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The purpose of this paper is to examine the volatility of daily returns in a sample of developed and emerging equity markets at different time scales through wavelet decomposition. Such information is vital for international investors who have different time horizons for their investment decisions and trading strategies.

The wavelet technique used here allows the return series to be viewed at different frequency by decomposing the series into different time horizons known as time scales. The decomposed return series enable investigation of return variability at different return intervals.

In an analysis at different time scales, there is no evidence to suggest that the return dynamics of developed and emerging markets are different. In both types of markets, return variance is time scale dependent, satisfying a pure power law process, and the variability in returns is more likely to be due to the dynamics at the lower time scales. While emerging markets generally exhibit a higher level of volatility, the relative contribution from each time scale is quite similar to that of the developed markets.

The difference in the return dynamics between emerging and developed markets is observed at the lowest time scale. This is an indication that differences in the return dynamics between the two types of markets may be more likely in the short term (high frequency) rather than in the long term. A plausible reason for this is speculative trading. Such information is vital for international investors who have different time horizons for their investment decisions and trading strategies.

The purpose of this paper is to examine contagion among the major world markets during the last 25 years and propose a new way to analyze contagion with wavelet methods.

The analysis uses a novel way to study contagion using wavelet methods. The comparison is made between co‐movements at different time scales. Co‐movement methods of the discrete wavelet transform and the continuous wavelet transform are applied.

Clear signs of contagion among the major markets are found. Short time scale co‐movements increase during the major crisis while long time scale co‐movements remain approximately at the same level. In addition, gradually increasing interdependence between markets is found.

Because of the chosen method, the approach is limited to large data sets.

The research has practical implications to portfolio managers etc. who wish to have better view of the dynamics of the international equity markets.

The research uses novel wavelet methods to analyze world equity markets. These methods allow the markets to be analyzed in the whole state space.

The purpose of this paper is to revisit the dynamic linkages between the US and the national securitized real estate markets of each of the nine Asian-Pacific (APAC) economies in time-frequency domain.

Wavelet decomposition via multi-resolution analysis is employed as an empirical methodology to consider time-scale issue in studying the dynamic changes of the US–APAC cross-real estate interdependence.

The strength and direction of return correlation, return exogeneity, shock impulse response, market connectivity and causality interactions change when specific time-scales are involved. The US market correlates with the APAC markets weakly or moderately in the three investment horizons with increasing strength of lead-lag interdependence in the long-run. Moreover, there are shifts in the net total directional volatility connectivity effects at the five scales among the markets.

Given the focus of the five approaches and associated indicators, the picture that emerges from the empirical results may not completely uniform. However, long-term investors and financial institutions should evaluate the time-scale based dynamics to derive a well-informed portfolio decision.

Future research is needed to ascertain whether the time-frequency findings can be generalizable to the regional and global context. Additional studies are required to identify the factors that contribute to the changes in the global and regional connectivity across the markets over the three investment horizons.

This study has successfully decomposed the various market linkage indicators into scale-dependent sub-components. As such, market integration in the Asia-Pacific real estate markets is a “multi-scale” phenomenon.

The purpose of this paper is to propose an integrative model of time management, and in particular develop a scale to measure organizational variables which would facilitate and support time management practices. The research also examined whether the time management environment is related to turnover intentions and stress.

Three studies are reported. Study 1 sampled 262 employees from 20 organizations and these data were used for the initial factor analysis of the time management environment (TiME) scale. Study 2 sampled 205 employees from an aircraft maintenance organization, and these data were used to further refine the factor structure of the TiME scale, to conduct a CFA, examine the relationship between the TiME scale factors and turnover intentions, and to examine the test‐retest reliability of the TiME scale. Study 3 sampled 156 employees across eight organizations, and these data were used to examine the relationship between the TiME scale factors and stress.

The TiME scale has five factors, and each has acceptable internal consistency and test‐retest reliability. TiME scale factor scores were negatively correlated with both turnover intentions and stress.

The research did not examine the convergent and discriminant validity of the TiME scale.

The TiME scale provides for the assessment of whether an organization's environment is facilitating and supporting its employees' attempts to engage in time management, and can also be used as a measure of transfer climate for time management training interventions.

The TiME scale addresses a gap in the time management literature. It has considerable applied value, and along with our integrative model should allow for the development of a more complex understanding of the time management process.

Apart from the well-known, high persistence of daily financial volatility data, there is also a short correlation structure that reverts to the mean in less than a month. We find this short correlation time scale in six different daily financial time series and use it to improve the short-term forecasts from generalized auto-regressive conditional heteroskedasticity (GARCH) models. We study different generalizations of GARCH that allow for several time scales. On our holding sample, none of the considered models can fully exploit the information contained in the short scale. Wavelet analysis shows a correlation between fluctuations on long and on short scales. Models accounting for this correlation as well as long-memory models for absolute returns appear to be promising.

A striking feature of Jaques' work is his “no nonsense” attitude to the “manager‐subordinate” relationship. His blunt account of the origins of this relationship seems at first sight to place him in the legalistic “principles of management” camp rather than in the ranks of the subtler “people centred” schools. We shall see before long how misleading such first impressions can be, for Jaques is not making simplistic assumptions about the human psyche. But he certainly sees no point in agonising over the mechanism of association which brings organisations and work‐groups into being when the facts of life are perfectly straightforward and there is no need to be squeamish about them.

The purpose of this study is to discover and model the asymmetry in the price volatility of financial markets, in particular the foreign exchange markets as the first underlying applications.

The volatility of the financial market price is usually defined with the standard deviation or variance of the price or price returns. This standard definition of volatility is split into the upper part and the lower one, which are termed here as Yang volatility and Yin volatility. However, the definition of yin‐yang volatility depends on the scale of the time, thus the notion of scale space of price‐time is also introduced.

It turns out that the duality of yin‐yang volatility expresses not only the asymmetry of price volatility, but also the information about the trend. The yin‐yang volatilities in the scale space of price‐time provide a complete representation of the information about the multi‐level trends and asymmetric volatilities. Such a representation is useful for designing strategies in market risk management and technical trading. A trading robot (a complete automated trading system) was developed using yin‐yang volatility, its performance is shown to be non‐trivial. The notion and model of yin‐yang volatility has opened up new possibilities to rewrite the option pricing formulas, the GARCH models, as well as to develop new comprehensive models for foreign exchange markets.

The asymmetry of price volatility and the magnitude of volatility in the scale space of price‐time has yet to be united in a more coherent model.

The new model of yin‐yang volatility and scale space of price‐time provides a new theoretical structure for financial market risk. It is likely to enable a new generation of core technologies for market risk management and technical trading strategies.

This work is original. The new notion and model of yin‐yang volatility in scale space of price‐time has cracked up the core structure of the financial market risk. It is likely to open up new possibilities such as: a new portfolio theory with a new objective function to minimize the sum of the absolute yin‐volatilities of the asset returns, a new option pricing theory using yin‐yang volatility to replace the symmetric volatility, a new GARCH model aiming to model the dynamics of yin‐yang volatility instead of the symmetric volatility, new technical trading strategies as are shown in the paper.

The purpose of this paper is to reveal the multi‐scale relation between power law distribution and correlation of stock returns and to figure out the determinants underlying capital markets.

The multi‐scale relation between power law distribution and correlation is investigated by comparing the original series with the special series. The eliminating intraday trend series approach developed by Liu et al. is utilized to analyze the effects of power law decay change on correlation properties, and shuffling series originated by Viswanathan et al. for the impacts of special type of correlation on power‐law distribution.

It is found that the accelerating decay of power law has an insignificant effect on correlation properties of returns and the empirical results indicate that time scale may also be an important factor maintaining power law property of returns besides correlation. When time scale is under critical point, the effects of correlation are crucial, and the correlation of nonlinear long‐range presents the strongest influence. However, for time scale beyond critical point, the impact of correlation begins to diminish or even finally disappear and then the power law property shows complete dependence on time scale.

The 5‐min high frequency data of the Shanghai market as the empirical benchmark is insufficient to depict the relation over the entire time scale in the Chinese stock market.

The paper identifies the determinants of market dynamics to apply them to risk management through analysis of multi‐scale relations, and supports endeavors to introduce time parameter into further risk measures and control.

The paper provides the empirical evidence that time scale is one of the key determinants of market dynamics by analyzing the multi‐scale relation between power law distribution and correlation.

The social sciences are really the “hard sciences” and the physical sciences are the “easy” sciences. One of the great contributors to making the job of the social scientist very difficult is the lack of fundamental dimensions on the basis of which absolute (i.e. ratio) scales can be formulated and in which relationships could be realized as the [allegedly] coveted equations of physics. This deficiency leads directly to the uses of statistical methods of various types. However it is possible, as shown, to formulate equations and to use them to obtain ratio/absolute scales and relationships based on them. This paper uses differential/integral equations, fundamental ideas from the processing view of the brain‐mind, multiple scale approximation via Taylor series, and basic reasoning some of which may be formulated as infinite‐valued logic, and which is related to probability theory (the theoretical basis of statistics) to resolve some of the basic issues relating to learning theory, the roles of nature and nurture in intelligence, the measurement of intelligence itself, and leads to the correct formulation of the potential‐actual type behaviors (specifically intelligence) and dynamical‐temporal model of intelligence development. Specifically, it is shown that the: (1) basic model for intelligence in terms of genetics and environment has to be multiplicative, which corresponds to a logical‐AND, and is not additive; (2) related concept of “genetics” creating its own environment is simply another way of saying that the interaction of genetics and environment is multiplicative as in (1); (3) timing of environmental richness is critical and must be modeled dynamically, e.g. in the form of a differential equation; (4) path functions, not point functions, must be used to model such phenomena; (5) integral equation formulation shows that intelligence at any time t, is a a sum over time of the past interaction of intelligence with environmental and genetic factors; (6) intelligence is about 100 per cent inherited on a global absolute (ratio) scale which is the natural (dimensionless) scale for measuring variables in social science; (7) nature of the approximation assumptions implicit in statistical methods leads to “heritability” calculations in the neighborhood of 0.5. and that short of having controlled randomized experiments such as in animal studies these are expected sheerely due to the methods used; (8) concepts from AI, psychology, epistemology and physics coincide in many respects except for the terminology used, and these concepts can be modeled nonlinearly.

The purpose of this paper is to derive crucial insights from multi-scale analysis to detect equity return co-movements between Korean and emerging Asian markets.

Wavelet correlation, wavelet coherence and wavelet clustering measures are used to uncover Korean equity market interactions which are hard to see using any other modern econometric method and which would otherwise had remained hidden.

The authors observed that Korean equity market is strongly integrated with Asian equity markets at lower frequency scales and has a relatively weak correlation at higher frequencies. Further this correlation eventually grows strong in the interim of crises period at lower frequency scales. The authors, however, do not found any significant deviation in dendrograms generated in data clustering process from wavelet scale 2 to 6 which are associated with four and 64 weeks period, respectively. Overall the findings are relevant and have strong policy and practical implications.

The unique contribution of this paper is that it introduces wavelet clustering analysis to produce a nested hierarchy of similar markets at each frequency level for the first time in finance literature