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连续时间证券市场事件建模：基于强度的多元点过程模型AI 解读

A continuous time econometric modelling framework for multivariate financial market event (or ‘transactions’) data is developed in which the model is specified via the vector conditional intensity. Generalised Hawkes models are introduced that incorporate inhibitory events and dependence be- tween trading days. Novel omnibus specification tests based on a multivariate random time change theorem are proposed. A bivariate point process model of the timing of trades and…

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基于已实现极差的积分方差估计AI 解读

We provide a set of probabilistic laws for estimating the quadratic variation of continuous semimartingales with realized range-based variance -- a statistic that replaces every squared return of realized variance with a normalized squared range. If the entire sample path of the process is available, and under a set of weak conditions, our statistic is consistent and has a mixed Gaussian limit, whose precision is five times greater than that of realized variance. In…

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高频金融数据的多尺度跳跃与波动率分析AI 解读

The wide availability of high-frequency data for many financial instruments stimulates an upsurge interest in statistical research on the estimation of volatility. Jump-diffusion processes observed with market microstructure noise are frequently used to model high-frequency financial data. Yet existing methods are developed for either noisy data from a continuous-diffusion price model or data from a jump-diffusion price model without noise. We propose methods to cope with…

参数位于边界时的高阶渐近理论及其GARCH应用AI 解读

Andrews (1999, Econometrica 67, 1341–1383) derived the first-order asymptotic theory for a very general class of estimators when a parameter is on a boundary. We derive the second-order asymptotic theory in this setting in some special cases. We focus on the behavior of the quasi maximum likelihood estimator (QMLE) in stationary and nonstationary generalized autoregressive conditionally heteroskedastic (GARCH) models when constraints are imposed in the maximization…

波动率非对称建模：树结构多元 GARCH 模型的贝叶斯分析AI 解读

A new class of multivariate threshold GARCH models is proposed for the analysis and modelling of volatility asymmetries in financial time series. The approach is based on the idea of a binary tree where every terminal node parametrizes a (local) multivariate GARCH model for a specific partition of the data. A Bayesian stochastic method is developed and presented for the analysis of the proposed model consisting of parameter estimation, model selection and volatility…

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基于矩条件的金融收益 Copula 检验AI 解读

We propose a class of moment-based tests for copulas in a parametric multivariate dynamic context for financial returns. The proposed method takes into account the effect of estimation uncertainty. This effect is quite important but often is ignored in related studies. Our method can be applied to generate various tests to detect copula misspecification in different directions. In particular, on the basis of the conditional probabilities of quantile exceedances (Kendall's…

风险溢价在波动率预测中的作用AI 解读

I explain why at-the-money implied volatility is a biased and inefficient forecast of future realized volatility using the insights from the empirical option-pricing literature. First, I explain how the risk premia, which manifest themselves through disparity between objective and risk-neutral probability measures, lead to the disparity between realized and implied volatilities. Second, I show that this disparity is a function of the latent spot volatility, which I estimate…

平稳分数协整的局部 Whittle 分析及隐含与已实现波动率的关系AI 解读

I consider local Whittle analysis of a stationary fractionally cointegrated model. The local Whittle quasi maximum likelihood estimator is proposed to jointly estimate the integration orders of the regressors, the integration order of the errors, and the cointegration vector. The proposed estimator is semiparametric in the sense that it employs local assumptions on the joint spectral density matrix of the regressors and the errors near the zero frequency. I show that the…

允许非高斯误差的均值—方差有效性多元检验AI 解读

We develop exact mean–variance efficiency tests of the market portfolio in the context of (conditional and unconditional) capital asset pricing models (CAPM), allowing for a wide class of possibly non-Gaussian error distributions. The proposed procedures are applicable in a general multivariate linear regression framework, and exactness is achieved through Monte Carlo test techniques. We also perform exact multivariate diagnostic checks. Empirical results show that the…

非线性非高斯状态空间模型的蒙特卡洛估计AI 解读

We develop a proposal or importance density for state space models with a nonlinear non-Gaussian observation vector y ∼ p(y¦θ) and an unobserved linear Gaussian signal vector θ ∼ p(θ). The proposal density is obtained from the Laplace approximation of the smoothing density p(θ¦y). We present efficient algorithms to calculate the mode of p(θ¦y) and to sample from the proposal density. The samples can be used for importance sampling and Markov chain Monte Carlo methods. The…

Lévy 过程驱动的随机波动率模型：模拟与推断AI 解读

We study Ornstein-Uhlenbeck stochastic processes driven by Lévy processes, and extend them to more general non-Ornstein-Uhlenbeck models. In particular, we investigate the means of making the correlation structure in the volatility process more flexible. For one model, we implement a method for introducing quasi long-memory into the volatility model. We demonstrate that the models can be fitted to real share price returns data.

可耗竭资源价格的均衡动态AI 解读

ABSTRACT We develop equilibrium models of exhaustible resource markets with endogenous extraction choices and prices. Our analysis demonstrates how adjustment costs can generate oil and gas forward price dynamics with two factors, consistent with the behavior these commodities exhibit in the Schwartz and Smith (2000) calibration. Our two‐factor model predicts that stochastic volatility will arise in these markets as a natural consequence of production adjustments, however,…

非对称幂分布：理论及风险度量应用AI 解读

Abstract Theoretical literature in finance has shown that the risk of financial time series can be well quantified by their expected shortfall, also known as the tail value‐at‐risk. In this paper, I construct a parametric estimator for the expected shortfall based on a flexible family of densities, called the asymmetric power distribution (APD). The APD family extends the generalized power distribution to cases where the data exhibits asymmetry. The first contribution of the…

混合条件异方差模型的贝叶斯推断AI 解读

We estimate by Bayesian inference the mixed conditional heteroskedasticity model of Haas et al. (2004a Journal of Financial Econometrics 2, 211--50). We construct a Gibbs sampler algorithm to compute posterior and predictive densities. The number of mixture components is selected by the marginal likelihood criterion. We apply the model to the SP500 daily returns. Copyright Royal Economic Society 2007

基于数值积分的高斯混合滤波：非对称随机波动率模型的极大似然估计AI 解读

I consider Gaussian filters based on numerical integration for maximum likelihood estimation of stochastic volatility models with leverage. I show that for this class of models, the prediction step of the Gaussian filter can be evaluated analytically without linearizing the state--space model. Monte Carlo simulations show that the mixture Gaussian filter performs remarkably well in terms of both accuracy and computation time compared to the quasi-maximum likelihood and…

COGARCH(1,1)模型的矩估计AI 解读

We suggest moment estimators for the parameters of a continuous time GARCH(1,1) process based on equally spaced observations. Using the fact that the increments of the COGARCH(1,1) process are strongly mixing with exponential rate, we show that the resulting estimators are consistent and asymptotically normal. We investigate the empirical quality of our estimators in a simulation study based on the variance gamma driven COGARCH(1,1) model. The estimated volatility with…

利用样条与合流超几何函数估计期权隐含风险中性密度AI 解读

We examine the ability of two recent methods -- the smoothed implied volatility smile method (SML) and the density functionals based on confluent hypergeometric functions (DFCH) -- for estimating implied risk-neutral densities (RNDs) from European-style options. Two complementary Monte Carlo experiments are conducted and the performance of the two RND estimators is evaluated by the root mean integrated squared error (RMISE) criterion. Results from both experiments show that…

波动率估计中时间域与状态域方法的动态整合AI 解读

Time- and state-domain methods are two common approaches to nonparametric prediction. Whereas the former uses data predominantly from recent history, the latter relies mainly on historical information. Combining these two pieces of valuable information is an interesting challenge in statistics. We surmount this problem by dynamically integrating information from both the time and state domains. The estimators from these two domains are optimally combined based on a…

马尔可夫状态转换 GARCH 过程的平稳性AI 解读

Generalized autoregressive conditional heteroskedasticity (GARCH) models with Markov-switching regimes are often used for volatility analysis of financial time series. Such models imply less persistence in the conditional variance than the standard GARCH model and potentially provide a significant improvement in volatility forecast. Nevertheless, conditions for asymptotic wide-sense stationarity have been derived only for some degenerated models. In this paper, we introduce…