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期限结构中的状态转换、风险溢价与商业周期AI 解读

Recent evidence indicates that using multiple forward rates sharply predicts future excess returns on U.S. Treasury bonds—the R 2 ’s being around 30%. The projection coefficients in these regressions exhibit a distinct pattern that relates to the maturity of the forward rate. These dimensions of the data in conjunction with the transition dynamics of bond yields offer a serious challenge to term structure models. In this paper we show that a regime-shifts term structure…

利用协偏度检验资产定价模型AI 解读

In this article we investigate portfolio coskewness using a quadratic market model as a return-generating process. We show that the portfolios of small (large) firms have negative (positive) coskewness with the market. We test an asset pricing model including coskewness by checking the validity of the restrictions that it imposes on the return-generating process. We find evidence of an additional component in expected excess returns, which is not explained by either…

CAViaR：条件自回归风险价值模型AI 解读

Value at risk (VaR) is the standard measure of market risk used by financial institutions. Interpreting the VaR as the quantile of future portfolio values conditional on current information, the conditional autoregressive value at risk (CAViaR) model specifies the evolution of the quantile over time using an autoregressive process and estimates the parameters with regression quantiles. Utilizing the criterion that each period the probability of exceeding the VaR must be…

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Consistent standard errors for target variance approach to GARCH estimation.

扩展的常条件相关 GARCH 模型及其四阶矩结构AI 解读

The constant conditional correlation general autoregressive conditional heteroskedasticity (GARCH) model is among the most commonly applied multivariate GARCH models and serves as a benchmark against which other models can be compared. In this paper we consider an extension to this model and examine its fourth-moment structure. The extension, first defined by Jeantheau (1998, Econometric Theory 14, 70–86), is motivated by the result found and discussed in this paper that the…

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潜在广义 ARCH 结构的似然估计AI 解读

GARCH models are commonly used as latent processes in econometrics, financial economics, and macroeconomics. Yet no exact likelihood analysis of these models has been provided so far. In this paper we outline the issues and suggest a Markov chain Monte Carlo algorithm which allows the calculation of a classical estimator via the simulated EM algorithm or a Bayesian solution in O(T) computational operations, where T denotes the sample size. We assess the performance of our…

汇率条件异方差：基于分数单整方法的进一步结果AI 解读

Abstract A recent article (Tse, 1998 ) published in this journal analysed the conditional heteroscedasticity of the yen–dollar exchange rate based on the fractionally integrated asymmetric power ARCH model. In this paper, we present replication results using Tse's ( 1998 ) yen–dollar series. We also examine the robustness of Tse's ( 1998 ) findings across different currencies, sample periods and non‐nested GARCH‐type models. Unlike Tse ( 1998 ), we find some evidence of…

利用广义谱与非线性时间序列模型推断汇率可预测性AI 解读

It is often documented, based on autocorrelation, variance ratio, and power spectrum, that exchange rates approximately follow a martingale process. Because these data check serial uncorrelatedness rather than martingale difference, they may deliver misleading conclusions in favor of the martingale hypothesis when the test statistics are insignificant. In this paper, we explore whether there exists a gap between serial uncorrelatedness and martingale difference for exchange…

股票价格的持续期依赖AI 解读

This article studies time series dependence in the direction of stock prices by modeling the (instantaneous) probability that a bull or bear market terminates as a function of its age and a set of underlying state variables, such as interest rates. A random walk model is rejected both for bull and bear markets. Although it fits the data better, a generalized autoregressive conditional heteroscedasticity model is also found to be inconsistent with the very long bull markets…

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同时建模条件异方差与尺度变化AI 解读

This paper proposes a semiparametric approach by introducing a smooth scale function into the standard generalized autoregressive conditional heteroskedastic (GARCH) model so that conditional heteroskedasticity (CH) and scale change in financial returns can be modeled simultaneously. An estimation procedure combining kernel estimation of the scale function and maximum likelihood estimation of the GARCH parameters is proposed. Asymptotic properties of the estimators are…

非高斯 Ornstein–Uhlenbeck 随机波动过程的贝叶斯推断AI 解读

Summary We develop Markov chain Monte Carlo methodology for Bayesian inference for non-Gaussian Ornstein–Uhlenbeck stochastic volatility processes. The approach introduced involves expressing the unobserved stochastic volatility process in terms of a suitable marked Poisson process. We introduce two specific classes of Metropolis–Hastings algorithms which correspond to different ways of jointly parameterizing the marked point process and the model parameters. The performance…

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An asymmetric information model of a finite horizon “nth order” rational asset price bubble is presented, where (all agents know that)n the asset is worthless. Also, the model has only two agents, so the first order version of the bubble is simpler than other first order bubbles in the literature.

This paper analyses multivariate high frequency financial data using realized covariation. We provide a new asymptotic distribution theory for standard methods such as regression, correlation analysis, and covariance. It will be based on a fixed interval of time (e.g., a day or week), allowing the number of high frequency returns during this period to go to infinity. Our analysis allows us to study how high frequency correlations, regressions, and covariances change through…

GARCH 过程参数估计量的效率AI 解读

We propose a class of estimators for the parameters of a GARCH(p,q) sequence. We show that our estimators are consistent and asymptotically normal under mild conditions. The quasi-maximum likelihood and the likelihood estimators are discussed in detail. We show that the maximum likelihood estimator is optimal. If the tail of the distribution of the innovations is polynomial, even a quasi-maximum likelihood estimator based on exponential density performs better than the…

时变波动率模型的统计推断AI 解读

This paper offers a new approach for estimating and forecasting the volatility of financial time series. No assumption is made about the parametric form of the processes. On the contrary, we only suppose that the volatility can be approximated by a constant over some interval. In such a framework, the main problem consists of filtering this interval of time homogeneity; then the estimate of the volatility can be simply obtained by local averaging. We construct a locally…

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个股收益的新闻到达、跳跃动态与波动率成分AI 解读

ABSTRACT This paper models components of the return distribution, which are assumed to be directed by a latent news process. The conditional variance of returns is a combination of jumps and smoothly changing components. A heterogeneous Poisson process with a time‐varying conditional intensity parameter governs the likelihood of jumps. Unlike typical jump models with stochastic volatility, previous realizations of both jump and normal innovations can feed back asymmetrically…

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