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Understanding cyclical activity is an important component of efficient portfolio management. Property appraisal models that do not explicitly take into account cyclical fluctuations may produce unrealistic valuation estimates resulting in property assets being incorrectly added to or removed from the general investment portfolio. In this paper we use conventional spectral analysis techniques to examine property and financial assets for evidence of cycles and co‐cycles. One finding is that the very pronounced cyclical patterns that appear in direct real estate markets and the economy as a whole are very much less obvious once they have filtered through to securitised property markets and financial assets markets.

The purpose of this paper is to survey literature on macroeconomic nonlinear dynamics.

The paper identifies five influential types of models where the possible generation of endogenous cycles and chaotic motion arises. First, the frameworks that make use of the one‐hump logistic type equation; second, the models inspired on the growth literature of the 1940s; third, intertemporal utility maximization problems with increasing returns; fourth, models that can be represented as piecewise dynamic maps; and, fifth, bounded rationality – heterogeneous expectations setups.

The attention will be mainly focused on the theme of business cycles; an interpretation of the deterministic real business cycle model with increasing returns is proposed and a graphical analysis of the underlying system shows that strange attractors are observable for specific sets of parameter values.

The study of endogenous cycles in macroeconomic literature has important implications for policy: if fluctuations are due to deterministic reasons this may imply that by manipulating policy parameters governments may be able to change the qualitative nature of the economy's dynamics.

The paper gives a comprehensive view of nonlinear dynamics in macroeconomics. It shows that various relevant subjects might be addressed in this kind of models, e.g. economic growth, asset pricing, business cycles, consumption decisions, among others.

The scientific world has for a long time worked with linear tools to explain complex non‐linear phenomena. Also non‐linear relationships were transformed to linear in order to be handled. This was so despite the fact that nature has been giving us examples showing that the real world was complex, dynamic and non‐linear. This was so until developments in computer science provided the calculating means to the theory of complexity. Today, the theory of complexity is applied to many sciences and to disaster management as well. The application of complexity theory is attempted from a disaster management point of view. As the first attempt to incorporate management science into marine disaster prevention was the so‐called International Safety Management Code of the International Maritime Organization in 1998, we will attempt to connect the theory of complexity to the ISM code. The ISM code, being a safety standard, has been based on good management and organization rulesl. However, ISM code was accused of increasing the bureaucracy of the system with a result that no substance work could be done at the end. Moreover, a need has now emerged, i.e. to establish a “particular ISM code man” among the crewmen on board all ships. On the other hand, modern complexity management suggests more flat hierarchies where information travels faster, and this is something needed in a marine disaster.

This study uses parametric hazard models to investigate duration dependence in US stock market cycles over the January 1929 through December 1992 period. Market cycles are determined using the Beveridge‐Nelson (1981) approach to the decomposition of economic time series. The results show that both real and nominal cycles exhibit positive duration dependence. The implication of this finding is that actual prices revert to their permanent or trend level in a non‐random manner as the cyclical component dissipates over time. This process is consistent with mean reversion in price and suggests that predictable periodicity in market cycles may exist. Only limited evidence is obtained that discrete shifts or trends in mean cycle duration exist. The length of market cycles appears not to have changed over the 1929–92 period.

Two measures of informational efficiency are applied to the market for paintings. The first is a measure of market efficiency as captured by serial dependency in returns. The serial correlation in an index of art returns suggests the possibility of persistent trends in the art market, however there is no empirical evidence that these trends can be easily exploited. The second is a measure of “price risk,” or instantaneous uncertainty about the immediate resale value of a work of art. The magnitude of the price risk suggests that there is a major role for dealers in the art market. Using historical data, I find that the price risk has been declining since the beginning of the painting market, indicating increasing informational efficiency. Paintings are like stocks and a dealer is like a broker. Someone makes money, then there is someone else who's really good at investing in stocks, and he tells the investor what to buy. If someone tells you to go to a good gallery rather than one that's not so good, you'll get a painting that might turn out to be worth something, a painting you like that's also a good investment. Its like having a broker tell you what stocks to buy. Andy Warhol

We investigate the long‐term dependency behavior of Asian foreign exchange markets by using rescaled range analysis. Emerging markets in Korea, Taiwan, India, and Thailand, show evidences of long memory in the exchange rate return series, while the exchange rate return persistence is not found in more developed and mature markets in Japan, Australia, Hong Kong, and Singapore. Our results suggest that the return‐generating processes and presence of long memory depends on the degree of market development. In addition, the findings suggest that Asian financial crisis affects long‐term dependences of Korean won and Thai baht in which their economies and currency were hard hit by the crisis.

The paper seeks to examine cycles and common cycles in the real estate markets of the UK, Japan, Singapore, Hong Kong and Malaysia using a combination of time domain and frequency domain methods.

The paper identifies the patterns of cyclical movement (if any) in the five public real estate markets, and searches for common cycle characteristics and patterns in international real estate markets. In addition to the time domain analyses, these empirical investigations are further empowered by a frequency domain method that includes spectral and co‐spectral analyses.

International real estate markets are characterized by cyclical behavior that exhibits phenomenal fluctuations. The markets are also pro‐cyclical; they do tend to move together. Furthermore, some differences in the patterns of the common cycles and their lead‐lag linkages are evident.

International investors would probably benefit from diversifying real estate stocks across the UK and Asian real estate markets, especially in the short and medium terms. However, the long‐term cyclical patterns across the national real estate stock markets are not sharply different, indicating that smaller diversification benefits are to be expected in the long term.

Common cycle analysis advances investors' understanding of the long‐term relationship and medium‐ and short‐term linkages across international real estate markets, thereby allowing investors and portfolio managers an opportunity to discern any contrasting cyclical patterns at all frequencies so as to assist in their portfolio decisions.

The purpose of this paper is to determine whether foreign exchange markets in India have become more efficient over time. There were two major developments in India’s foreign exchange market since the 1980s: first, a shift in foreign exchange management regime from a basket peg to a free float; and second, a rapid phase of economic liberalization since the mid-1990s. The paper attempts to find out whether the market efficiency of foreign exchange markets is affected by these developments. The paper mainly uses the well-known Hurst exponent calculated through corrected empirical R over S analysis to determine whether the exchange rates possess long memory. The robustness of the method is tested by calculating the Hurst exponent through two other prevalent methods in the literature.

The authors apply the corrected empirical Hurst exponent which employs the Anis Lloyd correction with the modification suggested by Weron. The sensitivity of the results is then tested by replicating the calculations using the detrended fluctuation analysis and Robinson’s method.

All the methods show that: first, there is no significant change in the overall efficiency of the foreign exchange market vis a vis the US$ for the time period from 1980 to 2017. Second, neither regime shifts nor calculations over sub-time periods is able to identify significant change in the efficiency level of the market for the US$ exchange rate. Third, efficiency of different exchange rate markets are different over the time period 1999–2017. The US$ market has unequivocally more long run memory compared to the GBP, Yen and EURO markets. Fourth, the results are robust to the method used for calculations.

Does the efficiency of asset markets evolve over time? This paper attempts to answer this question. In the process, the paper studies the effect of regime shifts and progressive globalization on the ability of the market to internalize information.

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.

The purpose of this study is to estimate an empirical model for new office space development starts, based on the theoretical treatment of urban growth. The study introduces a new parameter, namely, office space-usage pattern, to the office space development equation and tests whether developers respond to non-price measures in deciding to commence new developments.

The study first introduces a co-integration approach based on an error correction model to test for long-run relations and short-run dynamics of new office space development. A multivariate regression model is then introduced to identify significant determinants that influence office development starts. The study uses annual data over a time span of 30 years.

Estimated results provide strong evidence that the newly introduced parameter exerts a positive impact on new office space development. It suggests that if the average floor space per employee changes by one percentage point, new office development starts would change by 1.5 percentage point, indicating even a marginal change in floor-space usage per employee (SPE) would have a significant impact on new office space development. Empirical estimates also suggest a strong response of office development starts to the lagged land supply and office space stock.

The paper raises the concern about the importance of non-price measures of the supply-side of the office market. There is scope to address the research questions using better data sets. It is also possible to model the supply adjustment process more dynamically in an error correction framework.

The findings would suggest that non-price measures, such as space-usage pattern, need to be taken into account when planning and estimating future office space needs. This finding provides valuable insight for our current knowledge on factors affecting new office supply.

This is the first study to introduce office floor space usage as a determinant of office development starts in an urban growth conceptual framework for the Hong Kong office market.