每日简报

具有灵活残差与离散马尔可夫动态的自激及互激点过程AI 解读

This work introduces a self and mutually exciting point process that embeds flexible residuals and intensity with discretely Markovian dynamics. By allowing the integration of diverse residual distributions, this model serves as an extension of the Hawkes process, facilitating intensity modeling. This model's nature enables a filtered historical simulation that more accurately incorporates the properties of the original time series. Furthermore, the process extends to…

利用基于模拟的校准比较随机波动模型中的 MCMC 算法AI 解读

Simulation Based Calibration (SBC) is applied to analyse two commonly used, competing Markov chain Monte Carlo algorithms for estimating the posterior distribution of a stochastic volatility model. In particular, the bespoke 'off-set mixture approximation' algorithm proposed by Kim, Shephard, and Chib (1998) is explored together with a Hamiltonian Monte Carlo algorithm implemented through Stan. The SBC analysis involves a simulation study to assess whether each sampling…

偏斜 t 分布实现随机波动率模型：波动率与尾部风险预测AI 解读

Accurate forecasting of volatility is essential for financial risk management and for the evaluation of tail risk measures such as value-at-risk (VaR) and expected shortfall (ES). This study proposes the realized stochastic volatility (RSV) model, an extension of the traditional stochastic volatility (SV) model that incorporates realized volatility as an efficient proxy for latent volatility. To better capture the stylized features of financial return distributions,…

利用期权估计波动率的波动率与杠杆效应AI 解读

We propose model-free (nonparametric) estimators of the volatility of volatility and leverage effect using high-frequency observations of short-dated options. At each point in time, we integrate available options into estimates of the conditional characteristic function of the price increment until the options' expiration and we use these estimates to recover spot volatility. Our volatility of volatility estimator is then formed from the sample variance and first-order…

预期短缺的LASSO估计AI 解读

We propose an $\ell_1$-penalized estimator for high-dimensional models of Expected Shortfall (ES). The estimator is obtained as the solution to a least-squares problem for an auxiliary dependent variable, which is defined as a transformation of the dependent variable and a pre-estimated tail quantile. Leveraging a sparsity condition, we derive a nonasymptotic bound on the prediction and estimator errors of the ES estimator, accounting for the estimation error in the…