The development and spread of financial innovations
金融创新的发展与扩散AI 解读 ↓
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金融创新的发展与扩散AI 解读 ↓
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I study financial product innovation in a model with two classes of agents: “sophisticated” and “unsophisticated.” Unsophisticated agents are hit with frictions that lower the return to a conventional asset they hold. Sophisticated agents construct financial innovations that are perfect substitutes for the conventional asset, but are not subject to the friction. In the absence of complete information, unsophisticated agents learn about innovations through a contagion process, as they encounter competitors who have already adopted them. The model yields two equilibria: in one, the innovation persists; in the other, it disappears. Only one equilibrium is stable, and this is determined by the strength of the contagion and by early strategic interactions between sophisticated agents. The model suggests mechanisms for several empirical regularities in the financial innovation literature. Additionally, two applications demonstrate how to estimate the contagion parameter with a short time series of data, and how to use it to predict whether a financial innovation will spread.
In this paper, we develop bootstrap methods for factor-augmented regressions with potentially weak common components, allowing signal eigenvalues to diverge at heterogeneous rates $N^{α_k}$, where $0<α_k\leq1$, $k=1,\ldots,r$. The regression estimator targets a rotation of the latent parameter vector that may be data dependent, while factor estimation can induce asymptotic bias. The proposed procedure directly constructs the rotation-specific target in the bootstrap world,…
Normality pre-tests on reduced-form innovations cannot detect whether two or more underlying structural shocks are Gaussian, and hence cannot detect a failure of identification in non-Gaussian SVARs. We propose a bootstrap-based approach that reveals such a failure. Under the null of valid identification and certain regularity conditions, the conditional bootstrap distribution of the maximum likelihood estimator of the impact matrix is asymptotically normal, so the…
基于广义逆距离权重的GNAR模型改进多元时间序列预测
Many multivariate time series arise on networks where relationships between variables depend on physical or more general distances. Standard vector autoregressive models struggle in such settings due to their quadratic parameter growth, while generalised network autoregressive (GNAR) models offer a parsimonious alternative by explicitly incorporating network structures. Existing GNAR formulations assume fixed weights, typically uniform or reciprocal distances, which may not…
多元Lévy自适应B样条回归
We propose Multivariate Lévy Adaptive B-Spline Regression (MLABS), a Bayesian nonparametric regression model designed to capture nonlinear effects, interactions, and locally varying features of multivariate functions. MLABS represents the regression function as a random sum of low-order tensor-product B-spline basis functions under a finite Lévy random measure prior. The resulting representation adapts to the complexity and local structure of the regression function while…
Joint-event forecasts combine an estimated dependence model with current marginal forecasts. Retaining historical transforms at the marginal fits available when each outcome arrived creates overlapping estimation errors. We derive their joint influence with the terminal estimates in a stable Vine Copula VAR with normal innovation margins and a fixed, correctly specified Gaussian or positive Clayton vine. An intercept identity and the stable VAR filter reduce the historical…
Wigner 矩阵时间序列的相变监测
We develop methodology and theory for the detection of a phase transition in a time-series of high-dimensional random matrices. In the model we study, at each time point \( t = 1,2,\ldots \), we observe a deformed Wigner matrix \( \mathbf{M}_t \), where the unobservable deformation represents a latent signal. This signal is detectable only in the supercritical regime, and our objective is to detect the transition to this regime in real time, as new matrix--valued…