, or distribution) using a loss function that is consistent for the functional of interest, of which there is an infinite number. If forecasters all use correctly specified models free from estimation error, and if the information sets of competing forecasters are nested, then the ranking induced by a single consistent loss function is sufficient for the ranking by any consistent loss function. This article shows, via analytical results and realistic simulation-based analyses, that the presence of misspecified models, parameter estimation error, or nonnested information sets, leads generally to sensitivity to the choice of (consistent) loss function. Thus, rather than merely specifying the target functional, which narrows the set of relevant loss functions only to the class of loss functions consistent for that functional, forecast consumers or survey designers should specify the single specific loss function that will be used to evaluate forecasts. An application to survey forecasts of U.S. inflation illustrates the results.Item Open Access Comparing Predictive Accuracy in the Presence of a Loss Function Shape Parameter(Journal of Business and Economic Statistics, 2021-01-01) Barendse, S; Patton, AJWe develop tests for out-of-sample forecast comparisons based on loss functions that contain shape parameters. Examples include comparisons using average utility across a range of values for the level of risk aversion, comparisons of forecast accuracy using characteristics of a portfolio return across a range of values for the portfolio weight vector, and comparisons using recently-proposed “Murphy diagrams” for classes of consistent scoring rules. An extensive Monte Carlo study verifies that our tests have good size and power properties in realistic sample sizes, particularly when compared with existing methods which break down when then number of values considered for the shape parameter grows. We present three empirical illustrations of the new test.Item Open Access Daily House Price Indexes: Construction, Modeling, and Longer-Run Predictions(Economic Research Initiatives at Duke (ERID), 2013-06-11) Bollerslev, T; Patton, AJ; Wang, WWe construct daily house price indexes for ten major U.S. metropolitan areas. Our calculations are based on a comprehensive database of several million residential property transactions and a standard repeat-sales method that closely mimics the procedure used in the construction of the popular monthly Case-Shiller house price indexes. Our new daily house price indexes exhibit similar characteristics to other daily asset prices, with mild autocorrelation and strong conditional heteroskedasticity, which are well described by a relatively simple multivariate GARCH type model. The sample and model-implied correlations across house price index returns are low at the daily frequency, but rise monotonically with the return horizon, and are all commensurate with existing empirical evidence for the existing monthly and quarterly house price series. A simple model of daily house price index returns produces forecasts of monthly house price changes that are superior to various alternative forecast procedures based on lower frequency data, underscoring the informational advantages of our new more finely sampled daily price series.Item Open Access Dynamic copula models and high frequency data(Journal of Empirical Finance, 2015-01-01) Patton, AJ; De Lira Salvatierra, I© 2014 Elsevier B.V.This paper proposes a new class of dynamic copula models for daily asset returns that exploits information from high frequency (intra-daily) data. We augment the generalized autoregressive score (GAS) model of Creal et al. (2013) with high frequency measures such as realized correlation to obtain a "GRAS" model. We find that the inclusion of realized measures significantly improves the in-sample fit of dynamic copula models across a range of U.S. equity returns. Moreover, we find that out-of-sample density forecasts from our GRAS models are superior to those from simpler models. Finally, we consider a simple portfolio choice problem to illustrate the economic gains from exploiting high frequency data for modeling dynamic dependence.Item Open Access From zero to hero: Realized partial (co)variances(Journal of Econometrics, 2021-01-01) Bollerslev, T; Medeiros, MC; Patton, AJ; Quaedvlieg, RThis paper proposes a generalization of the class of realized semivariance and semicovariance measures introduced by Barndorff-Nielsen et al. (2010) and Bollerslev et al. (2020a) to allow for a finer decomposition of realized (co)variances. The new “realized partial (co)variances” allow for multiple thresholds with various locations, rather than the single fixed threshold of zero used in semi (co)variances. We adopt methods from machine learning to choose the thresholds to maximize the out-of-sample forecast performance of time series models based on realized partial (co)variances. We find that in low dimensional settings it is hard, but not impossible, to improve upon the simple fixed threshold of zero. In large dimensions, however, the zero threshold embedded in realized semi covariances emerges as a robust choice.Item Open Access Multivariate Leverage Effects and Realized Semicovariance GARCH Models(2018-04-16) Bollerslev, T; Patton, AJ; Quaedvlieg, RItem Open Access Realized semibetas: Disentangling “good” and “bad” downside risks(Journal of Financial Economics, 2021-01-01) Bollerslev, T; Patton, AJ; Quaedvlieg, RWe propose a new decomposition of the traditional market beta into four semibetas that depend on the signed covariation between the market and individual asset returns. We show that semibetas stemming from negative market and negative asset return covariation predict significantly higher future returns, while semibetas attributable to negative market and positive asset return covariation predict significantly lower future returns. The two semibetas associated with positive market return variation do not appear to be priced. The results are consistent with the pricing implications from a mean-semivariance framework combined with arbitrage risk driving a wedge between the risk premiums for long and short positions. We conclude that rather than betting against the traditional market beta, it is better to bet on and against the “right” semibetas.Item Open Access Realized Semicovariances: Looking for Signs of Direction Inside the Covariance Matrix(Economic Research Initiatives at Duke (ERID) Working Paper, 2017-09-05) Bollerslev, T; Li, J; Patton, AJ; Quaedvlieg, RItem Open Access Risk Price Variation: The Missing Half of Empirical Asset Pricing(Economic Research Initiatives at Duke (ERID) Working Paper, 2019-05-24) Patton, AJ; Weller, BMItem Open Access Testing for Unobserved Heterogeneity via K-Means Clustering(2019-07-15) Patton, AJ; Weller, BMItem Open Access The Impact of Hedge Funds on Asset Markets(2015-08-08) Kruttli, MS; Patton, AJ; Ramadorai, TThis paper provides evidence of the impact of hedge funds on asset markets. We construct a simple measure of the aggregate illiquidity of hedge fund portfolios, based on the cross-sectional average first order autocorrelation coefficient of hedge fund returns, and show that it has strong and robust in- and out-of-sample forecasting power for 72 portfolios of international equities, corporate bonds, and currencies over the 1994 to 2013 period. The forecasting ability of hedge fund illiquidity for asset returns is in most cases greater than, and provides independent information relative to, well-known predictive variables for each of these asset classes. We rationalize these findings using a simple equilibrium model in which hedge funds provide liquidity in asset markets.Item Open Access Time-Varying Systemic Risk: Evidence from a Dynamic Copula Model of CDS Spreads(Economic Research Initiatives at Duke (ERID) Working Paper, 2013-05-23) Oh, DH; Patton, AJThis paper proposes a new class of copula-based dynamic models for high dimension conditional distributions, facilitating the estimation of a wide variety of measures of systemic risk. Our proposed models draw on successful ideas from the literature on modeling high dimension covariance matrices and on recent work on models for general time-varying distributions. Our use of copula-based models enable the estimation of the joint model in stages, greatly reducing the computational burden. We use the proposed new models to study a collection of daily credit default swap (CDS) spreads on 100 U.S. firms over the period 2006 to 2012. We find that while the probability of distress for individual firms has greatly reduced since the financial crisis of 2008-09, the joint probability of distress (a measure of systemic risk) is substantially higher now than in the pre-crisis period.Item Open Access What You See is Not What You Get: The Costs of Trading Market Anomalies(2017-10-27) Patton, AJ; Weller, BM