Search results

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.

In this study the martingale hypothesis concerning the stock index futures market is analyzed. The purpose is to understand how this notion concerning the behavior of the index futures affects the forecasting process. In addition, the forecasting of both daily and weekly stock index futures is examined. For daily forecasting, we find that the martingale method outperforms stepwise autoregressive and exponential smoothing methods However, for weekly forecasts, the stepwise autoregressive method is best.

This paper proposes a discrete time real options model with time‐dependent and serial correlated return process for a real estate development problem with waiting options. Based on a Martingale condition, the paper claims to be able to relax many unrealistic assumptions made in the typical real option pricing methodology. Our real option model is a new one without assuming the return process as “Ito Process”, specifically, without assuming a geometric Brownian motion. We apply the model to the condominium market in Tokyo metropolitan area in the period 1971‐1997 and estimate the value of waiting to invest in 1998‐2007. The results partly provide realistic estimates of the parameters and show the applicability of our model.

This paper aims to examine the impact of stock market liberalization on efficiency of the stock markets in Latin America.

Daily stock indices from Latin American countries, including Brazil, Mexico, Chile, Peru, Jamaica and Trinidad and Tobago, are used in the analysis. To examine the impact of stock market liberalization on efficiency, the authors use several approaches, including the runs test, Chow–Denning multiple variation ratio test, Wright variance ratio test, the martingale hypothesis test and the stochastic dominance (SD) test, on the above Latin American stock market indices.

The authors find that stock market liberalization does not improve stock market efficiency in Latin America.

This investigation is among the first to examine the impact of stock market liberalization on the efficiency of the stock markets. It is among the first to examine the impact of stock market liberalization on the efficiency of the Latin American stock markets. It is also among the first to apply the martingale hypothesis test and a SD approach on issue about efficient market.

To find martingale theory and methods of parameter vector estimation of multidimensional linear control system in dynamic system.

In parameter vector estimation of dynamic system, due to the limitation of conventional methods, the authors study the iterative formula and consistency of parameter vector estimation using the theory and methods of martingale, and extend its application field.

Some conditions of strongly consistent estimation and iterative formula are obtained. And the consistency of parameter vector estimation in multidimensional linear control system is discussed.

Solved some practical problems of parameter vector estimation in dynamic system.

The paper solve the accuracy problem of parameter vector estimation of multidimensional linear control system in dynamic system and extend parameter vector estimation application field.

This paper examines the martingale restriction for the KOSPI 200 index options market. And in cases of the rejections, we investigate the relative market efficiency between stock index and stock index options market, using approximate entropy (ApEn) method proposed by Pincus (1994), which quantifies a complexity, irregularity and unpredictability in time series. The empirical results of this study clearly reject the martingale restriction and regression analyses indicate that the historical returns of underlying index can explain about 25% of the price differences between option-implied and market index prices but the total trading volume can explain only a small portion of the price differences. These results have cast doubt on the informational efficiency of this market. Comparing the relative market efficiency based on ApEn have showed that the complexity or irregularity of KOSPI 200 index is larger than the index options during the entire sample period. But, Examining separately ApEn of the magnitude and the sign time series which compose log-returns document that stock index options market reflect more efficiently the information about the direction of price changes than the stock index market in 2014 and the efficiency of the index options market about the directional information may be affected by directional traders who prefer certain strategies designed by exploiting past stock market movements.

This paper examines the pricing of interest rates derivatives such as caps and swaptions in the pricing kernel framework. The underlying state variable is extended to the general infinitely divisible Levy process. For computational purposes, a simple pricing kernel as in Flesaker and Hughston (1996) and Jin and Glasserman (2001) is used. The main contribution or purpose of this paper is to find several proper positive martingales, which is key role of practical applications of the pricing kernel approach with interest rates guarantee to be positive. Particularly, this paper first finds and applies a quite general type of a positive martingale process to pricing interest rate derivatives such as swaptions and range notes in the incomplete market setting. Such interest rate derivatives are hard to find analytic solutions. Consequently, this paper shows that such a choice of the positive martingale in the kernel framework is a promising approach to price interest rate derivatives

In this work, the authors are interested in the notion of vector valued and set valued Pettis integrable pramarts. The notion of pramart is more general than that of martingale. Every martingale is a pramart, but the converse is not generally true.

In this work, the authors present several properties and convergence theorems for Pettis integrable pramarts with convex weakly compact values in a separable Banach space.

The existence of the conditional expectation of Pettis integrable mutifunctions indexed by bounded stopping times is provided. The authors prove the almost sure convergence in Mosco and linear topologies of Pettis integrable pramarts with values in (cwk(E)) the family of convex weakly compact subsets of a separable Banach space.

The purpose of the present paper is to present new properties and various new convergence results for convex weakly compact valued Pettis integrable pramarts in Banach space.

The purpose of this study to evaluate the evolving market efficiency of the housing market under the framework of adaptive market hypothesis and martingale difference hypothesis taking a case of India.

The study used a wild bootstrap version of the generalized spectral (GS) test in the rolling window framework to measure possible time-varying linear and non-linear dependence in the housing market.

The study finds that the Indian housing market, in general, is not efficient, and this efficiency is dynamic, which changes with time lending support to the adaptive market hypothesis. The study confirms that the evolutionary model of individuals adapting to a changing environment via behavioural biases affects the efficiency of the housing market, which leads to the evolving efficiency of the housing market prices.

The study believes that the potential implications go beyond evolutionary forces and the adaptive market hypothesis , which, does not only depend on an individual's decision-making process but also on social psychology. Thus, a further attempt in this line, taking into account the social psychology and quantitative rigour towards drivers of evolving efficiency is suggested for future research.

The study suggests that there is a possibility of extra returns for market players, but not always. The Indian housing market has witnessed several landmark reforms in recent years, so it is believed that these reforms would decrease the inefficiency level of this market. Contrary to this, the study’s findings reveal an increase in the inefficiency level in recent years. As the Indian housing market shows evolving efficiency, it is believed that the increased inefficiency is temporary. The increased inefficiency can be regarded as the settlement stage of the various policy and technical reforms.

Confirming the presence or absence of adaptive efficiency in the housing market under possible non-linear dependence will be a significant addition to the existing literature.

This study presents various GARCH models for predicting movements (returns) and volatility patterns in major national stock market indices. These models depict that future returns in the national stock markets of Australia, Belgium, Canada, France, Italy and Switzerland are predictable from past realized returns; therefore, the stock indices for these markets violate the Martingale model. The stock market indices for Germany, Japan, the United Kingdom and the United States, however, behave as Martingale processes. Volatility patterns in these indices are predictable using past realized volatility measures.