This paper proposes a framework for hypothesis testing in the presence of nonlinearity and nonsta... more This paper proposes a framework for hypothesis testing in the presence of nonlinearity and nonstationarity. The focus is test hypotheses which cannot be treated using either unit root asymptotics and/or nonlinear functional central limit theorem results ).
Rationality of early release data is typically tested using linear regressions. Thus, failure to ... more Rationality of early release data is typically tested using linear regressions. Thus, failure to reject the null does not rule out the possibility of nonlinear dependence. This paper proposes two tests which instead have power against generic nonlinear alternatives. A Monte Carlo study shows that the suggested tests have good finite sample properties. Additionally, we carry out an empirical illustration using a real-time dataset for money, output, and prices. Overall, we find strong evidence against data rationality. Interestingly, for money stock the null is not rejected by linear tests but is rejected by our tests.
We take a model selection approach to the question of whether forward interest rates are useful i... more We take a model selection approach to the question of whether forward interest rates are useful in predicting future spot rates, using a variety of out-of-sample forecast-based model selection criteria: forecast mean squared error, forecast direction accuracy, and forecast-based trading system profitability. We also examine the usefulness of a class of novel prediction models called "artificial neural networks," and investigate the issue of appropriate window sizes for rolling-window-based prediction methods. Results indicate that the premium of the forward rate over the spot rate helps to predict the sign of future changes in the interest rate. Further, model selection based on an in-sample Schwarz Information Criterion (SIC) does not appear to be a reliable guide to out-of-sample performance, in the case of short-term interest rates. Thus, the in-sample SIC apparently fails to offer a convenient shortcut to true out-of-sample performance measures.
Journal of the American Statistical Association, Jan 24, 2012
In recent years, numerous volatility-based derivative products have been engineered. This has led... more In recent years, numerous volatility-based derivative products have been engineered. This has led to interest in constructing conditional predictive densities and confidence intervals for integrated volatility. In this paper, we propose nonparametric kernel estimators of the aforementioned quantities. The kernel functions used in our analysis are based on different realized volatility measures, which are constructed using the ex post variation of asset prices. A set of sufficient conditions under which the estimators are asymptotically equivalent to their unfeasible counterparts, based on an unobservable volatility process, is provided. Additionally, asymptotic normality is established. Based on the interpretation of related asymptotic properties that we derive, we additionally comment on a number of issues, including: the effects of using inconsistent volatility estimators in the presence of jumps and microstructure noise; and the asymptotic trade-offs between using robust realized volatility measures versus non-robust measures with differing data frequencies and different noise magnitudes, for example. The efficacy of the proposed estimators is examined via Monte Carlo experimentation, and an empirical illustration based upon data from the New York Stock Exchange is provided.
The purpose of this paper is to add to the empirical evidence on the efficacy of alternative simu... more The purpose of this paper is to add to the empirical evidence on the efficacy of alternative simulation models of the short term interest rate. This is done by constructing consistent specification tests that allow us to carry out a "horse-race" comparing various one, two, and three factor models (possibly with jumps), across multiple historical sample periods. We begin by outlining a three factor version of the simulation based specification test of Bhardwaj, Corradi and Swanson (BCS: 2008), which is based on a comparison of simulated and true conditional distributions and confidence intervals. Our evaluation involves comparing six affine models of the short rate during four historical periods, referred to as: "Post Bretton-Woods"; "Pre-1990s"; "The Stable 1990s"; and "Post 1990s". Based on the examination of Eurodollar rate data, we find that the CIR model, which is often rejected in the literature, performs best among the candidate models in "The Stable 1990s", while there is little to choose between one and two factor models when considering the "Post 1990s" period. Examination of "Pre 1990s" data, on the other hand, suggests there is little to choose between 2 and 3 factor models, and the one factor CIR model performs poorly. Moreover, under the "Post Bretton-Woods" period, the "best" performer is the three factor CHEN (1996) model examined by Andersen, . We conclude that the choice of model for simulating the future distribution of short rates is highly sample dependent.
... in the Presence of Integrated and Cointegrated Economic Variables Norman R. Swanson Rutgers U... more ... in the Presence of Integrated and Cointegrated Economic Variables Norman R. Swanson Rutgers University, New Brunswick, New Jersey, USA Ataman Ozyildirim The ... This test is hereafter referred to as the TYDL test.) In particular, it turns out that asymptotic chi-squared tests of ...
This paper proposes a framework for hypothesis testing in the presence of nonlinearity and nonsta... more This paper proposes a framework for hypothesis testing in the presence of nonlinearity and nonstationarity. The focus is test hypotheses which cannot be treated using either unit root asymptotics and/or nonlinear functional central limit theorem results ).
Rationality of early release data is typically tested using linear regressions. Thus, failure to ... more Rationality of early release data is typically tested using linear regressions. Thus, failure to reject the null does not rule out the possibility of nonlinear dependence. This paper proposes two tests which instead have power against generic nonlinear alternatives. A Monte Carlo study shows that the suggested tests have good finite sample properties. Additionally, we carry out an empirical illustration using a real-time dataset for money, output, and prices. Overall, we find strong evidence against data rationality. Interestingly, for money stock the null is not rejected by linear tests but is rejected by our tests.
We take a model selection approach to the question of whether forward interest rates are useful i... more We take a model selection approach to the question of whether forward interest rates are useful in predicting future spot rates, using a variety of out-of-sample forecast-based model selection criteria: forecast mean squared error, forecast direction accuracy, and forecast-based trading system profitability. We also examine the usefulness of a class of novel prediction models called "artificial neural networks," and investigate the issue of appropriate window sizes for rolling-window-based prediction methods. Results indicate that the premium of the forward rate over the spot rate helps to predict the sign of future changes in the interest rate. Further, model selection based on an in-sample Schwarz Information Criterion (SIC) does not appear to be a reliable guide to out-of-sample performance, in the case of short-term interest rates. Thus, the in-sample SIC apparently fails to offer a convenient shortcut to true out-of-sample performance measures.
Journal of the American Statistical Association, Jan 24, 2012
In recent years, numerous volatility-based derivative products have been engineered. This has led... more In recent years, numerous volatility-based derivative products have been engineered. This has led to interest in constructing conditional predictive densities and confidence intervals for integrated volatility. In this paper, we propose nonparametric kernel estimators of the aforementioned quantities. The kernel functions used in our analysis are based on different realized volatility measures, which are constructed using the ex post variation of asset prices. A set of sufficient conditions under which the estimators are asymptotically equivalent to their unfeasible counterparts, based on an unobservable volatility process, is provided. Additionally, asymptotic normality is established. Based on the interpretation of related asymptotic properties that we derive, we additionally comment on a number of issues, including: the effects of using inconsistent volatility estimators in the presence of jumps and microstructure noise; and the asymptotic trade-offs between using robust realized volatility measures versus non-robust measures with differing data frequencies and different noise magnitudes, for example. The efficacy of the proposed estimators is examined via Monte Carlo experimentation, and an empirical illustration based upon data from the New York Stock Exchange is provided.
The purpose of this paper is to add to the empirical evidence on the efficacy of alternative simu... more The purpose of this paper is to add to the empirical evidence on the efficacy of alternative simulation models of the short term interest rate. This is done by constructing consistent specification tests that allow us to carry out a "horse-race" comparing various one, two, and three factor models (possibly with jumps), across multiple historical sample periods. We begin by outlining a three factor version of the simulation based specification test of Bhardwaj, Corradi and Swanson (BCS: 2008), which is based on a comparison of simulated and true conditional distributions and confidence intervals. Our evaluation involves comparing six affine models of the short rate during four historical periods, referred to as: "Post Bretton-Woods"; "Pre-1990s"; "The Stable 1990s"; and "Post 1990s". Based on the examination of Eurodollar rate data, we find that the CIR model, which is often rejected in the literature, performs best among the candidate models in "The Stable 1990s", while there is little to choose between one and two factor models when considering the "Post 1990s" period. Examination of "Pre 1990s" data, on the other hand, suggests there is little to choose between 2 and 3 factor models, and the one factor CIR model performs poorly. Moreover, under the "Post Bretton-Woods" period, the "best" performer is the three factor CHEN (1996) model examined by Andersen, . We conclude that the choice of model for simulating the future distribution of short rates is highly sample dependent.
... in the Presence of Integrated and Cointegrated Economic Variables Norman R. Swanson Rutgers U... more ... in the Presence of Integrated and Cointegrated Economic Variables Norman R. Swanson Rutgers University, New Brunswick, New Jersey, USA Ataman Ozyildirim The ... This test is hereafter referred to as the TYDL test.) In particular, it turns out that asymptotic chi-squared tests of ...
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