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- High-frequency data (2)
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- Bootstrap; High-frequency data; Jumps; Regression; Semimartingale; Specification test; Stochastic volatility (1)
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Articles 1 - 13 of 13
Full-Text Articles in Social and Behavioral Sciences
Random Mechanism Design On Multidimensional Domains, Shurojit Chatterji, Huaxia Zeng
Random Mechanism Design On Multidimensional Domains, Shurojit Chatterji, Huaxia Zeng
Research Collection School Of Economics
We study random mechanism design in an environment where the set of alternatives has a Cartesian product structure. We first show that all generalized random dictatorships are strategy-proof on a minimally rich domain if and only if the domain is a top-separable domain. We next generalize the notion of connectedness (Monjardet, 2009) to establish a particular class of top-separable domains: connected domains, and show that in the class of minimally rich and connected domains, the multidimensional single-peakedness restriction is necessary and sufficient for the design of a flexible random social choice function that is unanimous and strategy-proof. Such a flexible …
Preferences With Changing Ambiguity Aversion, Jingyi Xue
Preferences With Changing Ambiguity Aversion, Jingyi Xue
Research Collection School Of Economics
This paper provides two equivalent representations for thegeneral class of uncertainty averse preferences studied by Cerreia-Vioglio,Maccheroni, Marinacci and Montrucchio (2011). The two representations employrespectively two important extensions of Gilboa and Schmeidler (1989)’s maxmindecision rule. The first is a weighted maxmin representation with anon-constant weight used in mixing the minimum and maximum expected utilities.The second is a variant constraint representation which evaluates a prospect bythe worst expected utility over a neighborhood of approximating priors wherethe size of the neighborhood depends on the prospect. The equivalent representationshave advantage in several respects. In the second part of this paper, we studythe wealth effect …
Fair Division With Uncertain Needs, Jingyi Xue
Fair Division With Uncertain Needs, Jingyi Xue
Research Collection School Of Economics
Imagine that agents have uncertain needs and a resource must be divided before uncertainty resolves. In this situation, waste typically occurs when the assignment to an agent turns out to exceed his realized need. How should the resource be divided in the face of possible waste? This is a question out of the scope of the existing rationing literature. Our main axiom to address the issue is no domination. It requires that no agent receive more of the resource than another while producing a larger expected waste, unless the other agent has been fully compensated. Together with conditional strict endowment …
Favorite-Longshot Bias In Pari-Mutuel Betting: An Evolutionary Explanation, Atsushi Kajii, Takahiro Watanabe
Favorite-Longshot Bias In Pari-Mutuel Betting: An Evolutionary Explanation, Atsushi Kajii, Takahiro Watanabe
Research Collection School Of Economics
Favorite-longshot bias (FLB) refers to an observed tendency whereby “longshots” are overvalued and favorites are undervalued. We offer an evolutionary explanation for FLB in pari-mutuel betting using a simple market model. A bettor is forced to quit with some probability if his total net gain in one day is negative. Because of a positive track take, the expected returns of any strategy are negative, and so every agent must eventually lose and disappear in the long run. Those who favor longshots have a better chance of getting ahead with rare but large gains, enabling them to survive for longer than …
Every Random Choice Rule Is Backwards-Induction Rationalizable, Jiangtao Li, Rui Tang
Every Random Choice Rule Is Backwards-Induction Rationalizable, Jiangtao Li, Rui Tang
Research Collection School Of Economics
Motivated by the literature on random choice and in particular the random utility models, we extend the analysis in Bossert and Sprumont (2013) to include the possibility that players exhibit stochastic preferences over alternatives. We prove that every random choice rule is backwards-induction rationalizable.
Robust Jump Regressions, Jia Li, Viktor Todorov, George Tauchen
Robust Jump Regressions, Jia Li, Viktor Todorov, George Tauchen
Research Collection School Of Economics
We develop robust inference methods for studying linear dependence between the jumps of discretely observed processes at high frequency. Unlike classical linear regressions, jump regressions are determined by a small number of jumps occurring over a fixed time interval and the rest of the components of the processes around the jump times. The latter are the continuous martingale parts of the processes as well as observation noise. By sampling more frequently the role of these components, which are hidden in the observed price, shrinks asymptotically. The robustness of our inference procedure is with respect to outliers, which are of particular …
How To Describe Objects?, Liu Peng
How To Describe Objects?, Liu Peng
Research Collection School Of Economics
This paper addresses the problem of randomly allocating n indivisible objects to n agents where each object can be evaluated according to a set of characteristics. The planner chooses a subset of characteristics and a ranking of them. Then she describes each object as a list of values according to the ranking of those chosen characteristics. Being informed of such a description, each agent figures out her preference that is lexicographically separable according to the characteristics chosen and ranked by the planner. Hence a description of the objects induces a collection of admissible preferences. We call a description good if …
Reduced Forms And Weak Instrumentation, Peter C. B. Phillips
Reduced Forms And Weak Instrumentation, Peter C. B. Phillips
Research Collection School Of Economics
This paper develops exact finite sample and asymptotic distributions for a class of reduced form estimators and predictors, allowing for the presence of unidentified or weakly identified structural equations. Weak instrument asymptotic theory is developed directly from finite sample results, unifying earlier findings and showing the usefulness of structural information in making predictions from reduced form systems in applications. Asymptotic results are reported for predictions from models with many weak instruments. Of particular interest is the finding that, in unidentified and weakly identified structural models, partially restricted reduced form predictors have considerably smaller forecast mean square errors than unrestricted reduced …
Random Assignments On Preference Domains With A Tier Structure, Peng Liu, Huaxia Zeng
Random Assignments On Preference Domains With A Tier Structure, Peng Liu, Huaxia Zeng
Research Collection School Of Economics
We address a standard random assignment problem (Bogomolnaia and Moulin (2001)) and search forsd-strategy-proof, sd-efficient and sd-envy-free or equal-treatment-of-equals rules.Our main result is that on a connected domain (Sato (2013)),if there exists a rule satisfying these axioms, this domain is endowed with a restricted tier structure.Furthermore, we show that, on such a domain, the Probabilistic Serial rule is the unique rulethat satisfies these axioms.As an extension, we introduce outside options to the model, and establish the same characterizations.
Strategy-Proofness Of The Probabilistic Serial Rule On Sequentially Dichotomous Domains, Peng Liu
Strategy-Proofness Of The Probabilistic Serial Rule On Sequentially Dichotomous Domains, Peng Liu
Research Collection School Of Economics
A class of preference domains is proposed: sequentially dichotomous domains. On any sequentially dichotomous domain, the probabilistic serial rule (Bogomolnaia and Moulin (2001)) is sd-strategy-proof. In addition, any sequentially dichotomous domain is maximal for the probabilistic serial rule to be sd-strategy-proof.
Jump Regressions, Jia Li, Viktor Todorov, George Tauchen
Jump Regressions, Jia Li, Viktor Todorov, George Tauchen
Research Collection School Of Economics
We develop econometric tools for studying jump dependence of two processes from high-frequency observations on a fixed time interval. In this context, only segments of data around a few outlying observations are informative for the inference. We derive an asymptotically valid test for stability of a linear jump relation over regions of the jump size domain. The test has power against general forms of nonlinearity in the jump dependence as well as temporal instabilities. We further propose an efficient estimator for the linear jump regression model that is formed by optimally weighting the detected jumps with weights based on the …
Mixed-Scale Jump Regressions With Bootstrap Inference, Jia Li, Viktor Todorov, George Tauchen
Mixed-Scale Jump Regressions With Bootstrap Inference, Jia Li, Viktor Todorov, George Tauchen
Research Collection School Of Economics
We develop an efficient mixed-scale estimator for jump regressions using high-frequency asset returns. A fine time scale is used to accurately identify the locations of large rare jumps in the explanatory variables such as the price of the market portfolio. A coarse scale is then used in the estimation in order to attenuate the effect of trading frictions in the dependent variable such as the prices of potentially less liquid assets. The proposed estimator has a non-standard asymptotic distribution that cannot be made asymptotically pivotal via studentization. We propose a novel bootstrap procedure for feasible inference and justify its asymptotic …
On Estimating Market Microstructure Noise Variance, Yingjie Dong, Yiu Kuen Tse
On Estimating Market Microstructure Noise Variance, Yingjie Dong, Yiu Kuen Tse
Research Collection School Of Economics
We study the market microstructure noise-variance estimation of high-frequency stock prices. Based on the Hansen and Lunde (2006) approach, we propose estimates using subsampling method at different time scales. We conduct a Monte Carlo study to compare our method against others in the literature. Our results show that our proposed estimates have lower (absolute) mean error and root mean-squared error, and their performance is quite stable at different time scales.