Oligopsony/oligopoly power and factor market performance

This paper is concerned with the economic performance of factor markets in an oligopsony/ oligopoly setting. Firm arid industry indexes are developed to measure factor market price distortions caused by exerted oligopsony/oligopoly power. These measures indicate that the elasticity of output demand, the elasticity of input supply, and the input and output conjectural elasticities determine the degree of non-competitive performance in factor markets. It is also shown that under special conditions the firm index equals the Lerner index and the industry index equals the Herfindahl-Hirschman index.     

Efficiency,growth and market power in the banking industry: New approach to efficient structure hypothesis

We extend the work of Homma, Tsutsui, and Uchida (2014) to provide empirical evidence on nexus of relationships in efficient structure (ES) hypothesis. In this framework, we test causality from cost efficiency to bank growth and then from bank growth to market concentration. We apply this approach to banking industry in Association of South East Asian (ASEAN) over the period of 1999–2014. The efficiency scores have been estimated by employing Slack Based Measurements Data Envelopment Analysis (SMB DEA). We apply Two-step system Generalized Method of Moments (GMM) and Panel Vector Auto Regression (PVAR) to account for endogeneity in estimation models. The results show that cost efficiency enables the banks to grow and obtain higher market share. The resultant growth then leads to higher market concentration/bank market power. There is also some evidence to support for quiet life (QL) hypothesis. Therefore, both ES and QL hypotheses may coexist in ASEAN banking industry.     

Forecasting electricity prices using bid data

Market liberalization and the expansion of variable renewable energy sources in power systems have made the dynamics of electricity prices more uncertain, leading them to show high volatility with sudden, unexpected price spikes. Thus, developing more accurate price modeling and forecasting techniques is a challenge for all market participants and regulatory authorities. This paper proposes a forecasting approach based on using auction data to fit supply and demand electricity curves. More specifically, we fit linear (LinX-Model) and logistic (LogX-Model) curves to historical sale and purchase bidding data from the Iberian electricity market to estimate structural parameters from 2015 to 2019. Then we use time series models on structural parameters to predict day-ahead prices. Our results provide a solid framework for forecasting electricity prices by capturing the structural characteristics of markets.     

Securitization, liquidity, and the Brady plan

Securitization of bank loans is proposed as a solution to the ongoing debt difficulties for emerging market nations. This paper analyzes the effects of the Brady plan for four participating countries. In addition to resolving repayment problems, the plan had a statistically significant effect on prices due to its positive impact on liquidity. Previously, the market for traded bank debt was illiquid and dominated by a few large traders who depressed the price by exerting oligopsony power. By using Perron’s method of discerning breaks, it is found that the introduction of bonds led to structural change in the LDC secondary debt market. These effects are in addition to those of debt resolution and domestic reform.     

The Social and Environmental Costs of Milk Production: Trends and Resistance in Vermont

The power of social institutions to influence patterns of behavior is evident in the dairy industry. Secondary data from the National Agricultural Statistics Service (NASS) show that dairy operators adapt to market pressures by expanding the size of their herds and/or adopting technologies that intensify milk production. A grounded theory approach using primary data collected in interviews with organic dairy operators reveals active resistance to the power imposed upon them by social institutions.     

Measurement of industry conduct with a latent structure

Empirical industrial organization economists have long been concerned with measuring the degree of competition in markets and understanding its underlying determinants, but they have faced serious difficulties, including data requirements and misspecification issues. This paper offers an alternative method of estimating market power and industry conduct. Market power is estimated for the fresh apple industry with a latent modelling structure. Specifically, variations of the MIMIC model are adapted to this problem. These variations are the multiple‐indicators (MI) and the filtered‐measure (FM) approaches. The results suggest that although the Washington apple industry has a large market share, it does not exercise market power. Factors that mitigate the exertion of market power in the fresh apple market include competitor shipments and retailer power. Copyright © 2004 John Wiley & Sons, Ltd.     

A conditionally heteroskedastic independent factor model with an application to financial stock returns

We propose a new conditionally heteroskedastic factor model, the GICA-GARCH model, which combines independent component analysis (ICA) and multivariate GARCH (MGARCH) models. This model assumes that the data are generated by a set of underlying independent components (ICs) that capture the co-movements among the observations, which are assumed to be conditionally heteroskedastic. The GICA-GARCH model separates the estimation of the ICs from their fitting with a univariate ARMA-GARCH model. Here, we will use two ICA approaches to find the ICs: the first estimates the components, maximizing their non-Gaussianity, while the second exploits the temporal structure of the data. After estimating and identifying the common ICs, we fit a univariate GARCH model to each of them in order to estimate their univariate conditional variances. The GICA-GARCH model then provides a new framework for modelling the multivariate conditional heteroskedasticity in which we can explain and forecast the conditional covariances of the observations by modelling the univariate conditional variances of a few common ICs. We report some simulation experiments to show the ability of ICA to discover leading factors in a multivariate vector of financial data. Finally, we present an empirical application to the Madrid stock market, where we evaluate the forecasting performances of the GICA-GARCH and two additional factor GARCH models: the orthogonal GARCH and the conditionally uncorrelated components GARCH.     

Dynamic relationships between industry returns and stock market returns

Different from prior studies which concentrate on the unidirectional impact of industry leading, this study examines the bi-directional dynamical causal relation between industry returns and stock market returns by considering multiple structural breaks for ten major eastern and southern Asia countries. Our results show that finance and consumer service industry returns have significant power in explaining the movements of market returns. Further, we apply logit regressions to explore the determinants of the leading hypotheses and find exchange rate and interest rate are important in explaining the industry–market nexus. In a developed market the industry and the market have feedback relations, but in a highly controlled economy the influence from the stock market dominates.     

Competition and market power in the European airline industry: 1976–90

In this paper, we study the market power of European airlines in an oligopoly structure with product differentiation for the period 1976–1990 and test the monopoly hypothesis (Captain, 1993). The paper analyzes the level of competition among eight major European airlines and finds little evidence for market power in the industry over that period. One of our main findings is that the high prices in Europe are not entirely due to the bilateral agreements (leading to possible monopoly power), but rather are a result of very high cost structures in the industry. These results are broadly in agreement with those of Roeller and Sickles (1994) which analyzes these issues in the European Industry by estimating a structural, two-stage game. © 1997 John Wiley & Sons, Ltd.     

Structural break in different stock index markets in China

This paper first presents a two-stage change point estimation approach in the framework of online analysis to detect the Chinese stock market abrupt variations during the period from 4 January 2005 to 10 December 2021. As a check, the pruned exact linear time (PELT) algorithm method is applied to detect structural changes in the framework of offline analysis in terms of all data. We select four representative indices in Chinese markets to find some important time-stamp tags. The results show that all indices can detect some common events, while the small-cap and small-mid-cap indices can identify local risks such as China’s market freezing. Besides, we find some events such as the global financial crisis and China’s market freezing can incur the inverse anomaly with higher volatility in lower reward.     

Inference on stochastic time-varying coefficient models

Recently, there has been considerable work on stochastic time-varying coefficient models as vehicles for modelling structural change in the macroeconomy with a focus on the estimation of the unobserved paths of random coefficient processes. The dominant estimation methods, in this context, are based on various filters, such as the Kalman filter, that are applicable when the models are cast in state space representations. This paper introduces a new class of autoregressive bounded processes that decompose a time series into a persistent random attractor, a time varying autoregressive component, and martingale difference errors. The paper examines, rigorously, alternative kernel based, nonparametric estimation approaches for such models and derives their basic properties. These estimators have long been studied in the context of deterministic structural change, but their use in the presence of stochastic time variation is novel. The proposed inference methods have desirable properties such as consistency and asymptotic normality and allow a tractable studentization. In extensive Monte Carlo and empirical studies, we find that the methods exhibit very good small sample properties and can shed light on important empirical issues such as the evolution of inflation persistence and the purchasing power parity (PPP) hypothesis.     

Long memory in the Ukrainian stock market and financial crises

This paper examines persistence in the Ukrainian stock market during the recent financial crisis. Using two different long memory approaches (R/S analysis and fractional integration) we show that this market is inefficient and the degree of persistence is not the same at different stages of the financial crisis. Therefore trading strategies might have to be modified. We also show that data smoothing is not advisable in the context of R/S analysis.     

Bubble-like housing boom–bust cycles: Evidence from the predictive power of households’ expectations

This study examines the role of households’ expectations in predicting the housing boom–bust cycles in the United States. It incorporates two nonlinear features of housing price dynamics: a threshold co-movement between households’ expectations and housing price growth and a structural break in their interrelation. It uses the monthly good-time-to-buy (GTTB) index as a proxy for households’ expectations about the U.S. housing market, and employs the structural break threshold vector autoregression (SBTVAR) to specify breakpoints in housing market dynamics during the recent decades. The findings indicate that shifts in interactions between households’ expectations and housing price growth are synchronous with the recent housing boom–bust cycles. The SBTVAR framework outperforms other models as it captures more of the housing market's unique dynamic characteristics. The GTTB index, which governs expectation regime-switching patterns, is able to signal the recent housing bust three periods in advance.     

British Bus Deregulation: Competition and Demand Coordination

Our framework formally explains some of the recent curious events surrounding deregulation of the British local bus industry. The winner-takes-all nature of this market induces competitive providers to engage in predatory or preemptive behavior in scheduling and pricing. Unlike previous works, we explicitly model consumers' responses to firms' schedule announcements. We show that the market is unstable if demand is uncoordinated, but stable otherwise. Our results highlight the important role played by demand-coordinating mechanisms in local bus markets and our results suggest that pure-strategy Nash equilibria exist in models à-la-Hotelling (1929) whenever demand is effectively coordinated.     

The saving,human wealth and asset pricing nexus: Evidence from around the world

The present study examines the dynamics of the saving, human wealth and asset pricing nexus across developed and emerging economies. We introduce two equilibrium asset pricing models in an intertemporal capital asset pricing framework, including the priced factors human wealth and market portfolio in the first framework and the saving and market portfolio in the second framework. Both asset pricing frameworks consist of two-factor, four-factor and five-factor asset pricing models. We control for size and value factors in the four-factor model and size, value and momentum factors in the five-factor model. The IV-GMM estimation and GRS test results indicate that human wealth and market portfolio for the first framework and saving and market portfolio in the second framework are primary priced factors in explaining the average returns for developed economies and the aggregate level. On the contrary, both frameworks fail to yield significant results explaining the average returns for emerging economies.     

THE EVOLUTION OF STOCK MARKET EFFICIENCY OVER TIME: A SURVEY OF THE EMPIRICAL LITERATURE

Abstract This paper provides a systematic review of the weak‐form market efficiency literature that examines return predictability from past price changes, with an exclusive focus on the stock markets. Our survey shows that the bulk of the empirical studies examine whether the stock market under study is or is not weak‐form efficient in the absolute sense, assuming that the level of market efficiency remains unchanged throughout the estimation period. However, the possibility of time‐varying weak‐form market efficiency has received increasing attention in recent years. We categorize these emerging studies based on the research framework adopted, namely non‐overlapping sub‐period analysis, time‐varying parameter model and rolling estimation window. An encouraging development is that the documented empirical evidence of evolving stock return predictability can be rationalized within the framework of the adaptive markets hypothesis.     

ECONOMETRIC APPROACHES TO EMPIRICAL MODELS OF EXCHANGE RATE DETERMINATION

Abstract. This paper identifies four principal econometric approaches to the estimation and testing of asset market models of exchange rate determination: the traditional, static reduced-form approach; the error correction and co-integration, dynamic reduced-form approaches; the simultaneous equations approach; and large scale, multi-equation macroeconometric simulation models. Each of these econometric approaches is evaluated with respect to its theoretical validity and the comparative properties of the empirical results obtained. This leads to the conclusion that although there may be little to choose between the different theoretical exchange rate models, there may be grounds for favouring a multi-equation, simultaneous estimation procedure for this class of models.     

Forecasting the Chinese stock market volatility with international market volatilities: The role of regime switching

The purpose of this paper is to investigate the role of regime switching in the prediction of the Chinese stock market volatility with international market volatilities. Our work is based on the heterogeneous autoregressive (HAR) model and we further extend this simple benchmark model by incorporating an individual volatility measure from 27 international stock markets. The in-sample estimation results show that the transition probabilities are significant and the high volatility regime exhibits substantially higher volatility level than the low volatility regime. The out-of-sample forecasting results based on the Diebold-Mariano (DM) test suggest that the regime switching models consistently outperform their original counterparts with respect to not only the HAR and its extended models but also the five used combination approaches. In addition to point accuracy, the regime switching models also exhibit substantially higher directional accuracy. Furthermore, compared to time-varying parameter, Markov regime switching is found to be a more efficient way to process the volatility information in the changing world. Our results are also robust to alternative evaluation methods, various loss functions, alternative volatility estimators, various sample periods, and various settings of Markov regime switching. Finally, we provide an extension of forecasting aggregate market volatility on monthly frequency and observe mixed results.     

AN INVESTIGATION OF INDUSTRY AND FIRM STRUCTURAL CHARACTERISTICS IN CORPORATE TURNAROUNDS*

This article uses the structure/conduct/performance framework as an underpinning to investigate the attributes of turnaround firms. Turnaround is defined as a substantial improvement of the firms's return on assets relative to the average return of its industry. Industry and firm structural characteristics including concentration, industry growth, R&D, advertising, market share, size, diversification, capital intensity and margins are identified. The results of a series of univariate and multivariate tests run on a sample of turnaround and non-turnaround firms indicate that size, R&D, and an interaction between operating margin and advertising can be helpful in explaining some turnaround situations.     

Crude oil price forecasting based on internet concern using an extreme learning machine

The growing internet concern (IC) over the crude oil market and related events influences market trading, thus creating further instability within the oil market itself. We propose a modeling framework for analyzing the effects of IC on the oil market and for predicting the price volatility of crude oil’s futures market. This novel approach decomposes the original time series into intrinsic modes at different time scales using bivariate empirical mode decomposition (BEMD). The relationship between the oil price volatility and IC at an individual frequency is investigated. By utilizing decomposed intrinsic modes as specified characteristics, we also construct extreme learning machine (ELM) models with variant forecasting schemes. The experimental results illustrate that ELM models that incorporate intrinsic modes and IC outperform the baseline ELM and other benchmarks at distinct horizons. Having the power to improve the accuracy of baseline models, internet searching is a practical way of quantifying investor attention, which can help to predict short-run price fluctuations in the oil market.