Key publications
Katsouris C (2022). Asymptotic Theory for Moderate Deviations from the Unit Boundary in. Quantile Autoregressive Time Series.
Abstract:
Asymptotic Theory for Moderate Deviations from the Unit Boundary in. Quantile Autoregressive Time Series
We establish the asymptotic theory in quantile autoregression when the model
parameter is specified with respect to moderate deviations from the unit
boundary of the form (1 + c / k) with a convergence sequence that diverges at a
rate slower than the sample size n. Then, extending the framework proposed by
Phillips and Magdalinos (2007), we consider the limit theory for the
near-stationary and the near-explosive cases when the model is estimated with a
conditional quantile specification function and model parameters are
quantile-dependent. Additionally, a Bahadur-type representation and limiting
distributions based on the M-estimators of the model parameters are derived.
Specifically, we show that the serial correlation coefficient converges in
distribution to a ratio of two independent random variables. Monte Carlo
simulations illustrate the finite-sample performance of the estimation
procedure under investigation.
Abstract.
Author URL.
Katsouris C (2022). Partial Sum Processes of Residual-Based and Wald-type Break-Point. Statistics in Time Series Regression Models.
Abstract:
Partial Sum Processes of Residual-Based and Wald-type Break-Point. Statistics in Time Series Regression Models
We revisit classical asymptotics when testing for a structural break in
linear regression models by obtaining the limit theory of residual-based and
Wald-type processes. First, we establish the Brownian bridge limiting
distribution of these test statistics. Second, we study the asymptotic
behaviour of the partial-sum processes in nonstationary (linear) time series
regression models. Although, the particular comparisons of these two different
modelling environments is done from the perspective of the partial-sum
processes, it emphasizes that the presence of nuisance parameters can change
the asymptotic behaviour of the functionals under consideration. Simulation
experiments verify size distortions when testing for a break in nonstationary
time series regressions which indicates that the Brownian bridge limit cannot
provide a suitable asymptotic approximation in this case. Further research is
required to establish the cause of size distortions under the null hypothesis
of parameter stability.
Abstract.
Author URL.
Publications by year
2022
Katsouris C (2022). Asymptotic Theory for Moderate Deviations from the Unit Boundary in. Quantile Autoregressive Time Series.
Abstract:
Asymptotic Theory for Moderate Deviations from the Unit Boundary in. Quantile Autoregressive Time Series
We establish the asymptotic theory in quantile autoregression when the model
parameter is specified with respect to moderate deviations from the unit
boundary of the form (1 + c / k) with a convergence sequence that diverges at a
rate slower than the sample size n. Then, extending the framework proposed by
Phillips and Magdalinos (2007), we consider the limit theory for the
near-stationary and the near-explosive cases when the model is estimated with a
conditional quantile specification function and model parameters are
quantile-dependent. Additionally, a Bahadur-type representation and limiting
distributions based on the M-estimators of the model parameters are derived.
Specifically, we show that the serial correlation coefficient converges in
distribution to a ratio of two independent random variables. Monte Carlo
simulations illustrate the finite-sample performance of the estimation
procedure under investigation.
Abstract.
Author URL.
Katsouris C (2022). Partial Sum Processes of Residual-Based and Wald-type Break-Point. Statistics in Time Series Regression Models.
Abstract:
Partial Sum Processes of Residual-Based and Wald-type Break-Point. Statistics in Time Series Regression Models
We revisit classical asymptotics when testing for a structural break in
linear regression models by obtaining the limit theory of residual-based and
Wald-type processes. First, we establish the Brownian bridge limiting
distribution of these test statistics. Second, we study the asymptotic
behaviour of the partial-sum processes in nonstationary (linear) time series
regression models. Although, the particular comparisons of these two different
modelling environments is done from the perspective of the partial-sum
processes, it emphasizes that the presence of nuisance parameters can change
the asymptotic behaviour of the functionals under consideration. Simulation
experiments verify size distortions when testing for a break in nonstationary
time series regressions which indicates that the Brownian bridge limit cannot
provide a suitable asymptotic approximation in this case. Further research is
required to establish the cause of size distortions under the null hypothesis
of parameter stability.
Abstract.
Author URL.
2021
Katsouris C (2021). Forecast Evaluation in Large Cross-Sections of Realized Volatility.
Abstract:
Forecast Evaluation in Large Cross-Sections of Realized Volatility
In this paper, we consider the forecast evaluation of realized volatility
measures under cross-section dependence using equal predictive accuracy testing
procedures. We evaluate the predictive accuracy of the model based on the
augmented cross-section when forecasting Realized Volatility. Under the null
hypothesis of equal predictive accuracy the benchmark model employed is a
standard HAR model while under the alternative of non-equal predictive accuracy
the forecast model is an augmented HAR model estimated via the LASSO shrinkage.
We study the sensitivity of forecasts to the model specification by
incorporating a measurement error correction as well as cross-sectional jump
component measures. The out-of-sample forecast evaluation of the models is
assessed with numerical implementations.
Abstract.
Author URL.
Katsouris C (2021). Optimal Portfolio Choice and Stock Centrality for Tail Risk Events.
Abstract:
Optimal Portfolio Choice and Stock Centrality for Tail Risk Events
We propose a novel risk matrix to characterize the optimal portfolio choice
of an investor with tail concerns. The diagonal of the matrix contains the
Value-at-Risk of each asset in the portfolio and the off-diagonal the pairwise
Delta-CoVaR measures reflecting tail connections between assets. First, we
derive the conditions under which the associated quadratic risk function has a
closed-form solution. Second, we examine the relationship between portfolio
risk and eigenvector centrality. Third, we show that portfolio risk is not
necessarily increasing with respect to stock centrality. Forth, we demonstrate
under certain conditions that asset centrality increases the optimal weight
allocation of the asset to the portfolio. Overall, our empirical study
indicates that a network topology which exhibits low connectivity is
outperformed by high connectivity based on a Sharpe ratio test.
Abstract.
Author URL.
Katsouris C (2021). Sequential Break-Point Detection in Stationary Time Series: An. Application to Monitoring Economic Indicators.
Abstract:
Sequential Break-Point Detection in Stationary Time Series: An. Application to Monitoring Economic Indicators
Monitoring economic conditions and financial stability with an early warning
system serves as a prevention mechanism for unexpected economic events. In this
paper, we investigate the statistical performance of sequential break-point
detectors for stationary time series regression models with extensive
simulation experiments. We employ an online sequential scheme for monitoring
economic indicators from the European as well as the American financial markets
that span the period during the 2008 financial crisis. Our results show that
the performance of these tests applied to stationary time series regressions
such as the AR(1) as well as the AR(1)-GARCH(1,1) depend on the severity of the
break as well as the location of the break-point within the out-of-sample
period. Consequently, our study provides some useful insights to practitioners
for sequential break-point detection in economic and financial conditions.
Abstract.
Author URL.
Katsouris C (2021). Treatment effect validation via a permutation test in Stata.
Abstract:
Treatment effect validation via a permutation test in Stata
In this paper we describe the testing procedure for assessing the statistical
significance of treatment effect under the experimental conditions of baseline
imbalance across covariates and attrition from the survey, using the
permutation tests proposed by Freedman and Lane (1983) and Romano and Wolf
(2016). We discuss the testing procedure for these hypotheses based on a linear
regression model and introduce the new Stata command [R] permtest for the
implementation of the permutation test in Stata. Moreover, we investigate the
finite-sample performance as well as the statistical validity of the test with
a Monte Carlo simulation study in which we examine the empirical size and power
properties under the conditions of baseline imbalance and attrition for a fixed
number of permutation steps.
Abstract.
Author URL.
