4,909 research outputs found
Testing for Multiple Bubbles 1: Historical Episodes of Exuberance and Collapse in the S&P 500
Published in International Economic Review, https://doi.org/10.1111/iere.12132</p
Testing for Multiple Bubbles 2: Limit Theory of Real Time Detectors
Singapore MOE Academic Research Tier 2Published in International Economic Review, https://doi.org/10.1111/iere.12131</p
Testing for Multiple Bubbles
Identifying and dating explosive bubbles when there is periodically collapsing behavior over time has been a major concern in the economics literature and is of great importance for practitioners. The complexity of the nonlinear structure inherent in multiple bubble phenomena within the same sample period makes econometric analysis particularly difficult. The present paper develops new recursive procedures for practical implementation and surveillance strategies that may be employed by central banks and fiscal regulators. We show how the testing procedure and dating algorithm of Phillips, Wu and Yu (2011, PWY) are affected by multiple bubbles and may fail to be consistent. The present paper proposes a generalized version of the sup ADF test of PWY to address this difficulty, derives its asymptotic distribution, introduces a new date-stamping strategy for the origination and termination of multiple bubbles, and proves consistency of this dating procedure. Simulations show that the test significantly improves discriminatory power and leads to distinct power gains when multiple bubbles occur. Empirical applications are conducted to S&P 500 stock market data over a long historical period from January 1871 to December 2010. The new approach identifies many key historical episodes of exuberance and collapse over this period, whereas the strategy of PWY and the CUSUM procedure locate far fewer episodes in the same sample range.Date-stamping strategy, Generalized sup ADF test, Multiple bubbles, Rational bubble, Periodically collapsing bubbles, Sup ADF test
Specification Sensitivity in Right-Tailed Unit Root Testing for Explosive Behavior
Right-tailed unit root tests have proved promising for detecting exuberance in economic and financial activities. Like left-tailed tests, the limit theory and test performance are sensitive to the null hypothesis and the model specification used in parameter estimation. This paper aims to provide some empirical guidelines for the practical implementation of right-tailed unit root tests, focussing on the sup ADF test of Phillips, Wu and Yu (2011), which implements a right-tailed ADF test repeatedly on a sequence of forward sample recursions. We analyze and compare the limit theory of the sup ADF test under different hypotheses and model specifications. The size and power properties of the test under various scenarios are examined in simulations and some recommendations for empirical practice are given. Empirical applications to the Nasdaq and to Australian and New Zealand housing data illustrate these specification issues and reveal their practical importance in testing.Unit root test, Mildly explosive process, Recursive regression, Size and power
ODN: Opening the Deep Network for Open-set Action Recognition
In recent years, the performance of action recognition has been significantly
improved with the help of deep neural networks. Most of the existing action
recognition works hold the \textit{closed-set} assumption that all action
categories are known beforehand while deep networks can be well trained for
these categories. However, action recognition in the real world is essentially
an \textit{open-set} problem, namely, it is impossible to know all action
categories beforehand and consequently infeasible to prepare sufficient
training samples for those emerging categories. In this case, applying
closed-set recognition methods will definitely lead to unseen-category errors.
To address this challenge, we propose the Open Deep Network (ODN) for the
open-set action recognition task. Technologically, ODN detects new categories
by applying a multi-class triplet thresholding method, and then dynamically
reconstructs the classification layer and "opens" the deep network by adding
predictors for new categories continually. In order to transfer the learned
knowledge to the new category, two novel methods, Emphasis Initialization and
Allometry Training, are adopted to initialize and incrementally train the new
predictor so that only few samples are needed to fine-tune the model. Extensive
experiments show that ODN can effectively detect and recognize new categories
with little human intervention, thus applicable to the open-set action
recognition tasks in the real world. Moreover, ODN can even achieve comparable
performance to some closed-set methods.Comment: 6 pages, 3 figures, ICME 201
Breaking a novel colour image encryption algorithm based on chaos
Recently, a colour image encryption algorithm based on chaos was proposed by
cascading two position permutation operations and one substitution operation,
which are all determined by some pseudo-random number sequences generated by
iterating the Logistic map. This paper evaluates the security level of the
encryption algorithm and finds that the position permutation-only part and the
substitution part can be separately broken with only and 2 chosen plain-images, respectively, where is the size of the
plain-image. Concise theoretical analyses are provided to support the
chosen-plaintext attack, which are verified by experimental results also.Comment: 5 pages, 1 figur
Parton shower algorithm with saturation effect
We extend the previously developed small parton shower algorithm to
include the kinematic constraint effect and resummation effect. This work
enables the Monte Carlo generator to simultaneously resum large and small
logarithms in the saturation regime for the first time. It is an important
step towards simulating processes involving multiple well separated hard
scales, such as di-jet production in eA collisions at EIC.Comment: 12 pages, 4 figures. arXiv admin note: text overlap with
arXiv:2211.0717
Testing for Multiple Bubbles: Historical Episodes of Exuberance and Collapse in the S&P 500
Recent work on econometric detection mechanisms has shown the effectiveness of recursive procedures in identifying and dating financial bubbles. These procedures are useful as warning alerts in surveillance strategies conducted by central banks and fiscal regulators with real time data. Use of these methods over long historical periods presents a more serious econometric challenge due to the complexity of the nonlinear structure and break mechanisms that are inherent in multiple bubble phenomena within the same sample period. To meet this challenge the present paper develops a new recursive flexible window method that is better suited for practical implementation with long historical time series. The method is a generalized version of the sup ADF test of Phillips, Wu and Yu (2011, PWY) and delivers a consistent date-stamping strategy for the origination and termination of multiple bubbles. Simulations show that the test significantly improves discriminatory power and leads to distinct power gains when multiple bubbles occur. An empirical application of the methodology is conducted on S&P 500 stock market data over a long historical period from January 1871 to December 2010. The new approach successfully identifies the well-known historical episodes of exuberance and collapse over this period, whereas the strategy of PWY and a related CUSUM dating procedure locate far fewer episodes in the same sample range
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