Shixuan Wang

Detecting At‐Most‐M Changes in Linear Regression Models

In this article, we provide a new procedure to test for at-most-m changes in the time-dependent regression model yt = xt⊤ βt et , 1 ≤ t ≤ T, that is, β1 = β2 = ⋯ = βT under the no-change null hypothesis against the alternative yt = xt⊤ β(i) et , if ki –1 < t ≤ ki*, 1 ≤ i ≤ m 1 and β(j) ≠ β(𝓁) for some 1 ≤ j,𝓁 ≤ m 1 with k0 = 0, 1 < k1* < k2* < ⋯ < km* < T, km 1 = T. Our procedure is based on weighted sums of the residuals, incorporating the possibility of m changes. The weak limit of the proposed test statistic is the sum of two double-exponential random variables. A small Monte Carlo simulation illustrates the applicability of the limit results in case of small and moderate sample sizes. We compare the new method to the cumulative sum control chart (CUSUM) and standardized (weighted) CUSUM procedures and obtain the power curves of the test statistics under the alternative. We apply our method to find changes in the unconditional four-factor capital asset pricing model.

The boomerang returns? Accounting for the impact of uncertainties on the dynamics of remanufacturing systems

Recent years have witnessed companies abandon traditional open-loop supply chain structures in favour of closed-loop variants, in a bid to mitigate environmental impacts and exploit economic opportunities. Central to the closed-loop paradigm is remanufacturing: the restoration of used products to useful life. While this operational model has huge potential to extend product life-cycles, the collection and recovery processes diminish the effectiveness of existing control mechanisms for open-loop systems. We systematically review the literature in the field of closed-loop supply chain dynamics, which explores the time-varying interactions of material and information flows in the different elements of remanufacturing supply chains. We supplement this with further reviews of what we call the three ‘pillars’ of such systems, i.e. forecasting, collection, and inventory and production control. This provides us with an interdisciplinary lens to investigate how a ‘boomerang’ effect (i.e. sale, consumption, and return processes) impacts on the behaviour of the closed-loop system and to understand how it can be controlled. To facilitate this, we contrast closed-loop supply chain dynamics research to the well-developed research in each pillar; explore how different disciplines have accommodated the supply, process, demand, and control uncertainties; and provide insights for future research on the dynamics of remanufacturing systems.

Structural breaks in panel data: Large number of panels and short length time series

ABSTRACT The detection of (structural) breaks or the so called change point problem has drawn increasing attention from the theoretical, applied economic and financial fields. Much of the existing research concentrates on the detection of change points and asymptotic properties of their estimators in panels when N, the number of panels, as well as T, the number of observations in each panel are large. In this paper we pursue a different approach, i.e., we consider the asymptotic properties when N→∞ while keeping T fixed. This situation is typically related to large (firm-level) data containing financial information about an immense number of firms/stocks across a limited number of years/quarters/months. We propose a general approach for testing for break(s) in this setup. In particular, we obtain the asymptotic behavior of test statistics. We also propose a wild bootstrap procedure that could be used to generate the critical values of the test statistics. The theoretical approach is supplemented by numerous simulations and by an empirical illustration. We demonstrate that the testing procedure works well in the framework of the four factors CAPM model. In particular, we estimate the breaks in the monthly returns of US mutual funds during the period January 2006 to February 2010 which covers the subprime crises.

Asset Return & Camel Process: Beauty and the Beast

In this paper, we propose a new diffusion process referred to as the “camel process” in order to model the cumulative return of a financial asset. This new process includes three parameters, the market condition parameter α, the overreaction correction parameter β, and the volatility parameter γ. Its steady state probability density function could be unimodal or bimodal, depending on the sign of the market condition parameter. The overreaction correction is realised through the non-linear drift term which incorporates the cube term of the instantaneous cumulative return. The time-dependent solution of its Fokker-Planck equation cannot be obtained analytically, but can be numerically solved using the finite difference method. The properties of the camel process are confirmed by our empirical estimation results of ten market indexes in two different periods.

Understanding the Chinese stock market: international comparison and policy implications

Abstract The definitions of the bear, sidewalk and bull markets are ambiguous in the existing literature. This makes it difficult for practitioners to distinguish between different market conditions. In this paper, we propose statistical definitions of the bear, sidewalk and bull markets, which correspond to the three states in our hidden semi-Markov model. We apply this analysis to the daily returns of the Chinese stock market and seven developed markets. Using the Viterbi algorithm to globally decode the most likely sequence of the market conditions, we systematically find the precise timing of the bear, sidewalk and bull markets for all the eight markets. Through the comparison of the estimation and decoding results, many unique characteristics of the Chinese stock market are revealed, such as ‘crazy bull’, ‘frequent and quick bear’ and ‘no buffer zone’. In China, the bull market is more volatile than in developed markets, the bear market occurs more frequently than in developed markets, and the sidewalk market has not functioned as a buffer zone since 2005. Possible causes of these unique characteristics are also discussed and implications for policy-making are suggested.

Testing bubbles: exuberance and collapse in the Shanghai A-share stock market

When a stock market bubble bursts, it can trigger financial crises that spread to the real economy. New and selectively complicated time-series methods are emerging that allow for better understanding of bubbles. In this chapter, we use the sup augmented Dickey–Fuller test (SADF) test developed by Phillips et al. (2011) and the generalised sup augmented Dickey–Fuller test (GSADF) test developed by Phillips et al. (2013) to identify bubbles in the Shanghai A-share stock market; the tests can also track a bubble’s origination and termination dates. To our knowledge, this is the first time in the literature that the SADF and the GSADF have been applied to this stock market.