论文检索

股票与债券收益联合分布非线性动态的计量模型AI 解读

Abstract This paper considers a variety of econometric models for the joint distribution of US stock and bond returns in the presence of regime switching dynamics. While simple two‐ or three‐state models capture the univariate dynamics in bond and stock returns, a more complicated four‐state model with regimes characterized as crash, slow growth, bull and recovery states is required to capture their joint distribution. The transition probability matrix of this model has a…

GARCH均值模型中的人工回归检验AI 解读

The issue of finite-sample inference in Generalised Autoregressive Conditional Heteroskedasticity (GARCH)-like models has seldom been explored in the theoretical literature, although its potential relevance for practitioners is obvious. In some cases, asymptotic theory may provide a very poor approximation to the actual distribution of the estimators in finite samples. The aim of this paper is to propose the application of the so-called double length regressions (DLR) to…

两个时间尺度的故事AI 解读

It is a common practice in finance to estimate volatility from the sum of frequently sampled squared returns. However, market microstructure poses challenges to this estimation approach, as evidenced by recent empirical studies in finance. The present work attempts to lay out theoretical grounds that reconcile continuous-time modeling and discrete-time samples. We propose an estimation approach that takes advantage of the rich sources in tick-by-tick data while preserving…

二元 ARCH 模型：QML 估计的有限样本性质及 LM 型检验应用AI 解读

This paper provides two main new results: the first shows theoretically that large biases and variances can arise when the quasi-maximum likelihood (QML) estimation method is employed in a simple bivariate structure under the assumption of conditional heteroskedasticity; and the second demonstrates how these analytical theoretical results can be used to improve the finite-sample performance of a test for multivariate autoregressive conditional heteroskedastic (ARCH) effects,…

波动模型的预测比较：有模型能胜过 GARCH(1,1) 吗？AI 解读

Abstract We compare 330 ARCH‐type models in terms of their ability to describe the conditional variance. The models are compared out‐of‐sample using DM–$ exchange rate data and IBM return data, where the latter is based on a new data set of realized variance. We find no evidence that a GARCH(1,1) is outperformed by more sophisticated models in our analysis of exchange rates, whereas the GARCH(1,1) is clearly inferior to models that can accommodate a leverage effect in our…

摘要暂缺

摘要暂缺

GARCH 模型残差的高阶矩部分和过程及其应用AI 解读

In this paper we construct high moment partial sum processes based on residuals of a GARCH model when the mean is known to be 0. We consider partial sums of kth powers of residuals, CUSUM processes and self-normalized partial sum processes. The kth power partial sum process converges to a Brownian process plus a correction term, where the correction term depends on the kth moment μk of the innovation sequence. If μk=0, then the correction term is 0 and, thus, the kth power…

This paper evaluates the importance of "property rights institutions," which protect citizens against expropriation by the government and powerful elites, and "contracting institutions," which enable private contracts between citizens. We exploit exogenous variation in both types of institutions driven by colonial history and document strong first‐stage relationships between property rights institutions and the determinants of European colonization strategy (settler…

条件分位数预测的评价与组合AI 解读

We propose an encompassing test for comparing conditional quantile forecasts in an out-of-sample framework. Our test provides a basis for forecast combination when encompassing is rejected. Its central features are (1) use of the “tick” loss function, (2) a conditional approach to out-of-sample evaluation, and (3) derivation in an environment with asymptotically nonvanishing estimation uncertainty. Our approach is valid under general conditions; the forecasts can be based on…

检验可预测性的交易策略方法AI 解读

We propose a market timing test for conditional mean independence of financial returns. The new excess predictability (EP) test statistic has an interpretation of a properly normalized return of a certain trading strategy. We discuss similarities of the EP test to the popular directional accuracy (DA) test of Pesaran and Timmermann. Power properties of the EP test are advantageous, and size properties are comparable to those of the DA test. We illustrate application of the…

一类 ARCH(q) 模型准极大似然估计的渐近性质AI 解读

Strong consistency and asymptotic normality are established for the quasi-maximum likelihood estimator for a class of ARCH(q) models. The conditions are that the ARCH process is geometrically ergodic with a moment of arbitrarily small order. Furthermore for consistency, we assume that the second-order moment exists for the nondegenerate rescaled errors and, similarly, that the fourth-order moment exists for asymptotic normality to hold. Contrary to existing literature on…

最小绝对偏差估计的条件异方差时间序列模型诊断AI 解读

The recent paper by Peng & Yao (2003) gave an interesting extension of least absolute deviation estimation to generalised autoregressive conditional heteroscedasticity, GARCH, time series models. The asymptotic distributions of absolute residual autocorrelations and squared residual autocorrelations from the GARCH model estimated by the least absolute deviation method are derived in this paper. These results lead to two useful diagnostic tools which can be used to check…

摘要暂缺

股票收益的非平稳性AI 解读

The paper outlines a methodology for analyzing daily stock returns that relinquishes the assumption of global stationarity. Giving up this common working hypothesis reflects our belief that fundamental features of the financial markets are continuously and significantly changing. Our approach approximates the nonstationary data locally by stationary models. The methodology is applied to the S&P 500 series of returns covering a period of over seventy years of market activity.…

刻画高持续性波动的非线性GARCH模型AI 解读

In this paper we study a new class of nonlinear GARCH models. Special interest is devoted to models that are similar to previously introduced smooth transition GARCH models except for the novel feature that a lagged value of conditional variance is used as the transition variable. This choice of the transition variable is mainly motivated by the desire to find useful models for highly persistent volatility. The underlying idea is that high persistence in conditional variance…

含跳跃仿射随机波动率模型推断的自适应 MCMC 方法AI 解读

In this paper we propose an efficient Markov chain Monte Carlo (MCMC) algorithm to estimate stochastic volatility models with jumps and affine structure. Our idea relies on the use of adaptive methods that aim at reducing the asymptotic variance of the estimates. We focus on the Delayed Rejection algorithm in order to find accurate proposals and to efficiently simulate the volatility path. Furthermore, Bayesian model selection is addressed through the use of reduced runs of…

不假定参数已识别的 GMM 参数检验AI 解读

We propose a generalized method of moments (GMM) Lagrange multiplier statistic, i.e., the K statistic, that uses a Jacobian estimator based on the continuous updating estimator that is asymptotically uncorrelated with the sample average of the moments. Its asymptotic χ-super-2 distribution therefore holds under a wider set of circumstances, like weak instruments, than the standard full rank case for the expected Jacobian under which the asymptotic χ-super-2 distributions of…

波动率长记忆的估计AI 解读

We consider semiparametric estimation of the memory parameter in a model that includes as special cases both long-memory stochastic volatility and fractionally integrated exponential GARCH (FIEGARCH) models. Under our general model the logarithms of the squared returns can be decomposed into the sum of a long-memory signal and a white noise. We consider periodogram-based estimators using a local Whittle criterion function. We allow the optional inclusion of an additional…

一类新的多元偏斜密度及其在GARCH模型中的应用AI 解读

We propose a practical and flexible method to introduce skewness in multivariate symmetric distributions. Applying this procedure to the multivariate Student density leads to a “multivariate skew-Student” density in which each marginal has a specific asymmetry coefficient. Combined with a multivariate generalized autoregressive conditional heteroscedasticity model, this new family of distributions is found to be more useful than its symmetric counterpart for modeling stock…

含时变成分的结构学习：追踪金融时间序列的横截面结构AI 解读

Summary When modelling multivariate financial data, the problem of structural learning is compounded by the fact that the covariance structure changes with time. Previous work has focused on modelling those changes by using multivariate stochastic volatility models. We present an alternative to these models that focuses instead on the latent graphical structure that is related to the precision matrix. We develop a graphical model for sequences of Gaussian random vectors when…

利用辅助变量构造平稳时间序列模型及其应用AI 解读

Here we present a novel method for modeling stationary time series. Our approach is to construct the model with a specified marginal family and build the dependence structure around it. We show that the resulting time series is linear with a simple autocorrelation structure. We construct models that parallel existing structures, namely state-space models, autoregressive conditional heteroscedasticity (ARCH) models, and generalized ARCH models. We use Bayesian techniques to…

企业杠杆是否不足？杠杆对违约概率的影响AI 解读

ABSTRACT A commonly held view in corporate finance is that firms are less leveraged than they should be, given the potentially large tax benefits of debt. In this paper, I study the effect of firms' leverage on default probabilities as represented by the firms' ratings. Using an instrumental variable approach, I find that the leverage's effect on ratings is three times stronger than it is if the endogeneity of leverage is ignored. This stronger effect results in a higher…

方差比检验：连续时间渐近框架下的尺寸与功效分析AI 解读

We consider the statistical properties of the variance ratio statistic in the context of testing for market efficiency defined by uncorrelated returns. The statistic is then the ratio of the variance of K-period returns to the variance of one-period returns scaled by K. We use a continuous-time asymptotic framework whereby we let the sample size increase to infinity keeping the span of the data fixed. We also let the aggregation parameter K increase such that K/T → κ as T,…