Asset Pricing with Observable Stochastic Discount Factors

The stochastic discount factor model provides a general framework for pricing assets. By specifying the discount factor suitably it encompasses most of the theories currently in use, including CAPM and consumption CAPM. The SDF model has been based on the use of single and multiple factors, and on latent and observed factors. In most situations, and especially for the term structure, single factor models are inappropriate, whilst latent variables require the somewhat arbitrary specification of generating processes and are difficult to interpret. In this paper we survey the principal different implementations of the SDF model for bonds, equity and FOREX and propose a new approach. This is based on the use of multiple factors that are observable and modelling the joint distribution of excess returns and the factors using a multi–variate GARCH–in–mean process. We argue that in general single equation and VAR models, although widely used in empirical finance, are inappropriate as they do not satisfy the no–arbitrage condition. Since risk premia arise from conditional covariation between the returns and the factors, both a multi–variate context and having conditional covariances in the conditional mean process, is essential. We explain how apparent exceptions, such as the CIR and Vasicek models, in fact meet this requirement — but at a price. We explain our new approach, discuss how it might be implemented and present some empirical evidence, mainly from our own researches. Partly, to enable comparisons to be made, the survey also includes evidence from recent empirical work using more traditional approaches.     

九江两次可观测固态降水过程总结

通过分析常规实况资料以及EC-thin预报模式对比分析,对2018年12月7日20时至9日08时和2019年2月23日08时至14时发生在九江两次可观测降雪的成因进行对比分析,得出在降水充足的条件下,500hPa温度场成雪条件充足,中低层及地面温度不能使得固态降水完全融化时,到地面就形成可观测固态降水。     

Variables — Observable,objective, measurable and constructed

Quality & Quantity -     

Guessing the beliefs

A decision maker facing Knightian uncertainty is about to tell if he prefers an act X or Y. Two agents try to guess what he is going to do. All of them have preferences that achieve a separation of utility from beliefs. The only thing that the two agents do not know is the beliefs, so they evaluate them. We give a definition of “guessing better” and deal with its implications. We study particular cases as subjective expected utility and Choquet expected utility.     

Updating Choquet beliefs

We apply Pires’s coherence property between unconditional and conditional preferences that admit a CEU representation. In conjunction with consequentialism (only those outcomes on states which are still possible can matter for conditional preference) this implies that the conditional preference may be obtained from the unconditional preference by taking the Full Bayesian Update of the capacity.     

Observable implications of Nash and subgame-perfect behavior in extensive games

We provide necessary and sufficient conditions for observed outcomes in extensive game forms, in which preferences are unobserved, to be rationalized first, weakly, as a Nash equilibrium and then as the unique subgame-perfect equilibrium. Thus, one could use these conditions to find that play is (a) consistent with subgame-perfect equilibrium, or (b) not consistent with subgame-perfect behavior but is consistent with Nash equilibrium, or (c) consistent with neither.     

Parameter-efficient deep probabilistic forecasting

Probabilistic time series forecasting is crucial in many application domains, such as retail, ecommerce, finance, and biology. With the increasing availability of large volumes of data, a number of neural architectures have been proposed for this problem. In particular, Transformer-based methods achieve state-of-the-art performance on real-world benchmarks. However, these methods require a large number of parameters to be learned, which imposes high memory requirements on the computational resources for training such models. To address this problem, we introduce a novel bidirectional temporal convolutional network that requires an order of magnitude fewer parameters than a common Transformer-based approach. Our model combines two temporal convolutional networks: the first network encodes future covariates of the time series, whereas the second network encodes past observations and covariates. We jointly estimate the parameters of an output distribution via these two networks. Experiments on four real-world datasets show that our method performs on par with four state-of-the-art probabilistic forecasting methods, including a Transformer-based approach and WaveNet, on two point metrics (sMAPE and NRMSE) as well as on a set of range metrics (quantile loss percentiles) in the majority of cases. We also demonstrate that our method requires significantly fewer parameters than Transformer-based methods, which means that the model can be trained faster with significantly lower memory requirements, which as a consequence reduces the infrastructure cost for deploying these models.     

Bayesian incentive compatible beliefs

The problem of incentives for correct revelation is studied as a game with incomplete information where players have individual beliefs concerning other's types. General conditions on the beliefs are given which are shown to be sufficient for the existence of a Pareto-efficient mechanism for which truth-telling is a Bayesian equilibrium.     

Demand substitution across US cities: Observable similarity and home price correlation

This paper studies demand substitution in the context of US cities. Demand substitution occurs when individuals on the margin between certain city pairs affect demand patterns in the aggregate, causing certain cities to be better substitutes than others. Using a discrete model of city choice, I derive two predictions for migration flows and test them empirically using city-to-city migration data from the US Census. I show that cities which are similar on a variety of observable measures have higher levels of gross migration flows in the steady state and higher net migration flows in response to labor demand shocks. Finally, I propose pairwise correlation in metropolitan home prices as a price-based measure of substitutability and show that it contains substantial predictive power for migration flows relative to observable similarity.     

Previous beliefs and continuance intention

This work analyses the role of previous beliefs and attitudes on the intention to continue using online platforms. Although the literature has ignored the role of those variables in determining the continuance usage, this work makes use of the mediated relationships to verify its relevance. The conclusion is that, although previous attitudes do not have an impact on the continuance usage, the previous beliefs about the effort expectation, the perceived usefulness and trust do have an impact on the continuance intention to use an e-learning platform.     

Reexamination on updating Choquet beliefs

Eichberger et al. (2007)  characterize the full Bayesian update rule for capacities. This paper shows that a conditional preference relation represented by the Choquet expected utility with respect to the updated capacity through the rule does not satisfy the axiom of Conditional Certainty Equivalence Consistency. A counterexample is provided and it is proved that a relaxation of the axiom maintains their results.     

Frequent probabilistic punishment in law enforcement

Timing and frequency of punishment are critical elements in law enforcement. Previous studies suggest the superiority of immediate punishment schemes over delayed punishment, as well as the importance of frequent punishment. Yet law enforcement schemes which utilize both frequent and immediate punishment are often cost prohibitive. In this work, we propose the “bad lottery immediate punishment” as an effective substitute to immediate punishment. This is a punishment mechanism that signals immediately to an offender that his violation has been spotted, but the actual penalty is delayed and probabilistic. We discuss implications in law enforcement, where probabilistic punishment is potentially more cost effective. Received: May 2000 / Acccepted: December 2000     

Consistency in the probabilistic assignment model

This paper introduces a notion of consistency for the probabilistic assignment model, which we call probabilistic consistency. We show that the axioms equal treatment of equals and probabilistic consistency characterize the uniform rule, which is the rule which randomizes uniformly over all possible assignments.     

Evolution of heterogeneous beliefs and asset overvaluation

I analyze a model in which different agents have different non-rational expectations about the future price and cash flows of a risky asset. The beliefs in the society evolve according to a very general class of evolution functions that are monotone; that is if one type has increased its share in the population then all types with higher profit should also have increased their shares. I show that the price of the risky asset converges to the risk-neutral fundamental price even though all agents in the economy are risk-averse. The risky asset thus becomes overvalued as compared to the equilibrium with rational expectations. The overvaluation is a result of the evolution of beliefs and does not rely on such asymmetric assumptions as short-sale constraints or optimistic bias.     

Manipulation of voting schemes with restricted beliefs

In this paper we define manipulation with restricted beliefs as the possibility for some voter to have an insincere preference ordering that dominates the sincere one within the given individual beliefs over other agents’ preferences. We then show that all non-dictatorial voting schemes are manipulable in this sense, up to a given threshold.     

Similarity of differential information with subjective prior beliefs

We present a complete, separable and metrizable topology on the product space of information and (subjective) beliefs. Such a topology formalizes similarity of differential information without the assumption of a common prior, but under the assumption that objectively impossible events are considered impossible by subjective beliefs. As an application to the theory of the consumer, we provide results on the continuity of expected utility and demand functions. We also provide continuity results for the value of information and the insurance premium as defined in the literature.