The Random Walk Hypothesis in the Emerging Indian Stock Market

This paper examines the random walk hypothesis in the emerging Indian stock market using daily data on individual stocks. The statistical evidence in this paper rejects the random walk hypothesis. The results suggest that daily returns earned by individual stocks and by an equally weighted portfolio show significant non–linear dependence and persistent volatility effects. The non–linear dependence takes the form of ARCH–type conditional heteroskedasticity and does not appear to be caused by nonstationarity of underlying economic variables. Though conditional volatility is time varying, it does not explain expected returns.     

Forecasting Stock Index Futures Price Volatility: Linear vs. Nonlinear Models

The study examines the relative ability of various models to forecast daily stock index futures volatility. The forecasting models that are employed range from naïve models to the relatively complex ARCH-class models. It is found that among linear models of stock index futures volatility, the autoregressive model ranks first using the RMSE and MAPE criteria. We also examine three nonlinear models. These models are GARCH-M, EGARCH, and ESTAR. We find that nonlinear GARCH models dominate linear models utilizing the RMSE and the MAPE error statistics and EGARCH appears to be the best model for forecasting stock index futures price volatility.     

Assessing recreation carrying capacity of the environment attributes based on visitors’ willingness to pay

Recreation carrying capacity (RCC) is one of the most important indictors used in measuring the usage limits for the forest park resources. However, the consensus has not been reached with respect to the RCC’s content and its measuring metrics. In this paper, we attempt to establish a new RCC theoretical framework based on the demand theory of the tourism product characteristics. In the process, the choice experiment and orthogonal design methods are used in questionnaire designs and the conditional Logit model is used for parameter estimates. More than 700 park visitors are surveyed for data collection at the Shenyang National Forest park of Liaoning province in China. The primary park attributes being considered encompass vegetation, coverage, water quality, number of rubbish on the park trail path, admission fees, and congestion. The carrying capacity threshold for each specific attribute is identified.     

Multivariate contemporaneous-threshold autoregressive models

This paper proposes a contemporaneous-threshold multivariate smooth transition autoregressive (C-MSTAR) model in which the regime weights depend on the ex-ante probabilities that latent regime-specific variables exceed certain threshold values. A key feature of the model is that the transition function depends on all the parameters of the model as well as on the data. Since the mixing weights are also a function of the regime-specific noise covariance matrix, the model can account for contemporaneous regime-specific co-movements of the variables. The stability and distributional properties of the proposed model are discussed, as well as issues of estimation, testing and forecasting. The practical usefulness of the C-MSTAR model is illustrated by examining the relationship between US stock prices and interest rates.     

A unified approach to bivariate discrete distributions

Here we introduce a bivariate generalized hypergeometric factorial moment distribution (BGHFMD) through its probability generating function (p.g.f.) whose marginal distributions are the generalized hypergeometric factorial moment distributions introduced by Kemp and Kemp (Bull Int Stat Inst 43:336–338,1969). Well-known bivariate versions of distributions such as binomial, negative binomial and Poisson are special cases of this distribution. A genesis of the distribution and explicit closed form expressions for the probability mass function of the BGHFMD, its factorial moments and the p.g.f.’s of its conditional distributions are derived here. Certain recurrence relations for probabilities, moments and factorial moments of the bivariate distribution are also established.     

空间分析在住宅价值评估中应用研究综述

住宅是一种异质性商品,它的构成使用价值的各个特征之间有明显的差异,因此特征价格定价模型广泛用于住宅的价值评估。但是由于住宅数据中常常表现出空间问题,使得传统的特征价格模型不再适用,空间效应在不动产领域的存在引起了学者们的广泛关注。对空间分析在住宅价值评估中的应用研究进行了回顾和阐述,首先分析空间效应的来源和影响,讨论了在传统模型中纳入空间效应的重要性,接着阐述如何对这些空间效应进行有效建模和检验,并分析了进一步研究的方向。     

Detecting nonlinearity in time series by model selection criteria

This article analyzes the use of model selection criteria for detecting nonlinearity in the residuals of a linear model. Model selection criteria are applied for finding the order of the best autoregressive model fitted to the squared residuals of the linear model. If the order selected is not zero, this is considered as an indication of nonlinear behavior. The BIC and AIC criteria are compared to some popular nonlinearity tests in three Monte Carlo experiments. We conclude that the BIC model selection criterion seems to offer a promising tool for detecting nonlinearity in time series. An example is shown to illustrate the performance of the tests considered and the relationship between nonlinearity and structural changes in time series.     

Analyzing quantile spillover effects among international financial markets

This paper investigates the quantile-based spillover effects among 17 stock markets from January 1993 to January 2022, utilizing a quantile approach based on the variance decomposition of a quantile vector autoregression (QVAR) model. Compared with the traditional mean-based spillover measures, this new quantile approach allows for a nuanced investigation of spillovers at every quantile and capture spillovers under extreme events. The results show that: (1) the total spillover is high and exhibits strong time-varying characteristics, and the tail spillover is higher and more complex in scale and direction; (2) the spillover at each quantile level shows an upward trend, especially during the 2008 crisis and the COVID-19 epidemic; (3) developed countries (or regions) are the net exporters of stock market spillovers, while the developing countries are the net importers; and (4) the 17 stock markets constitute different local financial networks, which may be related to economic conditions and geographical location.     

The effects of oil price shocks on expenditure category CPI

In this study, we apply a two-block structural vector autoregressive (VAR) model proposed by Kilian and Park (2009) in order to investigate the dynamic effects of changes in oil price on the expenditure category consumer price index (CPI) in the United States and Japan. Our results confirm that each expenditure category price index responded very differently to the same structural shock, and that whether changes in oil price function as a positive stimulus or a negative shock for the individual expenditure category prices also depends on the kind of underlying shock that drives the changes in oil price. Finally, our results also reveal that the manner in which changes in oil price affect each expenditure category price differs between the United States and Japan and these detailed-level differences may lead to aggregate-level differences in the price response of both countries to changes in oil price.