THE HOMEOMORPHIC PROPERTY OF THE STOCHASTIC FLOW GENERATED BYTHE ONE-DEFAULT MODEL IN ONE DIMENSIONAL CASE

In this paper, we will try to study the same result proved in \cite{10}. So, on the same model and with some assumptions, we will study the property of homeomorphism of the stochastic flow generated by the natural model in a one-dimensional case and with some modifications, based on an important theory of Hiroshi Kunita. This is the main motivation of our research.

The evaluation of land surface parameters for model to derive land surface fluxes in the Tibetan Plateau

<p>Firstly, based on the difference of model and in-situ observations, a serious of sensitive experiments were done by using WRF. In order to use remote sensing products, a land-atmosphere model was initialized by ingesting land surface parameters, such as AMSR-E RS products, and the results were compared with the default model configuration and with in-situ long-term CAMP/Tibet observations.</p><p>Secondly, a land-atmosphere model was initialized by ingesting AMSR-E products, and the results were compared with the default model configuration and with in-situ long-term CAMP/Tibet observations. The differences between the AMSR-E initialized model runs with the default model configuration and in situ data showed an apparent inconsistency in the model-simulated land surface heat fluxes. The results showed that the soil moisture was sensitive to the specific model configuration. To evaluate and verify the model stability, a long-term modeling study with AMSR-E soil moisture data ingestion was performed. Based on test simulations, AMSR-E data were assimilated into an atmospheric model for July and August 2007. The results showed that the land surface fluxes agreed well with both the in-situ data and the results of the default model configuration. Therefore, the simulation can be used to retrieve land surface heat fluxes from an atmospheric model over the Tibetan Plateau.</p><p>All of the different methods will clarify the land surface heating field in complex plateau, it also can affect atmospheric cycle over the Tibetan Plateau even all of the global atmospheric cycle pattern.</p>

Sovereign Debt Standstills

As a response to economic crises triggered by COVID-19, sovereign debt standstill proposals emphasize debt payment suspensions without haircuts on the face value of debt obligations. We quantify the effects of standstills using a standard default model. We find that a one-year standstill generates welfare gains for the sovereign equivalent to a permanent consumption increase of between 0.1% and 0.3%, depending on the initial shock. However, except when it avoids a default, the standstill also implies capital losses for creditors of between 9% and 27%, which is consistent with their reluctance to participate in these operations and indicates that this reluctance would persist even without a free-riding or holdout problem. Standstills also generate a form of “debt overhang” and thus the opportunity for a “voluntary debt exchange”: complementing the standstill with haircuts could reduce creditors’ losses and simultaneously increase welfare gains. Our results cast doubts on the emphasis on standstills without haircuts.

Necessary conditions for algorithmic tuning of weather prediction models using OpenIFS as an example

Algorithmic model tuning is a promising approach to yield the best possible forecast performance of multi-scale multi-phase atmospheric models once the model structure is fixed. The problem is to what degree we can trust algorithmic model tuning. We approach the problem by studying the convergence of this process in a semi-realistic case. Let M(x, θ) denote the time evolution model, where x and θ are the initial state and the default model parameter vectors, respectively. A necessary condition for an algorithmic tuning process to converge is that θ is recovered when the tuning process is initialised with perturbed model parameters θ′ and the default model forecasts are used as pseudo-observations. The aim here is to gauge which conditions are sufficient in a semi-realistic test setting to obtain reliable results and thus build confidence on the tuning in fully realistic cases. A large set of convergence tests is carried in semi-realistic cases by applying two different ensemble-based parameter estimation methods and the atmospheric forecast model of the Integrated Forecasting System (OpenIFS) model. The results are interpreted as general guidance for algorithmic model tuning, which we successfully tested in a more demanding case of simultaneous estimation of eight OpenIFS model parameters.

State dependent correlations in the Vasicek default model

This paper incorporates state dependent correlations (those that vary systematically with the state of the economy) into the Vasicek default model. Other approaches to randomizing correlation in the Vasicek model have either assumed that correlation is independent of the systematic risk factor (zero state dependence) or is an explicit function of the systematic risk factor (perfect state dependence). By contrast, our approach allows for an arbitrary degree of state dependence and includes both zero and perfect state dependence as special cases. This is accomplished by expressing the factor loading as a function of an auxiliary (Gaussian) variable that is correlated with the systematic risk factor. Using Federal Reserve data on delinquency rates we use maximum likelihood to estimate the parameters of the model, and find the empirical degree of state dependence to be quite high (but generally not perfect). We also find that randomizing correlation, without allowing for state dependence, does not improve the empirical performance of the Vasicek model.

Constrained Efficient Borrowing with Sovereign Default Risk

Using a quantitative sovereign default model, we characterize constrained efficient borrowing by a Ramsey government that commits to income-history-contingent borrowing paths taking as given ex-post optimal future default decisions. The Ramsey government improves upon the Markov government because it internalizes the effects of borrowing decisions in period t on borrowing opportunities prior to t. We show the effect of borrowing decisions in t on utility flows prior to t can be encapsulated by two single dimensional variables. Relative to a Markov government, the Ramsey government distorts borrowing decisions more when bond prices are more sensitive to borrowing, and changes in bond prices have a larger effect on past utility. In a quantitative exercise, more than 80% of the default risk is eliminated by a Ramsey government, without decreasing borrowing. The Ramsey government also has a higher probability of completing a successful deleveraging (without defaulting), while smoothing out the fiscal consolidation.

Natural Disasters, Climate Change, and Sovereign Risk

I investigate how natural disasters can exacerbate fiscal vulnerabilities and trigger sovereign defaults. I extend a standard sovereign default model to include disaster risk and calibrate it to a sample of seven Caribbean countries that are frequently hit by hurricanes. I find that disaster risk reduces government's ability to issue debt and that climate change further restricts government's access to financial markets. Next, I show that "disaster clauses", that provide debt-servicing relief, allow governments to borrow more and preserve government's access to financial markets, amid rising risk of disasters. Yet, debt limits may need to be adopted to avoid overborrowing and a decline of welfare.

Second-guessing of Spinoza: Psychophysiological and behavioral evidence that believing is default during proposition comprehension

The belief-default model contends that believing is inexorable during comprehension, and falsification is a subsequent, secondary process. By contrast, the Cartesian belief-fixation model argues that naïve propositions may be mentally represented without a truth or falsity stance. In the present research, data from four studies help adjudicate belief-fixation models, favoring the belief-default model: Studies 1-3 show that newly represented propositions are initially believed as the consequences of the truth from a naïve represented proposition will automatically activate contradictory mental information even when this processing impairs task performance (a “false” false alarm belief bias). Naïve propositions cannot be “merely” represented (without a truth stance) during comprehension. Studies 3 and 4 reveal unique electrodermal activity signals corresponding to propositions considered to be either true or false. We argue that the observed autonomic reactivity constitutes the source of two different epistemic emotions associated with the perceived outcomes of a memory search (i.e., “aha” and wrongness, respectively). To account for the psychophysiological results, we hypothesize that the epistemic emotion of familiarity is substantiated by an “aha” emotion which signals the recovery of represented propositions considered true during mnemonic processing. In addition, we show that anti-belief-default conclusions from recent investigations using multinomial processing tree modeling are tenuous as they depend on the type of false information paradigm employed. In sum, the data support the belief-default model and indicate a novel psychophysiological method to distinguish “believed” memory retrieval products from “guessed” responses derived via metacognitive strategies during veridical identification.