Constructing Commercial Indices: A Semiparametric Adaptive Estimator Approach

Constant-quality commercial indices generated by ordinary least squares may suffer an efficiency loss due to leptokurtosis caused by outliers in transactions data. When the subsequent nonnormality occurs, substantial improvement in index precision is obtained by estimating the hedonic model using a semiparametric adaptive estimator technique. When this method was applied to 1,846 office transactions that occurred in the Phoenix metropolitan area from January 1997 through June 2004, a substantial standard error reduction of approximately 9% was realized relative to ordinary least squares estimates. The difference in average returns between the semiparametric method and ordinary least squares was about 0.25% in each period, which represents a substantial increase in commercial property index precision. JEL Classification C4 R0     

Financial Development and Economic Growth: Do Outliers Matter?

This study examines the effect of outliers on causal relationship between financial development and economic growth using 48 countries from 1988 to 2014. The dynamic panel model of Levine, Loayza, and Beck (2000) is used to examine this issue. We propose a novel approach by combining the least square dummy variable correction method (LSDVC) to remove the estimates bias in the dynamic panel model and the least trimmed squares (LTS) to control outlier influence. The combination of these two methods is referred to as LSDVC + LTS. Our results show a counter-intuitive evidence that bank development negatively affects economic growth when the outlier influence is ignored. This counter-intuitive evidence holds even when the conventional winsorization method is used to control the outliers. However, bank development exhibits a positive influence on economic growth once the proposed approach LSDVC + LTS is adopted. Also, stock market development exhibits a positive effect on economic growth regardless of the outliers.     

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.     

Return Distributions and Improved Tests of Asset Pricing Models

We compare and contrast some existing ordinary least squares(OLS)- and generalized method of moments (GMM)-based tests ofasset pricing models with a new more general test. This newtest is valid under the assumption that returns are ellipticallydistributed, a necessary and sufficient assumption of the linearcapital asset pricing model (CAPM). This new test fails to rejectthe CAPM on a dataset of stocks sorted by market valuations,whereas similar tests constructed from OLS and GMM estimationmethods reject the linear CAPM. We also find that outliers reducethe OLS-estimated mispricing of the linear CAPM on monthly returnssorted by previous performance, that is, momentum. Monte Carloevidence supports superior size and power properties of thenew test relative to OLS- and GMM-based tests.     

A New Method for Identifying the Effects of Foreign Exchange Interventions

Central banks react even to intraday changes in the exchange rate; however, in most cases, intervention data are available only at a daily frequency. This temporal aggregation makes it difficult to identify the effects of interventions on the exchange rate. We apply the Bayesian Markov‐chain Monte Carlo (MCMC) approach to this endogeneity problem. We use “data augmentation” to obtain intraday intervention amounts and estimate the efficacy of interventions using the augmented data. Applying this new method to Japanese data, we find that an intervention of 1 trillion yen moves the yen/dollar rate by 1.8%, which is more than twice as much as the magnitude reported in previous studies applying ordinary least squares to daily observations. This shows the quantitative importance of the endogeneity problem due to temporal aggregation.     

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.     

A Robustification of the Chain-Ladder Method

Abstract

In a non-life insurance business an insurer often needs to build up a reserve to able to meet his or her future obligations arising from incurred but not reported completely claims. To forecast these claims reserves, a simple but generally accepted algorithm is the classical chain-ladder method. Recent research essentially focused on the underlying model for the claims reserves to come to appropriate bounds for the estimates of future claims reserves. Our research concentrates on scenarios with outlying data. On closer examination it is demonstrated that the forecasts for future claims reserves are very dependent on outlying observations. The paper focuses on two approaches to robustify the chain-ladder method: the first method detects and adjusts the outlying values, whereas the second method is based on a robust generalized linear model technique. In this way insurers will be able to find a reserve that is similar to the reserve they would have found if the data contained no outliers. Because the robust method flags the outliers, it is possible to examine these observations for further examination. For obtaining the corresponding standard errors the bootstrapping technique is applied. The robust chain-ladder method is applied to several run-off triangles with and without outliers, showing its excellent performance.     

A Primer on Piecewise Parabolic Multiple Regression Analysis via Estimations of Chicago CBD Land Prices

This article proposes a non-parametric method for estimating spatial price functions. Space is divided into squares. The independent variables are barycentric coordinates that uniquely describe the location of observations in space. The regression coefficients are estimates of the height of the function directly over the vertices of the square spatial units. Within each square the function has hyperbolic iso-price curves and parabolic sections. The price function is continuous, but not differentiable, at the boundaries between contiguous squares. This method is applied to the problem of describing the price per front foot of land in the Chicago CBD. A rather complex price surface is revealed that would be difficult to estimate using other methodologies but was easily estimated by this simple method.     

Informational Efficiency: Which Institutions Matter?

I study the role of institutional investors to play in the relative informational efficiency of transaction prices, measured by departures from a random walk, using a sample of the constituent stocks of the Shanghai 180 composite index from 2004 to 2005. I find that greater informational efficiency is associated with larger institutional holdings. Meanwhile, this result is not driven by the variations in liquidity measures. In addition, the evidence also indicates that all institutions have a strong preference for mispriced stocks, firms with good corporate governance, and large-cap and profitable firms. If the potential endogeneity and outliers are considered, the findings of three-stage least squares and median regressions suggest that larger shareholdings by independent institutions, particularly by mutual funds and social security fund, lead to a more efficient market because of their fewer potential business connections with invested companies.     

A Semiparametric Method for Valuing Residential Locations: Application to Automated Valuation

This paper is motivated by automated valuation systems, which would benefit from an ability to estimate spatial variation in location value. It develops theory for the local regression model (LRM), a semiparametric approach to estimating a location value surface. There are two parts to the LRM: (1) an ordinary least square (OLS) model to hold constant for interior square footage, land area, bathrooms, and other structural characteristics; and (2) a non-parametric smoother (local polynomial regression, LPR) which calculates location value as a function of latitude and longitude. Several methods are used to consistently estimate both parts of the model. The LRM was fit to geocoded hedonic sales data for six towns in the suburbs of Boston, MA. The estimates yield substantial, significant and plausible spatial patterns in location values. Using the LRM as an exploratory tool, local peaks and valleys in location value identified by the model are close to points identified by the tax assessor, and they are shown to add to the explanatory power of an OLS model. Out-of-sample MSE shows that the LRM with a first-degree polynomial (local linear smoothing) is somewhat better than polynomials of degree zero or degree two. Future applications might use degree zero (the well-known NW estimator) because this is available in popular commercial software. The optimized LRM reduces MSE from the OLS model by between 5 percent and 11 percent while adding information on statistically significant variations in location value.     

CONSISTENT ESTIMATION OF RESIDUAL VARIANCE IN REGULATORY EVENT STUDIES

This study presents new evidence on alternative methods used to test for abnormal returns in regulatory event studies where cross-sectional correlation in residuals is significant. Results contradict earlier studies that find no advantages to using joint generalized least squares (JGLS) methods over ordinary least squares (OLS). We find that in an actual regulatory event study cross-correlation is significant, and that failing to correct for this correlation results in substantially higher calculated F-statistics. In Monte Carlo simulations we find that OLS test statistics are not well specified when residuals exhibit cross-sectional correlation at levels that are reasonable to expect in daily return data, while JGLS test statistics are well specified. The study includes tests of the effective power of the OLS and JGLS statistics.     

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.     

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.     

Member Plan Choice and Migration in Response to Changes in Member Premiums after Massachusetts Health Insurance Reform

In 2006 Massachusetts implemented a substantial reform of its health insurance market that included a new program for uninsured individuals with income between 100% of Federal Poverty (the upper limit for state Medicaid benefits) and 300% of Federal Poverty. Enrollment was compulsory for all citizens because of a mandate. Consumers who enrolled in this program, which offered generous benefits with low copays, received graduated subsidies depending on their income. Five insurers were contracted to underwrite the program, and consumers were able to choose their insurer. Insurers bid annually, and the member contribution was set according to an affordability schedule for the lowest-bidding insurer. Consumers could choose from the range of insurers, but if they chose a plan other than the lowest cost, their contributions reflected the difference. Premiums were changed annually at July 1, and members were eligible to move to a different plan at this date; a number of members migrated each year. This study aims to quantify the effect of this premium-induced switching behavior. Prior studies of member switching behavior have looked at employer plans and estimated the elasticity of response to changes in member contributions. The Massachusetts environment is unique in that there is a mandate (so being uninsured is not an option) and members may choose insurer but not benefit plan. Thus a study of migration in Massachusetts is uniquely able to quantify the effect of price (contribution rates) on member switching behavior. We find elasticity averaging ?0.21 for 2013 (the last year of the study) to be somewhat lower (in absolute value) than previous studies of employer populations. Elasticity has also been significantly increasing with time and appeared to have at least doubled over the studied period (i.e., 2008–2013). Prior studies have estimated higher elasticities in the range ?0.3 to ?0.6. We found that the data contained many outliers in terms of both changes in contributions and percentage of members switching plans. The effect of outliers was moderated by the choice of robust regression models, leading us to question whether other studies may have been affected by outliers, leading to overestimates of the elasticities.     

The impact of IFRS adoption on foreign direct investment

By analyzing a panel data set of over 1300 observations covering 124 countries, for the period from 1996 through 2009, this paper tests the basic argument that the adoption of International Foreign Reporting Standards (IFRSs) by a country results in increased foreign direct investment (FDI) inflows. Analysis of the data using an ordinary least squares (OLSs) approach provides evidence that adoption of IFRS leads to increased FDI inflows. The analysis indicates, however, that the overall increase in FDI inflows from IFRS adoption is due to the increase in FDI inflows by countries with developing, as opposed to developed, economies. A difference-in-difference test confirms these findings. A key potential driver for IFRS adoption by countries with developing economies is the desire to receive financial aid from the World Bank. This factor is explicitly taken into account using a two-stage instrumental variable (IV) model. The results using the IV model provide strong confirmation of the OLS results.     

ON THE CAPITAL STRUCTURE OF NON‐PROFIT ORGANISATIONS: A REPLICATION AND EXTENSION WITH BELGIAN DATA

The empirical tests of non‐profit organisations' capital structure theories by Jegers and Verschueren (2006) on a sample of Californian non‐profit organisations (data on 1999) are replicated and extended for a more recent Belgian sample (844 observations pertaining to 2007). Three complementary theories to explain the presence and levels of overall debt and financial debt are examined: equity constraints, agency, and borrowing constraints. The decision to borrow and the amount to be borrowed are analysed separately. The estimations obtained reveal that both are driven by different mechanisms. After having removed outliers, the results show effective equity constraints when explaining debt levels, as observed in the Californian sample with respect to the overall amount of debt. The results also indicate an agency explanation of debt: both the decision to borrow from financial institutions, and the overall amount of (financial) debt are positively affected by the presence of a potential agency gap between board and management. In the Californian sample, the results on this were mixed. Borrowing constraints were almost never discovered, similar to the conclusions reached by Jegers and Verschueren. However, slightly reducing the sample by removing outliers makes borrowing constraints apparent. As to the control variables, size positively affects the probability of borrowing, but, for the organisations taking on debt, negatively affects the level of borrowing. As could be expected, the amount of tangible fixed assets in place is positively related to the amount of financial debt.     

Monte Carlo valuation of natural gas investments

In this evaluation of energy assets related to natural gas, our particular focus is on a base load natural gas combined cycle power plant and a liquefied natural gas facility in a realistic setting. We also value several American-type investment options following the least squares Monte Carlo approach. We calibrate mean-reverting stochastic processes for gas and electricity prices by using data from NYMEX NG futures contracts and the Spanish wholesale electricity market, respectively. Additional sources of uncertainty concern the initial investment outlay, or the option's time to maturity, or the cost of CO₂ emission permits.     

An analysis of contagion among Asian countries using the canonical model of contagion

Understanding the dependence among economies is relevant to policy makers, central banks and investors in the decision-making process. One important issue for study is the existence of contagion among economies. This work considers the Canonical Model of Contagion by Pesaran and Pick (Journal of Economic Dynamics and Control, 2007), which differentiates contagion from interdependence. The ordinary least squares estimator of this model is biased by the endogenous variables in the model. In this study, instrumental variables are used to decrease the bias of the ordinary least squares estimator. The model is extended to the case of heteroskedastic errors, features that are generally found in financial data. We postulate the conditional volatility of the performance indices as instrumental variables and analyze the validity of these instruments using Monte Carlo simulations. Monte Carlo simulations estimate the distributions of the estimators under the null hypothesis. Finally, the canonical model of contagion is used to analyze the contagion among seven Asian countries.     

Outlier analysis and mortality forecasting: The United Kingdom and Scandinavian countries

The Lee-Carter model and its variants have been extensively employed by actuaries, demographers, and many others to forecast age-specific mortality. In this study, we use mortality data from England and Wales, and four Scandinavian countries to perform time-series outlier analysis of the key component of the Lee-Carter model – the mortality index. We begin by employing a systematic outlier detection process to ascertain the timing, magnitude, and persistence of any outliers present in historical mortality trends. We then try to match the identified outliers with imperative events that could possibly justify the vacillations in human mortality levels. At the same time, we adjust the effect of the outliers for model re-estimation. A new iterative model re-estimation method is proposed to reduce the chance of erroneous model specification. The empirical results indicate that the outlier-adjusted model could achieve more efficient forecasts of variables such as death rates and life expectancies. Finally, we point out that the Lee-Carter forecasts are especially vulnerable to outliers near the forecast origin, and discuss the potential limitations of the application of the Lee-Carter model to mortality forecasting.