Non-Parametric Statistic for Testing Cumulative Abnormal Stock Returns

Due to the non-normality of stock returns, nonparametric rank tests are gaining accceptance relative to parametric tests in financial economics event studies. In rank tests, financial assets’ multiple day cumulative abnormal returns (CARs) are replaced by cumulated ranks. This paper proposes modifications to the existing approaches to improve robustness to cross-sectional correlation of returns arising from calendar time overlapping event windows. Simulations show that the proposed rank test is well specified in testing CARs and is robust towards both complete and partial overlapping event windows.© 2022 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

The joint distribution of a linear transformation of internally studentized least squares residuals

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Long-range dependence in the returns and volatility of the Finnish housing market

Purpose - The purpose of this paper is to examine the evidence of long-range dependence behaviour in both house price returns and volatility for fifteen main regions in Finland over the period of 1988:Q1 to 2018:Q4. These regions are divided geographically into 45 cities and sub-areas according to their postcode numbers. The studied type of dwellings is apartments (block of flats) divided into one-room, two-rooms, and more than three rooms apartments types. Design/methodology/approach - For each house price return series, both parametric and semiparametric long memory approaches are used to estimate the fractional differencing parameter d in an autoregressive fractional integrated moving average [ARFIMA (p, d, q)] process. Moreover, for cities and sub-areas with significant clustering effects (autoregressive conditional heteroscedasticity [ARCH] effects), the semiparametric long memory method is used to analyse the degree of persistence in the volatility by estimating the fractional differencing parameter d in both squared and absolute price returns. Findings - A higher degree of predictability was found in all three apartments types price returns with the estimates of the long memory parameter constrained in the stationary and invertible interval, implying that the returns of the studied types of dwellings are long-term dependent. This high level of persistence in the house price indices differs from other assets, such as stocks and commodities. Furthermore, the evidence of long-range dependence was discovered in the house price volatility with more than half of the studied samples exhibiting long memory behaviour. Research limitations/implications - Investigating the long memory behaviour in both returns and volatility of the house prices is crucial for investment, risk and portfolio management. One reason is that the evidence of long-range dependence in the housing market returns suggests a high degree of predictability of the asset. The other reason is that the presence of long memory in the housing market volatility aids in the development of appropriate time series volatility forecasting models in this market. The study outcomes will be used in modelling and forecasting the volatility dynamics of the studied types of dwellings. The quality of the data limits the analysis and the results of the study. Originality/value - To the best of the authors’ knowledge, this is the first research that assesses the long memory behaviour in the Finnish housing market. Also, it is the first study that evaluates the volatility of the Finnish housing market using data on both municipal and geographical level.fi=vertaisarvioitu|en=peerReviewed

Testing for cumulative abnormal returns in event studies with the rank test

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Event-study methodology : Correction for cross-sectional correlation in standardized abnormal return tests

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Distribución de las transformaciones lineales de los residuos mínimos cuadrados studentizados internamente

Los residuos de regresión por mínimos cuadrados ordinarios tienen una distribución que depende de un parámetro escalar. El término “Studentización” se utiliza comúnmente para describir una cantidad U dependiente de un parámetro de escala dividida por una estimación de escala S, de forma que el ratio resultante, U/S, sigue una distribución que no tiene el inconveniente del parámetro de escala desconocido. La Studentización externa hace referencia a un ratio en que el numerador y el denominador son independientes, mientras que la Studentización interna se refiere al ratio en que ambos son dependientes. La ventaja de la Studentización interna es que puede utilizarse cualquier estimador de escala común, mientras que en la Studentización externa, cada residuo es obtenido por un estimador de escala diferente, con el fin de alcanzar la independencia. Con errores de regresión normales, la distribución conjunta de un conjunto arbitrario (linealmente independiente) de residuos Studentizados internamente está bien documentada. Sin embargo, en algunas aplicaciones una combinación lineal de residuos internamente Studentizados puede resultar útil. Sus limitaciones han sido bien documentadas, pero la distribución no parece haberse derivado en la literatura. Este trabajo contribuye a la literatura existente, en el sentido de obtener la distribución conjunta de una transformación arbitraria lineal de residuos de regresión por mínimos cuadrados ordinarios internamente Studentizados con distribución esférica de error. Todas las principales versiones de los residuos de regresión internamente Studentizados que se han utilizado comúnmente en la literatura son casos especiales de la transformación linea

Dynamic equilibrium correction modelling of yen Eurobond credit spreads

Understanding the long term relationship between the yields of risky and riskless bonds is a critical task for portfolio managers and policy makers. This study specifies an equilibrium correction model of the credit spreads between Japanese Government bonds (JGBs) and Japanese yen Eurobonds with high quality credit ratings. The empirical results indicate that the corporate bond yields are cointegrated with the otherwise equivalent JGB yields, with the spread defining the cointegration relation. In addition the results indicate that the equilibrium correction term is highly statistically significant in modelling credit spread changes. Another important factor is the risk-free interest rate with the negative sign, while there is little evidence of the contribution of the asset return to the behaviour of spreads.

Maximum likelihood estimators from discrete data modeled by mixed fractional Brownian motion with application to the Nordic stock markets

Mixed fractional Brownian motion is a linear combination of Brownian motion and independent Fractional Brownian motion that is extensively used for option pricing. The consideration of the mixed process is able to capture the long–range dependence property that financial time series exhibit. This paper examines the problem of deriving simultaneously the estimators of all the unknown parameters for a model driven by the mixed fractional Brownian motion using the maximum likelihood estimation method. The consistency and asymptotic normality properties of these estimators are provided. The performance of the methodology is tested on simulated data sets, and the outcomes illustrate that the maximum likelihood technique is efficient and reliable. An empirical application of the proposed method is also made to the real financial data from four Nordic stock market indices.©2020 Taylor & Francis Group, LLC. This is an Accepted Manuscript of an article published by Taylor & Francis in Communications in statistics: simulation and computation on 30 May 2020, available online: http://www.tandfonline.com/10.1080/03610918.2020.1764581.fi=vertaisarvioitu|en=peerReviewed

What drives correlation between stock market returns? : International evidence

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Further evidence on long-run abnormal returns after corporate events

This paper investigates abnormal standardized returns (ASRs) after major corporate events. Dutta, Knif, Kolari, and Pynnonen (2018) have shown that the ASR t-test has superior size and power compared to traditional test statistics. Based on this new test statistic compared to traditional test methods, we re-examine long-run abnormal returns after mergers and acquisitions, initial public offerings, seasoned equity offerings, dividend initiations, stock repurchases, stock splits, and reverse stock splits. While some recent studies report disappearing long-run event effects over time, our ASR tests in different subperiods from 1980 to 2015 detect significant long-run abnormal returns after these corporate actions. Graphical analyses of ASRs further support our statistical test results. We conclude that long-run abnormal returns persist after major corporate events.©2020 Elsevier. This manuscript version is made available under the Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY–NC–ND 4.0) license, https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed