The Impact of Sampling Frequency and Volatility Estimators on Change-Point Tests

The paper evaluates the performance of several recently proposed change-point tests applied to conditional variance dynamics and conditional distributions of asset returns. These are CUSUM-type tests for beta-mixing processes and EDF-based tests for the residuals of such nonlinear dependent processes. Hence the tests apply to the class of ARCH and SV type processes as well as data-driven volatility estimators using high-frequency data. It is shown that some of the high-frequency volatility estimators substantially improve the power of the structural breaks tests especially for detecting changes in the tail of the conditional distribution. Similarly, certain types of filtering and transformation of the returns process can improve the power of CUSUM statistics. We also explore the impact of sampling frequency on each of the test statistics. Ce papier évalue la performance de plusieurs tests de changement structurel CUSUM et EDF pour la structure dynamique de la variance conditionelle et de la distribution conditionnelle. Nous étudions l'impact 1) de la fréquence des observations, 2) de l'utilisation des données de haute fréquence pour le calcul des variances conditionnelles et 3) de transformation des séries pour améliorer la puissance des tests.Change-point tests, CUSUM, Kolmogorov-Smirnov, GARCH, quadratic variation, power variation, high-frequency data, location-scale distribution family, tests de changement structurel, CUSUM, Kolmogov-Smirnov, GARCH, variation quadratique, 'power variation', données de haute fréquence

Quality Control for Structural Credit Risk Models

Over the last four decades, a large number of structural models have been developed to estimate and price credit risk. The focus of the paper is on a neglected issue pertaining to fundamental shifts in the structural parameters governing default. We propose formal quality control procedures that allow risk managers to monitor fundamental shifts in the structural parameters of credit risk models. The procedures are sequential - hence apply in real time. The basic ingredients are the key processes used in credit risk analysis, such as most prominently the Merton distance to default process as well as financial returns. Moreover, while we propose different monitoring processes, we also show that one particular process is optimal in terms of minimal detection time of a break in the drift process and relates to the Radon-Nikodym derivative for a change of measure.

Test for Breaks in the Conditional Co-Movements of Asset Returns

We propose procedures designed to uncover structural breaks in the co-movements of financial markets. A reduced form approach is introduced that can be considered as a two stage method for reducing dimensionality of multivariate heteroskedastic conditional volatility models through marginalization. The main advantage is that one can use returns normalized by volatility filters that are purely data-driven and construct general conditional covariance dynamic specifications. The main thrust of our procedure is to examine change-points in the co-movements of normalized returns. We document, using a ten year period of two representative high frequency FX series, that regression models with non-Gaussian errors describe adequately their co-movements. Change-points are detected in the conditional covariance of the DM/US$and YN/US$ normalized returns over the decade 1986-1996.change-point tests, conditional covariance, high-frequency financial data, multivariate GARCH models

Monitoring for Disruptions in Financial Markets

Historical and sequential CUSUM change-point tests for strongly dependent nonlinear processes are studied. These tests are used to monitor the conditional variance of asset returns and to provide early information regarding instabilities or disruptions in financial risk. Data-driven monitoring schemes are investigated. Since the processes are strongly dependent several novel issues require special attention. One such issue is the sampling frequency. We study the power of detection as sampling frequencies vary. Analytical local power results are obtained for historical CUSUM tests and simulation evidence is presented for sequential tests. Finally, a prediction-based statistic is introduced that reduces the detection delay considerably. The prediction based formula is based on a local Brownian bridge approximation argument and provides an assessment of the likelihood of change-points. Nous étudions les tests CUSUM historiques et séquentiels pour des séries dépendantes avec des applications en finance. Pour les processus temporels, une nouvelle dimension se présente : l'effet du choix de la fréquence des observations. Un nouveau test est également proposé. Ce test est basé sur une formule de prévision locale d'un pont brownien.structural change, CUSUM, GARCH, quadratic variation, power variation, high frequency data, Brownian bridge, boundary crossing, sequential tests, local power, changement structurel, CUSUM, GARCH, variation quadratique, 'power variation', données de haute fréquence, pont Brownien, puissance locale, tests séquentiels

Detecting Multiple Breaks in Financial Market Volatility Dynamics

The paper evaluates the performance of several recently proposed tests for structural breaks in conditional variance dynamics of asset returns. The tests apply to the class of ARCH and SV type processes as well as data-driven volatility estimators using high-frequency data. In addition to testing for the presence of breaks, the statistics identify the number and location of multiple breaks. We study the size and power of the new test for detecting breaks in the second conditional variance under various realistic univariate heteroskedastic models, change-point hypotheses and sampling schemes. The paper concludes with an empirical analysis using data from the stock and FX markets for which we find multiple breaks associated with the Asian and Russian financial crises. These events resulted in changes in the dynamics of volatility of asset returns in the samples prior and post the breaks.change-point, break dates, ARCH, high-frequency data.

An Alternative Asymptotic Analysis of Residual-Based Statistics

This paper presents an alternative method to derive the limiting distribution of residual-based statistics. Our method does not impose an explicit assumption of (asymptotic) smoothness of the statistic of interest with respect to the model's parameters. and, thus, is especially useful in cases where such smoothness is difficult to establish. Instead, we use a locally uniform convergence in distribution condition, which is automatically satisfied by residual-based specification test statistics. To illustrate, we derive the limiting distribution of a new functional form specification test for discrete choice models, as well as a runs-based tests for conditional symmetry in dynamic volatility models.Le Cam's third lemma, Local Asymptotic Normality (LAN)

The relationship between private studio-based piano lessons and home-based private practice: Case studies of young piano students

This thesis examines the relationship between studio-based instrumental teaching and home-based private practice within the context of Cyprus. It focuses on practice-related behaviours and actions, as well as on factors that may influence this relationship (e.g., parental involvement, personal characteristics and motivation). Specific interest is given to the level of expertise to examine its impact on the teaching and learning of practice. Actions and behaviours of the teachers and the students are investigated through an in-depth examination of a set of private piano lessons and subsequent home-based practice sessions over a specific period of time. The research is based on six case studies, with the participants being chosen from a private music school located in Cyprus. For the purposes of this study, three different levels of musical expertise were used and assessed under the Associated Board of the Royal Schools of Music (ABRSM) criteria. The aim was to examine any influences of competency level on the relationship between studio-based lessons and students’ home-based practice. Videoed observations of the lessons and videos of the home-based practice sessions were used to gather data over four weeks, as well as data from unstructured interviews with the students and the teachers. The video data was analysed using a specially designed observation checklist drawn from the literature and results from two pilot studies, which was comprised of practice strategies, actions and behaviours found during the lessons and the practice sessions. A multi-methods approach to design and analysis was used, drawing on quantitative and qualitative perspectives to examine how categories from the observation checklist were used under different circumstances. Semi-structured interviews were also used and subsequently analysed with the use of the NVivo software. The results from all the data focus on two main themes: i) teaching methodologies that are related to practice and ii) practice strategies that were used in home-based practice by the students. Among the themes that emerged from the studio-based data were i) advisory comments of specific strategies, ii) reference to previous practice, iii) reference to future practice and iv) written notes about practice. Likewise, themes found from the home-based practice sessions were i) quantity of practice, ii) concentration while practising, iii) usage of own choice and tutor recommended practice strategies, and iv) identification of mistakes. The main findings are that the student’s level of expertise can influence the practice relationship between the studio-based lessons and the home-based practice sessions. However, according to the analysis, its influence may vary upon different teaching approaches (e.g., reference to future practice and written notes) depending on the teacher’s perceptions. Furthermore, findings revealed additional factors that can have a direct impact on the practice relationship. The teaching method applied by the teachers during the lessons was found to be one of the main factors influencing the student’s behaviour in the subsequent practice. Other factors of influence were the age of the students, their achieved level of practice development, self-regulation, personal characteristics and lastly, motivation and enjoyment of this activity. Findings also revealed several external factors that may have weight on the relationship between lessons and practice. The research showed that availability of time, other responsibilities, parental involvement, health issues and other aspects of the home environment (e.g., quite environment with no distractions) are possible influences on the studio-home practice relationship. However, their level of dynamic impact may vary with individuals. All findings are discussed in the context of this research by providing possible aspects that may influence learning within private music conservatories. A theoretical synthesis of the teaching and learning cycle is also proposed, which draws on all the findings from the six case studies. Finally, implications are made for instrumental tutors and their students

Is volatility good for growth? Evidence from the G7.

We provide empirical support for a DSGE model with nominal wage stickiness where growth is driven by learning-by-doing and money shocks and their variance are allowed to impact on long-run output growth. In our theoretical model the variance of monetary shocks has a negative effect on growth, while output volatility is good for growth as a positive relationship exists. Utilising a bivariate GARCH-M model we test the empirical conditional mean and variance relationships of nominal money and production growth rates in the G7 countries. We corroborate the theoretical model predictions with evidence from Bonferroni multiple tests across the G7.

Should macroeconomic forecasters use daily financial data and how?

We introduce easy to implement regression-based methods for predicting quarterly real economic activity that use daily financial data and rely on forecast combinations of MIDAS regressions. Our analysis is designed to elucidate the value of daily information and provide real-time forecast updates of the current (nowcasting) and future quarters. Our findings show that while on average the predictive ability of all models worsens substantially following the financial crisis, the models we propose suffer relatively less losses than the traditional ones. Moreover, these predictive gains are primarily driven by the classes of government securities, equities, and especially corporate risk.MIDAS, macro forecasting, leads, daily financial information, daily factors.