This paper proposes a new jump test for semi-martingale contained by microstructure noise based on the threshold pre-averaging bi-power estimation. Theoretically, we prove that such test has asymptotical size and power. Monte Carlo simulations show that the new test has better performance than Christensen et al(2014)'s test in noisy setting and we also consider adopting the false discovery rate (FDR) threshold technique to avoid spurious detections. In the empirical part, we investigate the contributions of jumps to total return variance from the Chinese stock market based on the tick-by-tick transaction data. The empirical results imply that the jump variation is an order of magnitude smaller than typical estimates found in the existing literature from different perspectives.
The 𝑅2 statistic and its classic adjusted version, say 𝑅*2, tend to overestimate the multiple correlation coefficient when dealing with multivariate data that exhibit heavy tails and tail dependence. This can result in an incorrect significance of correlation in high-dimensional scenarios. A new adaptive adjustment to the 𝑅2 statistic is proposed in this paper, which applies to a general population model that covers the family of elliptical distributions and an independent components model. Consistency and asymptotic normality of the new statistic are established under this general model. These findings are then applied to some fundamental inference problems in high dimensions.
Abstract BackgroundThe implementation of low-carbon economy is the key to sustainable economy and society development, and low-carbon technological innovation of new energy enterprise is the core driving force based on the perspective of sustainable development.MethodThis study establishes the low-carbon technology innovation compound systems and evaluation index order systems of new energy enterprise, integrates synergy theory and genetic algorithms, and constructs the dynamic co-evolution model of low-carbon technology innovation compound systems of new energy enterprise, expounds the order and stability of the compound systems.ResultsBased on the statistical data of 12 representative enterprises in "top 30 new energy enterprises" from 2010 to 2019, this paper establishes and analyzes the corresponding coevolution model. It is found that the gap of low-carbon technology innovation level between different new energy enterprises is narrowing. Among them, the level of new energy comprehensive enterprises is slightly higher than that of other types of enterprises, and that of central enterprises is slightly higher than that of private enterprises.ConclusionThe results consistently show that the model can better reflect the dynamic co-evolution relationships of low-carbon technology innovation compound systems of new energy enterprise, and reveal the partial competitive substitution, partial competitive coexistence and completeness among the three subsystems of input, output and support in the compound systems in detail. The three situations of independent coexistence fully demonstrate the competition and cooperation relationships among subsystems and the stability of overall compound systems, which provide new research direction for low-carbon technology innovation research for new energy enterprise.
(1) Analytical methods for the Longladuo mafic intrusions; (2) Whole-rock compositions and Sr-Nd-Pb isotopic data for the Longladuo diabases; (3) Zircon LA-ICP-MS U-Pb isotopic data and Lu-Hf isotopic compositions for the Longladuo diabases ;(4) Geochemical plots for the Longladuo diabases.
Jumps and market microstructure noise are stylized features of high-frequency financial data. It is well known that they introduce bias in the estimation of volatility (including integrated and spot volatilities) of assets, and many methods have been proposed to deal with this problem. When the jumps are intensive with infinite variation, the efficient estimation of spot volatility under serially dependent noise is not available and is thus in need. For this purpose, we propose a novel estimator of spot volatility with a hybrid use of the pre-averaging technique and the empirical characteristic function. Under mild assumptions, the results of consistency and asymptotic normality of our estimator are established. Furthermore, we show that our estimator achieves an almost efficient convergence rate with optimal variance when the jumps are either less active or active with symmetric structure. Simulation studies verify our theoretical conclusions. We apply our proposed estimator to empirical analyses, such as estimating the weekly volatility curve using second-by-second transaction price data.
Risk-neutral valuation is used widely in derivatives pricing. It is shown in this paper, however, that the naive approach of simply setting the growth rate of the underlying security to risk-free interest rate, which happens to work for a geometric Brownian motion (GBM) process, fails to work when the underlying price follows the arithmetic Brownian motion (ABM). Therefore, the formal approach using a martingale measure should be used instead when the underlying process is not a GBM.
The pharmaceutical industry spends more funds on drug detailing than on any other marketing instrument. Similar to the efiect of advertising, the impact of detailing expenditures spills over beyond the current period. We expect forward-looking flrms to observe this carry-over efiect and adopt a dynamic approach to determine optimal detailing levels by maximizing their long-term proflts. We develop a structural model of dynamic oligopoly competition to analyze flrms’ scheduling of detailing over time and to estimate detailing costs consistent with such scheduling. The model is estimated using a recently developed two-stage method. In the flrst stage, physician-level demand is estimated in a hierarchical Bayesian framework. Further, using a semi-parametric approach, we estimate physician-level policy functions that describe each flrm’s observed detailing actions, also in a hierarchical Bayesian framework. In the second stage, costs of detailing are estimated assuming that the observed detailing data are consistent with a Markov perfect Nash equilibrium. Our estimated demand model shows evidence of supersaturation at high levels of detailing stock. The estimated policy functions show that the optimal detailing level decreases in own detailing stock. Further, flrms escalate detailing in their rivals’ detailing stock when their rivals have low detailing stock, but cut back efiort when their rivals have high detailing stock. Our estimates of detailing costs capture all economically relevant information from flrms’ decisions on detailing, including both decisions to detail and decisions not to detail. They are substantially larger than extant industry estimates which are only based on accounting information.
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