Estimating betas from nonsynchronous data

Nonsynchronous trading of securities introduces into the market model a potentially serious econometric problem of errors in variables. In this paper properties of the observed market model and associated ordinary least squares estimators are developed in detail. In addition, computationally convenient, consistent estimators for parameters of the market model are calculated and then applied to daily returns of securities listed in the NYSE and ASE.     

A General Stationary Stochastic Regression Model for Estimating and Predicting Beta

A beta regression model is proposed where the coefficients follow a general class of stationary stochastic processes. The procedure identifies the process and estimates the parameters of the model simultaneously from the information contained in the return series. The returns of each of the Dow Jones 30 securities are examined. Betas of 5 of the securities are nonstationary and do not appear to follow a particular form of nonstationarity. Conclusions of many earlier studies may be suspect since they are based on procedures tailored to adoption of a specific form of beta nonstationarity and, thereby, based on an erroneous a priori assumption regarding such form. The ordinary least squares model is also found to be quite robust, providing reliable beta and intercept estimates not materially different from the more complex procedure with 25 of the return series.     

Implicit pricing across residential rental submarkets

This paper examines implicit price differences of rental housing characteristics across various property types to measure whether determinants of rents are valued in the aggregate or separately. The results show that hedonic price functions are not identical across property types, which suggests that ordinary least squares is not the appropriate estimation technique when modeling the implicit prices for an aggregate rental market. Generalized least squares estimation of a random coefficient model removes the restriction of fixed parameters imposed by OLS and allows estimation of implicit prices for rental markets containing multiple property types.     

Alternative event study methodology for detecting dividend signals in the context of joint dividend and earnings announcements

Friction models are used to examine the market reaction to the simultaneous disclosure of earnings and dividends in a thin‐trading environment. Friction modelling, a procedure using maximum likelihood estimation, can be used to replace both the market model and restricted least‐squares regression in event studies where there are two quantifiable variables and a number of possible interaction effects associated with the news that constitutes the study's event. The results indicate that the dividend signal can be separated from the earnings signal.     

Pot of gold or winner's curse? An event study of the auctions of 3G mobile telephone licences in the UK

Commentators have suggested that the winning companies in the UK 3G mobile telephone auction overpaid for their licences. However, event‐study method using the market model under ordinary least squares (OLS), robust and structural time‐series estimation yields no systematic evidence of the ‘winner's curse’. Positive as well as negative one‐day wealth effects are observed amongst both winners and losers, and there is no lasting adverse market reaction to the winners, taken as a group. We conclude there is no case for easing the regulatory stance in the industry on grounds that the winners paid too much.     

Insider ownership and firm value in New Zealand

The relationship between insider stock ownership and firm value is examined for a sample of publicly traded companies in New Zealand. Results in this study confirm earlier findings of a curvilinear relationship reported for larger markets. Insider ownership and firm value are positively related for ownership levels below 14% and above 40% and inversely related at intermediate levels of ownership. These results are fairly robust to different measures of firm performance (Tobin's q, market to book ratio and return on equity) and to several different estimation techniques such as ordinary least squares, two stage least squares, seemingly unrelated regressions and fixed effects regressions on panel data over 1994–1998. Findings in this study contribute to the growing body of international evidence that the non-linear cubic relationship between insider ownership and firm value is robust to differences in governance structures across markets.     

Systematic risk estimation in the presence of large and many outliers

It is well recognized that the effect of extreme points on systematic risk estimates is not adequately captured through least squares estimation. This article uses the reweighted least median squares (RWLMS) approach, first proposed by Rousseeuw (1984), which accurately detects outlier presence. Using a large sample of 1350 NYSE/AMEX firms, the article demonstrates that least squares does indeed mask several potentially influential points, that this masking is very pervasive over the sample, and that it may persist even after conventional robust estimation techniques are applied. When these masked points are “unmasked” by RWLMS and zero weights assigned to such observations, the resulting RWLMS estimates of beta are on average 10%–15% smaller. However, a Bayesian treatment of such points (assigning a priori nonzero weights) is possible in both one and two factor market models.     

Robust Estimation of Beta Coefficients: Evidence from a Small Stock Market

In this paper we demonstrate that robust estimators improve the reliability of estimates of beta coefficients on small, thinly traded stock markets. We outline several different types of robust and bounded influence regression estimators and assess them using a jackknife methodology on data from the Johannesburg Stock Exchange. The empirical evidence confirms the hypothesis that robust estimators are more efficient than least squares estimators and indicates that least squares estimators may over-estimate systematic risk in some cases.     

Price Limits and Beta

Price limits, which restrict daily price changes of a stock within a pre-specified range, make the stochastic properties of observed returns deviate from those of true returns, and hence lead to a biased estimates of the market model parameters. To investigate the impacts of price limits on the market model parameters, especially on beta, the restricted regression analysis is performed as well as the two-pass regression analysis used in examining the intervalling effect bias on beta. Empirical results suggest that, when prices are observed within a pre-specified bound, the estimates of beta using ordinary least squares substantially understate the true beta and suffer more from the intervalling effect bias. However, the delay effect of price limits on the adjustment of a security's price does not last too long, that is, remaining information is reflected on the subsequent day's stock prices very rapidly.     

The long-horizon returns behaviour of the Portuguese stock market1

In the last few years several research studies have challenged the traditional weak-form efficiency tests of the stock market. These studies suggested an alternative to the random walk model, containing temporary and permanent components. If stocks follow such a model then the traditional tests, using returns computed for short intervals would be unable to detect them. To investigate the evidence for such models in the Portuguese stock market ten stock indexes were created. This is a pioneer study of the Portuguese stock market, and uses nominal, real and excess returns, computed for longer horizons. Three methodologies were used: variance ratios, ordinary least squares regressions and weighted least squares regressions. The statistical significance of the results was studied using traditional parametric tests as well as non-parametric tests. The evidence is mixed, as the presence of tendencies towards mean aversion and mean reversion were detected. Results also show that the evidence is very sensitive to the methodology used and the signifcance tests performed. These results, however, do not necessarily reject the weak-form market efficiency hypothesis.     

Comparison of Seasonal Anomalies across Major Equity Markets: A Note

This paper reexamines the existence of seasonal anomalies in daily stock prices by integrating seasonal patterns into a single comprehensive model that captures the joint effects of seasonal variations for each of the three major markets. This model incorporates serial correlation and corrects for non-normality by using robust regression techniques. Serial correlation is found to be important, as is the day of the week and the January variable. Furthermore, the Tuesday after a Monday holiday is significant for two markets using the robust technique (but not ordinary least squares). Finally, the day-preceding-a-holiday effect is strongly significant.     

Is Beta Dead or Alive?

The debate about whether beta is dead or alive has heated up once again. We believe the empirical work supporting either side of the argument is limited because market frictions are not adequately addressed. This study clarifies the controversy about the issue by creating a new moving-average beta and analyzing two market anomalies: the turn-of-the-year and the Monday effects. What is discovered in this research is (1) that a fundamental source of the problem underlying the two types of market anomalies is the persistence of market frictions that retard the arbitrage process; (2) that beta is seriously ill if the effects of market frictions are ignored; and (3) that beta is alive and well if the effects of market frictions are accommodated. Also, we show, by using an optimal lead/lag structure, the moving-average beta provides significantly higher explanatory power for the turn-of the-year and the Monday effects than betas created from ordinary least squares regression and Scholes-Williams and Fama-French methods because the moving-average beta accommodates the effects of market frictions into the body of beta itself. This new type of beta, a moving-average beta, is demonstrated to be robust.     

Is Beta Dead or Alive?

The debate about whether beta is dead or alive has heated up once again. We believe the empirical work supporting either side of the argument is limited because market frictions are not adequately addressed. This study clarifies the controversy about the issue by creating a new moving-average beta and analyzing two market anomalies: the turn-of-the-year and the Monday effects. What is discovered in this research is (1) that a fundamental source of the problem underlying the two types of market anomalies is the persistence of market frictions that retard the arbitrage process; (2) that beta is seriously ill if the effects of market frictions are ignored; and (3) that beta is alive and well if the effects of market frictions are accommodated. Also, we show, by using an optimal lead/lag structure, the moving-average beta provides significantly higher explanatory power for the turn-of the-year and the Monday effects than betas created from ordinary least squares regression and Scholes-Williams and Fama-French methods because the moving-average beta accommodates the effects of market frictions into the body of beta itself. This new type of beta, a moving-average beta, is demonstrated to be robust.     

Acceptance of digital investment solutions: The case of robo advisory in Germany

The financial services sector is undergoing substantial change due to technological innovation and digitalization. Traditional banks face intensifying competition through the market entry of digital investment platforms that make use of automated investment advisory, so-called robo advisors. Based on replica of two German robo advisors, a sample of 96 participants assessed their intention to use such digital investment services. The results obtained using partial least squares (PLS) path modelling indicate that perceived usefulness and privacy are the most decisive factors with a one percent higher perceived usefulness (higher privacy) increasing usage intentions by 0.57 % (0.25 %). The results are robust to various socio-demographic and FinTech-related controls as well as alternative estimation procedures such as generalized structured component analysis (GSCA).     

Combining the Effects of OLS and Spread on Futures Hedging: Evidence from the Taiwan Stock Index

Abstract:

This study proposes a dynamic hedge ratio, the combined ordinary least squares spread (COLSS), which combines the hedge ratio of ordinary least squares and the value of spread. Using this dynamic ratio for hedging with futures contracts, one can replace spot risk with spread risk. The COLSS captures not only the long-run equilibrium between spot and futures returns, but also the short-run deviation from equilibrium. The spread is forecast by one-period lagged stock market factors and high-order moments that are estimated by an options model. In the in-sample and out-of-sample tests, the COLSS strategy achieves significant risk reduction and outperforms the alternative models by a large utility improvement.     

Focusing on the worst state for robust investing

Despite its shortcomings, the Markowitz model remains the norm for asset allocation and portfolio construction. A major issue involves sensitivity of the model's solution to its input parameters. The prevailing approach employed by practitioners to overcome this problem is to use worst-case optimization. Generally, these methods have been adopted without incorporating equity market behavior and we believe that an analysis is necessary. Therefore, in this paper, we present the importance of market information during the worst state for achieving robust performance. We focus on the equity market and find that the optimal portfolio in a market with multiple states is the portfolio with robust returns and observe that focusing on the worst market state provides robust returns. Furthermore, we propose alternative robust approaches that emphasize returns during market downside periods without solving worst-case optimization problems. Through our analyses, we demonstrate the value of focusing on the worst market state and as a result find support for the value of worst-case optimization for achieving portfolio robustness.     

Are They Hedgers or Speculators? Evidence from South Korea's Political Elections

This study uses a simultaneous equation model based on a three-stage least squares estimation to offer new empirical evidence that investors are hedgers or speculators during South Korea's elections. Major investor groups include individuals, securities companies, and foreigners in the Korea Composite Stock Price Index (KOSPI 200) market. The results show that cash market volatility and futures market activity have lead behaviors with one another. However, the contemporaneous variables of cash market volatility and options market activity have only unidirectional causality. Most investors will trade futures and options contracts for speculating within the entire sample period. During political election periods, investors prefer to trade options contracts for hedging rather than futures contracts.     

Estimating yield curves of the U.S. Treasury securities: An interpolation approach

Following the approach of interpolation, this paper proposes the multiple exponential decay model to fit yield curves for both the U.S. TIPS market and the conventional Treasury security market. Several estimation methods, including the unconstrained/constrained nonlinear minimization, quadratic programming, and the iterative linear least squares, are applied to estimate the unknown parameters according to different curve‐fitting purposes. Comparisons between the proposed model and the alternatives show that the multiple exponential decay successfully (1) adapts to a variety of shapes associated with yield curves, (2) (partially) keeps in line with the economic interpretations of Nelson–Siegel summarized by Diebold and Li ( 2006 ), and (3) dominates the competing models in curve‐fitting performance measured by mean fitted‐price errors over the sample period. In addition, the exact specification of a nonparametric interpolation model is pinned down by applying three statistical tools, which enable us to jointly take into account validity, optimality, and parsimoniousness of the proposed model.     

The evolution of risk premium as a measure for intra-regional equity market integration

We estimate and test the conditional version of an international capital asset pricing model using a parsimonious multivariate GARCH process and the multivariate nonlinear least squares method. Since our approaches are fully parametric, we can recover any quantity that is a function of the first two conditional moments. Our findings strongly support using a model that includes both regional market and foreign exchange risk. However, both sources of risk are detected only when their prices are allowed to change over time. Our empirical results show clear evidence of market integration to varying degrees, explained by the US term premium and the level of market openness. Though it reaches high values during turmoil periods and exhibits an upward trend toward the end of the estimation period, the Indonesian stock market remains partially integrated into the ASEAN-5 regional market. These results suggest that diversification into Indonesian market assets continues to produce substantial profits and that asset pricing rules should reflect a state of partial integration.     

Robust and Efficient Estimation of the Tail Index of a Single-Parameter Pareto Distribution

Abstract

Estimation of the tail index parameter of a single-parameter Pareto model has wide application in actuarial and other sciences. Here we examine various estimators from the standpoint of two competing criteria: efficiency and robustness against upper outliers. With the maximum likelihood estimator (MLE) being efficient but nonrobust, we desire alternative estimators that retain a relatively high degree of efficiency while also being adequately robust. A new generalized median type estimator is introduced and compared with the MLE and several well-established estimators associated with the methods of moments, trimming, least squares, quantiles, and percentile matching. The method of moments and least squares estimators are found to be relatively deficient with respect to both criteria and should become disfavored, while the trimmed mean and generalized median estimators tend to dominate the other competitors. The generalized median type performs best overall. These findings provide a basis for revision and updating of prevailing viewpoints. Other topics discussed are applications to robust estimation of upper quantiles, tail probabilities, and actuarial quantities, such as stop-loss and excess-of-loss reinsurance premiums that arise concerning solvency of portfolios. Robust parametric methods are compared with empirical nonparametric methods, which are typically nonrobust.