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Principal component (PC) techniques are commonly used to improve the small sample properties of the linear instrumental variables (IV) estimator. Carrasco (2012) argue that PC type methods provide a natural ranking of instruments with which to reduce the size of the instrument set. This chapter shows how reducing the size of the instrument based on PC methods can lead to poor small sample properties of IV estimators. A new approach to ordering instruments termed ‘normalized principal components’ (NPCs) is introduced to overcome this problem. A simulation study shows the favourable small samples properties of IV estimators using NPC, methods to reduce the size of the instrument relative to PC. Using NPC we provide evidence that the IV setup in Angrist and Krueger (1992) may not suffer the weak instrument problem.

This paper aims to test the finite sample properties of the automatic variance ratio (AVR) test and suggest suitable measure to improve its small sample properties under conditional heteroskedasticity and apply it to test the martingale hypothesis in the stock prices of the Portugal, Ireland, Italy, Greece and Spain (PIIGS economies) markets. This paper also seeks to investigate that “If the time series is not martingale, then what else?”

Monte Carlo experiments have been undertaken to test the small sample properties of automatic variance ratio (AVR) test. The study uses AVR test on daily and weekly data of the indices to investigate their martingale behaviour. It uses detrended fluctuation analysis (DFA) and BDS test statistics to answer, “If not martingale, then what else?”. The study also applies moving subsample approach to examine the dynamic behavior of stock prices and to obtain inferential findings robust to possible structural changes and presence of influential outliers.

The author finds that weighted bootstrap procedure significantly improves the small sample properties of AVR tests under conditional heteroskedasticity. The results provide evidence in support of the weak‐form efficiency of Italy and Spain. But Portugal, Ireland and Greece exhibit signs of long memory in the stock prices. All indices also exhibit chaotic characteristics.

This paper has both methodological and empirical originality. On the methodological aspect, the author proposes weighted bootstrap procedure on AVR test to improve its small sample properties. On the empirical side, the study finds that all stocks exhibit dynamic behavioral characteristics which change over time.

Foreclosure risk is a key concern to lenders of real estate mortgages. Using auction data, this study provides the first analysis of mortgage foreclosure in Singapore by examining how macro‐economic variables affect the probability of foreclosure. The foreclosure rate for properties is found to be increasing in the first five years of purchase and decreases as the holding period lengthens. The likelihood of foreclosure increases with unemployment rate, mortgage rate and expenditure and decreases with equity, dividend yield and lending volume at fourth and twentieth quarters lag. Further analysis shows considerable differences between residential and non‐residential properties. However, when the analysis on non‐residential properties is further separated into office, retail and industrial sub‐sectors, the results are relatively similar among the three sub‐sectors. This implies that banks and financial institutions should apply different underwriting standards for residential properties, mainly for owner‐occupation and non‐residential properties for the purpose of businesses and rental income.

The purpose of this paper is to investigate the relationship between occupiers’ satisfaction with the property management service they receive and the financial performance of commercial real estate.

The study uses occupier satisfaction data for 240 UK commercial properties collected over a 12-year period and the annual total returns achieved by those properties. Various statistical techniques are employed to assess whether increasing occupier satisfaction leads to greater returns for investors. These include comparing excess returns and risk-adjusted returns with occupier satisfaction at each property to assess whether superior property management generates outperformance (“positive alpha”). The study also investigates whether the relationship between occupier satisfaction and returns is the same across all sectors and whether it is affected by market conditions.

A positive correspondence is found between benchmark outperformance and occupier satisfaction. The relationship is similar for all sectors of commercial property and is particularly strong during the Global Financial Crisis, indicating that paying attention to satisfying the needs of occupiers has particular benefits during periods when the supply of commercial real estate exceeds demand.

The sample of properties was restricted to those for which occupier satisfaction data had been collected by RealService Ltd and whose owners permitted access to the financial performance results. This meant that the properties belong to only three landlords, all UK REITs that care sufficiently about occupier satisfaction to commission studies. Thus the findings might not apply to all commercial properties. The mechanism by which the positive relationship between satisfaction and financial performance occurs is not tested, but the conventional mechanisms of reputation and customer loyalty (the “service-profit chain”) are discussed.

The findings suggest that it is worthwhile for landlords, or property managers acting on their behalf, to understand the needs of their occupiers in order to deliver the level of service that those occupiers desire. Leases in the UK are generally “triple net” and the total returns used for this analysis are net of property management costs, so the positive relationship between satisfaction and performance is not the result of economising on service delivery. A further implication is that valuers should take more account of occupier satisfaction when assessing the capital value of a property, from which total returns are assessed.

Demonstrating the links between customer service, customer satisfaction and business profitability is rarely attempted because of the many confounding factors that affect profitability. UK listed real estate companies are typically reluctant to reveal the financial performance of individual properties, and information about occupiers’ satisfaction is not generally available. The authors were fortunate to be granted access to a time series of such data, and to be able to demonstrate that attention to delivering a property management service that satisfies occupiers is likely to bring financial rewards to the owners of the property.

This paper aims to give a brief review on behavioral economics and behavioral finance and discusses some of the previous research on agents' utility functions, applicable risk measures, diversification strategies and portfolio optimization.

The authors also cover related disciplines such as trading rules, contagion and various econometric aspects.

While scholars could first develop theoretical models in behavioral economics and behavioral finance, they subsequently may develop corresponding statistical and econometric models, this finally includes simulation studies to examine whether the estimators or statistics have good power and size. This all helps us to better understand financial and economic decision-making from a descriptive standpoint.

The research paper is original.

The purpose of this paper is two-fold. First, on a theoretical level we introduce a series-type instrumental variable (IV) estimator of the parameters of a spatial first order autoregressive model with first order autoregressive disturbances. We demonstrate that our estimator is asymptotically efficient within the class of IV estimators, and has a lower computational count than an efficient IV estimator that was introduced by Lee (2003). Second, via Monte Carlo techniques we give small sample results relating to our suggested estimator, the maximum likelihood (ML) estimator, and other IV estimators suggested in the literature. Among other things we find that the ML estimator, both of the asymptotically efficient IV estimators, as well as an IV estimator introduced in Kelejian and Prucha (1998), have quite similar small sample properties. Our results also suggest the use of iterated versions of the IV estimators.

The purpose of this paper is to test the efficient market hypothesis for major Indian sectoral indices by means of long memory approach in both time domain and frequency domain. This paper also tests the accuracy of the detrended fluctuation analysis (DFA) approach and the local Whittle (LW) approach by means of Monte Carlo simulation experiments.

The author applies the DFA approach for the computation of the scaling exponent in the time domain. The robustness of the results is tested by the computation of the scaling exponent in the frequency domain by means of the LW estimator. The author applies moving sub-sample approach on DFA to study the evolution of market efficiency in Indian sectoral indices.

The Monte Carlo simulation experiments indicate that the DFA approach and the LW approach provides good estimates of the scaling exponent as the sample size increases. The author also finds that the efficiency characteristics of Indian sectoral indices and their stages of development are dynamic in nature.

This paper has both methodological and empirical originality. On the methodological side, the author tests the small sample properties of the DFA and the LW approaches by using simulated series of fractional Gaussian noise and find that both the approach possesses superior properties in terms of capturing the scaling behavior of asset prices. On the empirical side, the author studies the evolution of long-range dependence characteristics in Indian sectoral indices.

This paper considers the finite-sample distribution of the 2SLS estimator and derives bounds on its exact bias in the presence of weak and/or many instruments. We then contrast the behavior of the exact bias expressions and the asymptotic expansions currently popular in the literature, including a consideration of the no-moment problem exhibited by many Nagar-type estimators. After deriving a finite-sample unbiased k-class estimator, we introduce a double-k-class estimator based on Nagar (1962) that dominates k-class estimators (including 2SLS), especially in the cases of weak and/or many instruments. We demonstrate these properties in Monte Carlo simulations showing that our preferred estimators outperform Fuller (1977) estimators in terms of mean bias and MSE.