Implicit Bayesian Inference Using Option Prices

Abstract. A Bayesian approach to option pricing is presented in which posterior inference about the underlying returns process is conducted implicitly via observed option prices. A range of models allowing for conditional leptokurtosis, skewness and time‐varying volatility in returns are considered, with posterior parameter distributions and model probabilities backed out from the option prices. Models are ranked according to several criteria, including out‐of‐sample predictive and hedging performance. The methodology accommodates heteroscedasticity and autocorrelation in the option pricing errors, as well as regime shifts across contract groups. The method is applied to intraday option price data on the S&P500 stock index for 1995. While the results provide support for models that accommodate leptokurtosis and skewness, no one model dominates when all criteria are considered.     

On a Mixture GARCH Time‐Series Model

Abstract. Recently, there has been a lot of interest in modelling real data with a heavy‐tailed distribution. A popular candidate is the so‐called generalized autoregressive conditional heteroscedastic (GARCH) model. Unfortunately, the tails of GARCH models are not thick enough in some applications. In this paper, we propose a mixture generalized autoregressive conditional heteroscedastic (MGARCH) model. The stationarity conditions and the tail behaviour of the MGARCH model are studied. It is shown that MGARCH models have tails thicker than those of the associated GARCH models. Therefore, the MGARCH models are more capable of capturing the heavy‐tailed features in real data. Some real examples illustrate the results.     

上海股市收益与流动性风险动态关系实证

应用Engle(2002)提出的动态条件相关多元GARCH模型(DCC-MVGARCH)和向量自回归(VAR)方法,研究了上海股票市场收益与Acharya and Pedersen(2005)提出的三种流动性风险以及系统风险变量的动态关系.研究结果表明,上海股市存在系统风险溢价和流动性风险溢价,但风险变量对收益率的影响较小,股市的风险传递机制尚未真正形成.     

基于GARCH模型的WTI原油价格波动性分析

运用GARCH、EGARCH的t分布模型对WTI原油现货价格进行了统计拟合分析,得到了其收益率序列尖峰厚尾和异方差性等主要概率特征,并对GARCH、EGARCH的t分布模型的预测效果进行了比较分析,发现基于学生t分布的EGARCH模型比GARCH模型能更好地描述WTI原油价格的波动特征,并且具有较好的预测能力.     

检验门限协整模型中的线性协整

考虑门限协整回归模型中线性的检验问题.在原假设为线性协整的条件下,构造TSupLM(supremumLagrange multiplier)统计量,并给出了极限分布.Monte Carlo实验研究了SupLM检验的有限样本性能,结果表明SupLM检验不受回归误差的序列相关性影响,也不受广义的自回归条件异方差GARCH(generalized autoregressiveconditional heteroskedastic)的影响.应用SupLM检验方法检测美国国库券收益率之间的关系,结果表明不同到期时间的国库券收益率之间存在门限协整关系.     

CBOE Volatility Index Forecasting under COVID-19: An Integrated BiLSTM-ARIMA-GARCH Model

After the outbreak of COVID-19, the global economy entered a deep freeze. This observation is supported by the Volatility Index (VIX), which reflects the market risk expected by investors. In the current study, we predicted the VIX using variables obtained from the sentiment analysis of data on Twitter posts related to the keyword “COVID-19,” using a model integrating the bidirectional long-term memory (BiLSTM), autoregressive integrated moving average (ARIMA) algorithm, and generalized autoregressive conditional heteroskedasticity (GARCH) model. The Linguistic Inquiry and Word Count (LIWC) program and Valence Aware Dictionary for Sentiment Reasoning (VADER) model were utilized as sentiment analysis methods. The results revealed that during COVID-19, the proposed integrated model, which trained both the Twitter sentiment values and historical VIX values, presented better results in forecasting the VIX in time-series regression and direction prediction than those of the other existing models.     

Estimating m-regimes STAR-GARCH model using QMLE with parameter transformation

It is well known in the literature that obtaining the parameter estimates for the Smooth Transition Autoregressive-Generalized Autoregressive Conditional Heteroskedasticity (STAR-GARCH) can be problematic due to computational difficulties. Conventional optimization algorithms do not seem to perform well in locating the global optimum of the associated likelihood function. This makes Quasi-Maximum Likelihood Estimator (QMLE) difficult to obtain for STAR-GARCH models in practice. Curiously, there has been very little research investigating the cause of the numerical difficulties in obtaining the parameter estimates for STAR-GARCH using QMLE. The aim of the paper is to investigate the nature of the numerical difficulties using Monte Carlo Simulation. By examining the surface of the log-likelihood function based on simulated data, the results provide several insights into the difficulties in obtaining QMLE for STAR-GARCH models. Based on the findings, the paper also proposes a simple transformation on the parameters to alleviate these difficulties. Monte Carlo simulation results show promising signs for the proposed transform. The asymptotic and robust variance-covariance matrices of the original parameter estimates are derived as a function of the transformed parameter estimates, which greatly facilitates inferences on the original parameters.     

A fuzzy GARCH model applied to stock market scenario using a genetic algorithm

In this paper, we derive a new application of fuzzy systems designed for a generalized autoregression conditional heteroscedasticity (GARCH) model. In general, stock market performance is time-varying and nonlinear, and exhibits properties of clustering. The latter means simply that certain large changes tend to follow other large changes, and in general small changes tend to follow other small changes. This paper shows results from using the method of functional fuzzy systems to analyze the clustering in the case of a GARCH model.The optimal parameters of the fuzzy membership functions and GARCH model are extracted using a genetic algorithm (GA). The GA method aims to achieve a global optimal solution with a fast convergence rate for this fuzzy GARCH model estimation problem. From the simulation results, we have determined that the performance is significantly improved if the leverage effect of clustering is considered in the GARCH model. The simulations use stock market data from the Taiwan weighted index (Taiwan) and the NASDAQ composite index (NASDAQ) to illustrate the performance of the proposed method.     

基于考虑外生变量的SWARCH模型对中国股市波动性的实证研究

货币供应量是货币政策的主要调控指标,在很大程度上影响着股市的波动情况,作者将货币供应量作为外生变量加入到SWARCH模型中,建立了上证指数的考虑外生变量的SWARCH模型,实证研究结果表明该模型具有较好的拟合和预测效果．     

Forecasting models for Taiwanese tourism demand after allowance for Mainland China tourists visiting Taiwan

The tourism industry is an increasingly important national industry for Taiwan. Government policymakers and business managers pay close attention to the development of the tourism industry. In a rapidly changing environment that is influenced by numerous socioeconomic factors, the tourism industry must have an accurate method to forecast future tourism demand such that decision makers will be able to meet future challenges more effectively. Based on these concerns, this study proposes the SARIMA–GARCH model to analyze and forecast the tourism demand in Taiwan and compare the predictive power of this model and other forecasting models. The results provide a valuable reference for decision-makers in the tourism industry of Taiwan.