Are securitised real estate markets efficient?: New international evidence based on an improved automatic Portmanteau test

We re-examine the efficiency of real estate markets based on the Escanciano-Lobato (2009) autocorrelation test which we improved by means of wild bootstrapping. Through Monte Carlo simulation, we find that the wild bootstrap-based autocorrelation test has very good performance even in small samples. We apply the improved test to examine the efficiency of 14 international securitized real estate markets—Australia, Canada, France, Germany, Hong Kong, Italy, Japan, Netherlands, Norway, Singapore, Sweden, Switzerland, United Kingdom and the United States. Our results show that only six of these markets—Australia, Hong Kong, Italy, Japan, Sweden and the United States are efficient while the rest are inefficient. We also find that the degree of efficiency or inefficiency of each of these markets varies considerably across time. These findings indicate that real estate markets are relatively less efficient as compared to stock and bond markets in general and may also offer an explanation as to why existing studies on real estate market efficiency have mixed results.     

Default and liquidity regimes in the bond market during the 2002–2012 period

Using a real‐time random regime shift technique, we identify and discuss two different regimes in the dynamics of credit spreads during 2002–2012: a liquidity regime and a default regime. Both regimes contribute to the patterns observed in credit spreads. The liquidity regime seems to explain the predictive power of credit risk on the 2007–2009 NBER recession, whereas the default regime drives the persistence of credit spreads over the same recession. Our results complement the recent dynamic structural models as well as monetary and credit supply effects models by empirically supporting two important patterns in credit spreads: the persistence and the predictive ability toward economic downturns.     

A semiparametric estimation of liquidity effects on option pricing

Abstract. This paper proposes a semiparametric option pricing model with liquidity, as proxied by the relative bid-ask spread. A nonparametric volatility function with liquidity costs as an explanatory variable is estimated using the Symmetrized Nearest Neighbors (SNN) estimator rather than the traditional kernel estimator. The SNN estimator is particularly suitable for the characteristics of option data in financial markets. Moreover, we propose a natural extension of the univariate bandwidth parameter optimal estimation to the multivariate case. A statistical design to test competing option pricing models which takes into account the lack of independence between them is also presented. The in-sample performance of the model turns out to be statistically favorable relative to the competing model without liquidity. Also, an additional experiment is performed within sample, but with just a subsample of options not employed in the nonparametric estimation of the implied volatility function being priced. The results are also favorable to our semiparametic theoretical option pricing model with liquidity. However, the out-of-sample performance is quite disappointing regardless of what option pricing model is employed in the estimation. Eva Ferreira and Gonzalo Rubio acknowledge the financial support provided by Dirección Interministerial Científica y Técnica (DGICYT) grants PB98-0149 and PB97-0621 respectively. All three authors aknowledge the financial support provided by Universidad del País Vasco (UPV/EHU) grant UPV 038.321-HA129/99, and the BSI Gamma Foundation. We appreciate the helpful comments of two anonymous referees, ángel León, José M. Campa, Fernando Tusell and Javier Fernández Navas, seminar participants at the Bank of Spain and the European Financial Management Association (Athens), and the computational assistance of Gregorio Serna. We thank Juan Ayuso and MEFF for providing the data used in this article. The contents of this paper are the sole responsability of the authors.     

Small investment and large returns: Terrorism, media and the economy

The study investigates the role of the media in the impact of terrorism on the economy. A unique data set of the newspaper articles that reported terrorist attacks during 2002 is used to evaluate their impact on the Tel Aviv Stock Exchange. An econometric analysis is performed in order to understand how a newspaper decides to cover a terrorist attack, i.e. the number of articles, positioning of articles, whether to include photos and the size of headlines. It was found that media coverage is an important channel through which terrorism produces economic damage. The findings also showed that the economic damage caused by terrorist attacks increases monotonically with the amount of media coverage. It was also found that the economic impact of the media coverage diminishes over time.     

Risk assessment of oil price from static and dynamic modelling approaches

The price gap between West Texas Intermediate (WTI) and Brent crude oil markets has been completely changed in the past several years. The price of WTI was always a little larger than that of Brent for a long time. However, the price of WTI has been surpassed by that of Brent since 2011. The new market circumstances and volatility of oil price require a comprehensive re-estimation of risk. Therefore, this study aims to explore an integrated approach to assess the price risk in the two crude oil markets through the value at risk (VaR) model. The VaR is estimated by the extreme value theory (EVT) and GARCH model on the basis of generalized error distribution (GED). The results show that EVT is a powerful approach to capture the risk in the oil markets. On the contrary, the traditional variance–covariance (VC) and Monte Carlo (MC) approaches tend to overestimate risk when the confidence level is 95%, but underestimate risk at the confidence level of 99%. The VaR of WTI returns is larger than that of Brent returns at identical confidence levels. Moreover, the GED-GARCH model can estimate the downside dynamic VaR accurately for WTI and Brent oil returns.     

State-price densities under heterogeneous beliefs, the smile effect, and implied risk aversion

It has been widely noted in the empirical literature that state-price densities implicit in financial asset prices are not log-normal. This paper shows that this phenomenon can be caused by heterogeneity in investors’ beliefs. It derives the state-price density under heterogeneous beliefs in closed form and demonstrates that heterogeneous beliefs can give rise to multimodal state-price densities. Consequences for the “smile effect” in implied option volatility and for measures of risk aversion inferred from empirical state-price densities are discussed.     

What are the effects of fiscal policy on asset markets?

We investigate the link between fiscal policy shocks and asset markets. Our results show that spending shocks have: a positive and persistent effect on GDP in the U.S. and in the U.K., while for Germany and Italy, such impact is temporary; a positive and persistent effect on housing prices; a negative effect on stock prices; and mixed effects on the price level. A VAR counter-factual exercise suggests that fiscal shocks play a minor role in the asset markets of the U.S. and Germany, and substantially increase the variability of housing and stock prices in the U.K., while government revenue shocks have increased volatility in Italy.     

The Effect of Annual Earnings Announcements on the Chinese Stock Markets

This paper examines the annual earnings announcement effect of the stock markets in China. The investigation is based on events analysis and carried out by modeling the daily changes of stock returns using the M-EGARCH approach, by testing the news effects of annual earnings announcement on the conditional mean of abnormal return and the variance of the returns. It is found that a higher than expected earnings announcement leads to a rise in the conditional mean of stock returns on days before the news announcement and a fall afterwards. The conditional volatility of the changes are significantly reduced by bigger absolute values of reported earnings before the news announcement and increased afterwards, supporting the rejection of semi-strong-form efficiency.     

IS THE COST OF DEBT CAPITAL HIGHER FOR YOUNGER FIRMS?

Not much is known about the returns to aging (maturing) in the market for small business finance. Using a large panel of closely held micro firms, we document that the cost of debt capital is higher for young firms. The main finding of this paper is that this negative qualitative relation is also obtained when cross‐sectional variation in unobservable creditworthiness of small businesses and within‐firm (i.e., inter‐temporal) variation in their observable creditworthiness are held constant. We control for the former by firm‐specific fixed effects and for the latter by a commercial credit score. We also provide an estimate of the quantitative magnitude of the aging effect, on which both economic theory and earlier empirical research are silent. We find that when a small business ages one year, its cost of debt capital decreases by 1–2 basis points. The effect is neither negligible nor alarmingly large.     

Using the Haar wavelet transform in the semiparametric specification of time series

Using theoretical arguments for nonparametric wavelet estimation, we devise regression-based semiparametric wavelet estimators to dissect linear from nonlinear effects in a time series. The wavelet estimators localize in both time and frequency so that distortion due to outliers is lessened. Our regression-based approach also lends itself to ease of replication, clarity, flexibility, timeliness and statistical validity. We demonstrate the efficacy of the approach via rolling regressions on time series of quarterly U.S. GDP growth rates, monthly Hong Kong/ U.S. exchange rates, weekly 1-month commercial interest rates and daily returns on the S&P 500.     

Stock exchange mergers and weak form of market efficiency: The case of Euronext Lisbon

This exploratory paper is among the first to examine the impact of stock exchange mergers on informational market efficiency. We focus on the merger of Bolsa de Valores de Lisboa e Porto (Portuguese Stock Exchange) with Euronext in 2002 (that created Euronext Lisbon). To investigate this question we perform numerous statistical tests: serial correlation test (ACF test), runs test, unit root test (Kwiatkowski, Philips, Schmidt, & Shin, 1992), multiple variance ratio test (Chow & Denning, 1993) and ranks and signs test (Wright, 2000).The results indicate that the Portuguese Equity Market is inefficient in weak form during pre-merger period implying that investors possessed an opportunity to earn abnormal returns though small in magnitude. The results, sensitive to the methodology used, indicate a mixed evidence of improvement in market efficiency during the post-merger period. Although the findings are mixed, yet most tests show a tendency of improved efficiency.     

Can we reject linearity in an HAR-RV model for the S&P 500? Insights from a nonparametric HAR-RV

This article tests the linearity assumption underlying the popular heterogeneous autoregressive model for realized volatility (HAR-RV). We implement a consistent model specification test that is robust to both distributional and model misspecification. We find that, using a nonparametric HAR-RV (NPHAR-RV), we are unable to reject the null of linearity.