Adaptive Interest Rate Modelling

A good description of the dynamics of interest rates is crucial to price derivatives and to hedge corresponding risk. Interest rate modelling in an unstable macroeconomic context motivates one factor models with time varying parameters. In this paper, the local parameter approach is introduced to adaptively estimate interest rate models. This method can be generally used in time varying coefficient parametric models. It is used not only to detect the jumps and structural breaks, but also to choose the largest time homogeneous interval for each time point, such that in this interval, the coeffcients are statistically constant. We use this adaptive approach and apply it in simulations and real data. Using the three month treasure bill rate as a proxy of the short rate, we nd that our method can detect both structural changes and stable intervals for homogeneous modelling of the interest rate process. In more unstable macroeconomy periods, the time homogeneous interval can not last long. Furthermore, our approach performs well in long horizon forecasting.CIR model, Interest rate, Local parametric approach, Time homogeneous interval, Adaptive statistical techniques

Generalized quantile regression

Die generalisierte Quantilregression, einschließlich der Sonderfälle bedingter Quantile und Expektile, ist insbesondere dann eine nützliche Alternative zum bedingten Mittel bei der Charakterisierung einer bedingten Wahrscheinlichkeitsverteilung, wenn das Hauptinteresse in den Tails der Verteilung liegt. Wir bezeichnen mit v_n(x) den Kerndichteschätzer der Expektilkurve und zeigen die stark gleichmßige Konsistenzrate von v-n(x) unter allgemeinen Bedingungen. Unter Zuhilfenahme von Extremwerttheorie und starken Approximationen der empirischen Prozesse betrachten wir die asymptotischen maximalen Abweichungen sup06x61 |v_n(x) − v(x)|. Nach Vorbild der asymptotischen Theorie konstruieren wir simultane Konfidenzb änder um die geschätzte Expektilfunktion. Wir entwickeln einen funktionalen Datenanalyseansatz um eine Familie von generalisierten Quantilregressionen gemeinsam zu schätzen. Dabei gehen wir in unserem Ansatz davon aus, dass die generalisierten Quantile einige gemeinsame Merkmale teilen, welche durch eine geringe Anzahl von Hauptkomponenten zusammengefasst werden können. Die Hauptkomponenten sind als Splinefunktionen modelliert und werden durch Minimierung eines penalisierten asymmetrischen Verlustmaßes gesch¨atzt. Zur Berechnung wird ein iterativ gewichteter Kleinste-Quadrate-Algorithmus entwickelt. Während die separate Schätzung von individuell generalisierten Quantilregressionen normalerweise unter großer Variablit¨at durch fehlende Daten leidet, verbessert unser Ansatz der gemeinsamen Schätzung die Effizienz signifikant. Dies haben wir in einer Simulationsstudie demonstriert. Unsere vorgeschlagene Methode haben wir auf einen Datensatz von 150 Wetterstationen in China angewendet, um die generalisierten Quantilkurven der Volatilität der Temperatur von diesen Stationen zu erhaltenGeneralized quantile regressions, including the conditional quantiles and expectiles as special cases, are useful alternatives to the conditional means for characterizing a conditional distribution, especially when the interest lies in the tails. We denote $v_n(x)$ as the kernel smoothing estimator of the expectile curves. We prove the strong uniform consistency rate of $v_{n}(x)$ under general conditions. Moreover, using strong approximations of the empirical process and extreme value theory, we consider the asymptotic maximal deviation $\sup_{ 0 \leqslant x \leqslant 1 }|v_n(x)-v(x)|$. According to the asymptotic theory, we construct simultaneous confidence bands around the estimated expectile function. We develop a functional data analysis approach to jointly estimate a family of generalized quantile regressions. Our approach assumes that the generalized quantiles share some common features that can be summarized by a small number of principal components functions. The principal components are modeled as spline functions and are estimated by minimizing a penalized asymmetric loss measure. An iteratively reweighted least squares algorithm is developed for computation. While separate estimation of individual generalized quantile regressions usually suffers from large variability due to lack of sufficient data, by borrowing strength across data sets, our joint estimation approach significantly improves the estimation efficiency, which is demonstrated in a simulation study. The proposed method is applied to data from 150 weather stations in China to obtain the generalized quantile curves of the volatility of the temperature at these station

Running with a Mask? The Effect of Air Pollution on Marathon Runners’ Performance

Using a sample of over 0.3 million marathon runners in 37 cities and 55 races in China in 2014 and 2015, we estimate the air pollution elasticity of finish time to be 0.041. Our causal identification comes from the exogeneity of air pollution on the race day because runners are required to register a race a few months in advance and we control for city fixed effects, seasonal effects, and weather condition on the race day. Including individual fixed effects also provides consistent evidence. Our study contributes to the emerging literature on the effect of air pollution on short-run productivity, particularly on the performance of athletes engaging outdoor sports and other workers whose jobs require intensive physical activities

Running with a Mask? The Effect of Air Pollution on Marathon Runners’ Performance

Using a sample of over 0.3 million marathon runners in 37 cities and 55 races in China in 2014 and 2015, we estimate the air pollution elasticity of finish time to be 0.041. Our causal identification comes from the exogeneity of air pollution on the race day because runners are required to register a race a few months in advance and we control for city fixed effects, seasonal effects, and weather condition on the race day. Including individual fixed effects also provides consistent evidence. Our study contributes to the emerging literature on the effect of air pollution on short-run productivity, particularly on the performance of athletes engaging outdoor sports and other workers whose jobs require intensive physical activities

Techno-invasion and job satisfaction in China: The roles of boundary preference for segmentation and marital status

BACKGROUND: While the intensive work-related use of information and communication technologies after working hours have led to increased techno-invasion, much less is known regarding whether and for whom techno-invasion influences job satisfaction. OBJECTIVE: Drawing on the conservation of resources theory and person-environment fit theory, this study examined the relationship between techno-invasion and decreased job satisfaction. Specific attention was paid to the moderating effect of boundary preference for segmentation and its joint influence with marital status on this relationship. METHODS: Questionnaire data were collected by an online survey of a nationwide and diverse sample of 472 employees from China. Data were analyzed using descriptive statistics, confirmatory factor analysis and hierarchical regression analysis. RESULTS: We found that techno-invasion negatively correlated with job satisfaction, which was strengthened by boundary preference for segmentation. Furthermore, the results of a three-way interaction effect suggested that the moderating role of boundary preference for segmentation on the relationship between techno-invasion and job satisfaction is stronger for unmarried employees than it is for married ones. CONCLUSIONS: The effect of techno-invasion on employees’ job satisfaction can be strengthened or weakened by their boundary preference for segmentation and marital status

Detection and Genetic Analysis of Porcine Bocavirus

Porcine Bocavirus (PBoV) has been reported to be associated with postweaning multisystemic wasting syndrome and pneumonia in pigs. In this study, a survey was conducted to evaluate the prevalence of PBoV in slaughter pigs, sick pigs, asymptomatic pigs and classical swine fever virus (CSFV) eradication plan herds in five provinces of China (Henan, Liaoning, Shandong, Hebei and Tianjin) by means of PCR targeting NS1 gene of PBoV. Among the total of 403 tissue samples, 11.41% were positive for PBoV. The positive rates of spleen (20.75%) and inguinal lymph node (27.18%) are higher than those of other organs. PCR products of twenty PBoV positive samples from slaughter pigs were sequenced for phylogenetic analysis. The result revealed that PBoV could be divided into 6 groups (PBoV-a~PBoV-f). All PBoV sequenced in this study belong to PBoV-a–PBoV-d with 90.1% to 99% nucleotide identities. Our results exhibited significant genetic diversity of PBoV and suggested a complex prevalence of PBoV in Chinese swine herds. Whether this diversity of PBoV has a significance to pig production or even public health remains to be further studied

Business groups and corporate social responsibility: Evidence from China

This study investigates the impact of firms' business group affiliations on their performance in corporate social responsibility (CSR) in the context of China. We find that firms with a dual-status of simultaneously being a business group member and a state-owned enterprise (SOE) have weaker CSR performance. Our finding is consistent with the view that CSR engagement is a strategy for firms to pursue political legitimacy from the government and seek legitimacy in general from the public. The business group affiliation and the SOE identity together afford legitimacy to the firm and reduce its need to conduct CSR activities

Boosting Few-shot Action Recognition with Graph-guided Hybrid Matching

Class prototype construction and matching are core aspects of few-shot action recognition. Previous methods mainly focus on designing spatiotemporal relation modeling modules or complex temporal alignment algorithms. Despite the promising results, they ignored the value of class prototype construction and matching, leading to unsatisfactory performance in recognizing similar categories in every task. In this paper, we propose GgHM, a new framework with Graph-guided Hybrid Matching. Concretely, we learn task-oriented features by the guidance of a graph neural network during class prototype construction, optimizing the intra- and inter-class feature correlation explicitly. Next, we design a hybrid matching strategy, combining frame-level and tuple-level matching to classify videos with multivariate styles. We additionally propose a learnable dense temporal modeling module to enhance the video feature temporal representation to build a more solid foundation for the matching process. GgHM shows consistent improvements over other challenging baselines on several few-shot datasets, demonstrating the effectiveness of our method. The code will be publicly available at https://github.com/jiazheng-xing/GgHM.Comment: Accepted by ICCV202