The Choice of Methodology for Computing Housing Price Indexes: Comparisons of Temporal Aggregation and Sample Definition

Housing transactions are executed and recorded daily, but are routinely pooled into longer time periods for the measurement and analysis of housing price trends. We utilize an unusually rich data set, covering essentially all arm's length housing sales in Sweden for a dozen years, in an attempt to understand the effect of temporal aggregation upon estimates of housing prices and their volatilities. This rich data set also provides a unique opportunity to compare the results using the conventional weighted repeat sales model (WRS) to those based on a research strategy which incorporates all available information on house sales. The results indicate the clear importance of temporal disaggregation in the estimation of housing prices and volatilities—regardless of the model employed.The appropriately disaggregated model is then used as a benchmark to compare estimates of the course of housing prices produced by the two models during the twelve year period 1981–1993. These results indicate that much of the difference between estimates of price movements can be attributed to the data limitations which are inherent in the repeat sales approach. The results, thus, suggest caution in the interpretation of government-produced price indices or those produced by private firms based on the repeated sales model.     

Aggregation Bias in Repeat-Sales Indices

The repeat-sales methodology has become a standard approach for estimating real estate price indices. This article examines the underlying assumptions inherent in the repeat sales model and provides an empirical test for both included and omitted variables as sources of aggregation bias. The results indicate that virtually all price indices may be biased, the degree of bias being dependent upon the number of variables examined and the instability of their parameters over time.     

Temporal aggregation and risk–return relation

The function form of a linear intertemporal relation between risk and return is suggested by Merton's [1973. Econometrica 41, 867–887] analytical work for instantaneous returns, whereas empirical studies have examined the nature of this relation using temporally aggregated data, i.e., daily, monthly, quarterly, or even yearly returns. Our paper carefully examines the temporal aggregation effect on the validity of the linear specification of the risk–return relation at discrete horizons, and on its implications on the reliability of the resulting inference about the risk–return relation based on different observation intervals. Surprisingly, we show that, based on the standard Heston's [1993. Review of Financial Studies 6, 327–343] dynamics, the linear relation between risk and return will not be distorted by the temporal aggregation at all. Neither will the sign of this relation be flipped by the temporal aggregation, even at the yearly horizon. This finding excludes the temporal aggregation issue as a potential source for the conflicting empirical evidence about the risk–return relation in the earlier studies.     

Short Sales Constraints and Momentum in Stock Returns

Abstract:   We show that stock characteristics identified by D'Avolio (2002) provide a reliable index of the mostly unobservable short sales constraints. Specifically, we find that this index is positively related to the level of short interest and to short selling costs implied by the disparity in prices in the options and stock markets, and is negatively related to future returns. Using this index, we show that the magnitude of momentum returns for the period 1984 to 2001 is positively related to short sales constraints, and loser stocks rather than winner stocks drive this result. We conclude that short sales constraints are important in preventing arbitrage of momentum in stock returns.     

美国银行贷款销售:发展、现状与启示

银行贷款二级销售市场是美国发展最快的金融市场之一,本论文从多个角度较为全面地考察了美国银行的贷款销售行为,包括美国银行贷款销售的发展历史、发展现状、银行贷款的销售动机以及贷款销售本身对银行的影响等问题,分析了中国银行贷款销售的现状及原因,认为中国银行应加强投行业务创新,从积极的信用风险管理和经营模式创新的角度来大力发展贷款销售业务.     

美国银行贷款销售：发展、现状与启示

银行贷款二级销售市场是美国发展最快的金融市场之一，本论文从多个角度较为全面地考察了美国银行的贷款销售行为，包括美国银行贷款销售的发展历史、发展现状、银行贷款的销售动机以及贷款销售本身对银行的影响等问题，分析了中国银行贷款销售的现状及原因，认为中国银行应加强投行业务创新，从积极的信用风险管理和经营模式创新的角度来大力发展贷款销售业务。     

The Effects of Attributes on the Repeat Sales Pattern of Residential Property in Hong Kong

There has been copious research work on the development of house price models and the construction of house price indices. However, results in some studies revealed that the accuracy of such indices could be subject to selection bias when using only information from a sample of sold properties to estimate value movements for the entire housing stock. In particular, estimated house price appreciation is usually systematically higher among properties that change hands more frequently. It therefore suggests that the determination of important factors affecting the transaction frequency or intensity of a housing unit should be a more fundamental research question. This paper examines the possible factors that determine the popularity of residential unit by means of a repeated sales pattern. The Poisson regression model and event history analysis techniques are employed to assess the effect of attributes on transaction frequency and intensity. The event history analyses technique can take account of transaction-specific as well as time-dependent covariates, and therefore is recommended for analyzing repeated sales data in a real estate market. All transaction records during the period 1993–2000 from the Land Registry of one of the most popular residential estates in Hong Kong were used to illustrate the method. Unlike a response to favorable transaction price, good quality units do not necessarily inherently display a high transaction frequency. Rather, units of average quality are more likely to be transactionally active.     

Controlling for Quality in House Price Indices

Given the importance of house prices it is not surprising that house price indices are used for many purposes. One of the factors that differentiates these indices is the house price determinants (such as structural characteristics and neighborhood quality) that are accounted for—that is, held constant. Indices are usually generated from house price regressions. It is shown that, regardless of the desired level of accounting, it is necessary to control for all significant determinants of house prices in these regressions to obtain unbiased estimates of the growth in house prices. An empirical example shows that not controlling for neighborhood quality can lead to substantial biases in estimates of house price appreciation rates even if the index does not account for this factor.     

Short Holds,the Distributions of First and Second Sales,and Bias in the Repeat-Sales Price Index

The Journal of Real Estate Finance and Economics -     

Long-memory and heterogeneous components in high frequency Pacific-Basin exchange rate volatility

Recent research examining high-frequency financial data has suggested that volatility dynamics may be confounded by the existence of an intra-day periodic pattern and multiple sources of volatility. This paper examines whether these dynamics are present in the US Dollar exchange rates of five Pacific Basin economies. Using 30-min sampled returns, evidence of a ‘U’-shape intra-day pattern in volatility for regional markets is reported and controlled for using a Flexible Fourier transform. Supportive evidence for the existence of multiple volatility components is offered by semi-parametric fractional difference estimates of the long-memory properties of absolute exchange rate returns at various intra-day data sampling frequencies. Further parametric evidence of an explicit component structure in such high frequency exchange rate volatility is offered by the estimates of a component-GARCH model which comprises both a long-run volatility component exhibiting slow shock decay and a short-run volatility component exhibiting far more rapid decay, and provides a generally superior fit to the data. Further application of these C-GARCH models in the analysis of high frequency volatility spillovers between the currencies considered also reveals that such spillovers are predominantly transitory rather than highly persistent in nature, but that where volatility spillovers do impact on the long-run component of exchange rate volatility the Australian Dollar plays a pivotal role in the localised causality transmission mechanism.      

Using Leading Indicators to Forecast U.S. Home Sales in a Bayesian Vector Autoregressive Framework

This article uses Bayesian vector autoregressive models to examine the usefulness of leading indicators in predicting U.S. home sales. The benchmark Bayesian model includes home sales, price of homes, mortgage rate, real personal disposable income, and unemployment rate. We evaluate the forecasting performance of six alternative leading indicators by adding each, in turn, to the benchmark model. Out-of-sample forecast performance over three periods shows that the model that includes building permits authorized consistently produces the most accurate forecasts. Thus, the intention to build in the future provides good information with which to predict U.S. home sales. Another finding suggests that leading indicators with longer leads outperform the short-leading indicators.     

Temporal aggregation and unit roots in nominal foreign exchange rates

Extant empirical evidence on the logarithm of the daily and weekly spot exchange rates indicates the presence of unit roots. At a lower frequency, however, there is evidence that monthly spot rates do not have a unit root, although the autocorrelation coefficient may be close to one in absolute value. Unlike earlier studies, the present article applies various tests to data with different frequencies and reports evidence suggesting that exchange rates may not have unit roots. Absence of nonstationarity in the observations implies that econometric models may not have to apply any differencing.The authors gratefully acknowledge Hashem Dezhbakhsh for helpful suggestions. Comments are welcome.     

Aggregation Bias in Estimates of Conditional Conservatism: Theory and Evidence

This paper documents a study about the influence of the aggregation effect on the estimates of models based on the original Basu model – specifically the Ball, Kothari and Nikolaev model (Ball et al., 2013b). We provide an analytical study of the effect, showing that it can produce two biases: an omitted‐variable bias and a truncated‐sample bias. Using separate proxies for good and bad news for each company and year, we estimate the empirical sign and magnitude of those biases. Our results show that the estimates of conditional conservatism based on regressions of (unexpected) earnings on (unexpected) returns, as in the paper by Ball et al., are contaminated by substantial aggregation bias. More specifically, the aggregation effect causes these models to underestimate good‐news timeliness and overestimate bad‐news timeliness, thereby overestimating differential timeliness. Moreover, when we use proxies that provide better control for the aggregation effect, the differential timeliness coefficient tends to 0, showing that the influence of conditional conservatism on the returns–earnings relationship is, at best, marginal.     

Exploiting the monthly data flow in structural forecasting

A quarterly stochastic general equilibrium (DSGE) model is combined with a now-casting model designed to read timely monthly information as it becomes available. This implies (1) mapping the structural quarterly DSGE with a monthly version that maintains the same economic restrictions; (2) augmenting the model with a richer data set and (3) updating the estimates of the DSGE׳s structural shocks in real time following the publication calendar of the data. Our empirical results show that our methodology enhances the predictive accuracy in now-casting. An analysis of the Great Recession also shows that our framework would have helped tracing the DSGE׳s structural shocks in real time, obtaining, for example, a more timely account of the 2008 contraction.     

Accounting for Variation in the Explanatory Power of the Psychometric Paradigm: The Effects of Aggregation and Focus

Most psychometric studies of risk perception have used data that have been averaged over participants prior to analysis. Such aggregation obscures variation among participants and inflates the magnitude of relationships between psychometric dimensions and dependent variables such as overall riskiness. However, most studies that have not averaged data over participants have also shifted the focus of analysis from differences among hazards to differences among participants. Hence, it is unclear whether observed reductions in the explanatory power of psychometric dimensions result from the change in the level of analysis or from the change in the focus of analysis. Following Willis et al.'s (2005 Willis, H. H., DeKay, M. L., Fischhoff, B. and Morgan, M. G. 2005. Aggregate, disaggregate, and hybrid analyses of ecological risk perceptions. Risk Analysis, 25(2): 405–428. [Crossref], [PubMed], [Web of Science ®] , [Google Scholar]) analysis of ecological risk perceptions, we unconfound these two variables in a study of risk perceptions in Santiago, Chile, although we use more traditional hazards, attributes, and statistical procedures. Results confirm that psychometric dimensions explain less variation in judgments of riskiness and acceptability at the disaggregate level than at the aggregate level. However, they also explain less variation when the focus of analysis is differences among participants rather than differences among hazards. These two effects appear to be similar in magnitude. A simple hybrid analysis economically represents variation among participants' judgments of hazards' riskiness by relating those judgments to a common set of psychometric dimensions from a traditional aggregate‐level analysis.     

The shape of small sample biases in pricing kernel estimations

Numerous empirical studies find pricing kernels that are not-monotonically decreasing; the findings are at odds with the pricing kernel being marginal utility of a risk-averse, so-called representative agent. We study in detail the common procedure which estimates the pricing kernel as the ratio of two separate density estimations. In the first step, we analyse theoretically the functional dependence for the ratio of a density to its estimated density; this cautions the reader regarding potential computational issues coupled with statistical techniques. In the second step, we study this quantitatively; we show that small sample biases shape the estimated pricing kernel, and that estimated pricing kernels typically violate the commonly believed monotonicity at the centre even when the true pricing kernel fulfils these. This contributes to an alternative, statistical explanation for the puzzling shape in pricing kernel estimations.