上证综合指数波动情况研究——基于滚动窗口的马尔科夫链预测模型

文章运用基于滚动窗口的马尔科夫链预测模型,对上证综指的变动进行研究,创新地给出概率转移矩阵、极限概率以及预测准确率的时变特征,并首次给出马尔科夫链预测模型的最优窗口长度和状态定义阀值。研究显示,大盘波动幅度与大盘的极限概率有着密切的关系;股指期货推出后大盘平盘概率占据主导地位,平稳性显著提高,马尔科夫链预测模型的预测准确率也有了较大提高。     

GARP数量化选股及马尔科夫链择时策略研究

随着现代金融理论的发展与国内金融工程技术的提高,量化交易策略逐渐在中国资本市场崭露头角。文章以上证综指成分股作为股票池,运用2012~2015年基本面数据和行情数据对GARP数量化选股模型以及择时策略展开实证研究。在检验各选股因子有效性的基础上,通过综合评分的方法构建GARP选股模型;运用马尔科夫链预测模型,对股票价格的状态进行动态预测,从而构建择时信号;探究状态预测准确率受窗口长度及状态阈值的影响,并给出最优参数取值。文章首次基于动态马尔科夫预测模型设计择时策略,并与GARP选股模型有效地结合,为数量化投资提供新的思路。     

基于马尔科夫链模型的沪综指数预测

面临大盘的剧烈波动和调整,大盘的走势也越来越难判断,本文在当前股票市场的背景下,采用马尔科夫链的方法对沪综合指数的走势进行预测,通过马尔科夫的平稳分布和最终的稳态条件,计算出大盘涨、平、跌三个状态的概率分布,并对投资者提出一定的借鉴性建议。     

基于马尔科夫链模型的沪综指数预测

面临大盘的剧烈波动和调整,大盘的走势也越来越难判断,本文在当前股票市场的背景下,采用马尔科夫链的方法对沪综合指数的走势进行预测,通过马尔科夫的平稳分布和最终的稳态条件,计算出大盘涨、平、跌三个状态的概率分布,并对投资者提出一定的借鉴性建议。     

基于马尔科夫链对股票价格预测

合理地对股票价格进行预测是众多股票研究者所追求的目标。而随着知识学习的不断深入,利用数学模型的方法进行股票价格预测近年来更加受到人们的关注。在研究股票市场时,我们常常利用马尔科夫链的方法预测股票价格趋势,以期为投资者及股票市场管理者提供一些决策依据。本文先向大家简单介绍了马尔科夫链,接着建立数学模型,并利用实例检验了所建立的马尔科夫链模型在进行股价预测时的可行性,为以后股价预测方面的研究提供借鉴。     

马尔科夫预测法在股票价格预测中的应用

文章通过介绍马尔科夫预测法的基本原理,并且把马尔科夫预测法应用到股票价格的预测中,运用马尔科夫预测法关键是获得初始状态向量和状态转移概率矩阵,通过实证分析的验证,马尔科夫预测法在短期的股票价格预测中还是可以取得一定的效果的。     

基于马尔科夫链的武汉三次产业贡献率的比较分析

根据武汉市1991-2008年的三次产业贡献率,应用马尔科夫预测法以及多元线性回归方法,给出三次产业贡献率的转移概率矩阵估算公式和回归方程,并据此对三次产业贡献率进行预测分析。     

基于马尔科夫链的武汉三次产业贡献率的比较分析

根据武汉市1991-2008年的三次产业贡献率,应用马尔科夫预测法以及多元线性回归方法,给出三次产业贡献率的转移概率矩阵估算公式和回归方程,并据此对三次产业贡献率进行预测分析。     

基于聚类的Markov预测模型的改进

传统的Markov链模型是一种简单而有效的预测模型,该模型存在着预测准确率低,存储复杂度高等缺点.改进的基于聚类的Markov链预测模型,利用用户访问特征和人们浏览网页与时间高度相关的思想来改善模型,建立了基于用户访问特征和时间段聚类的Markov预测模型并进行了模拟实验和结果分析.     

财务困境预测模型的实证分析与改进——基于中国上市公司的研究

本文利用2010—2012年沪深两市中被ST的47家公司和47家非ST公司的财务指标,根据中国资本市场的实际状况,构建了一个系数与变量修正后的Z-score模型;然后建立一个以现金流量为基础的财务预警统计模型,共同构建财务困境预测模型组合.实证研究表明:整个预测系统的准确率较高,ST公司在T-2年前被成功预测的概率为82.98%,配合现金流财务预警模型,准确率进一步提升.所以该预测模型组合具有较大的使用和研究价值.     

Harnessing jump component for crude oil volatility forecasting in the presence of extreme shocks

Oil markets are subject to extreme shocks (e.g. Iraq’s invasion of Kuwait), causing the oil market price exhibits extreme movements, called jumps (or spikes). These jumps pose challenges on oil market volatility forecasting using conventional volatility dynamic models (e.g. GARCH model) This paper characterizes dynamics of jumps in oil market price using high frequency data from three perspectives: the probability (or intensity) of jump occurrence, the sign (e.g. positive or negative) of jumps, and the concurrence with stock market jumps. And then, the paper exploits predictive ability of these jump-related information for oil market volatility forecasting under the mixed data sampling (MIDAS) modeling framework. Our empirical results show that augmenting standard MIDAS model using the three jump-related information significantly improves the accuracy of oil market volatility forecasting. The jump intensity and negative jump size are particularly useful for predicting future oil volatility. These results are widely consistent across a variety of robustness tests. This work provides new insights on how to forecast oil market volatility in the presence of extreme shocks.     

Forecasting gold volatility with geopolitical risk indices

This paper tries to forecast gold volatility with multiple country-specific (GPR) indices and compares the role of combined prediction models and dimension reduction methods regarding the improvement of gold volatility prediction accuracy. For this purpose, GARCH-MIDAS model’s several extensions are used. We find firstly that most country-specific GPR indices have driving effects on gold volatility, and it makes sense to take forecast information from multiple country-specific GPR indices into account when forecasting gold volatility. The out-of-sample empirical results also indicate that the dimension reduction methods yield better predictions compared to the combined prediction models. In addition, dimension reduction technologies have excellent forecasting performance mainly during low gold volatility periods. Finally, our empirical findings are robust after changing the evaluation method, model settings, in-sample length and gold market.     

Futures volatility forecasting based on big data analytics with incorporating an order imbalance effect

Future markets play vital roles in supporting economic activities in modern society. For example, crude oil and electricity futures markets have heavy effects on a nation’s energy operation management. Thus, volatility forecasting of the futures market is an emerging but increasingly influential field of financial research. In this paper, we adopt big data analytics, called Extreme Gradient Boosting (XGBoost) from computer science, in an attempt to improve the forecasting accuracy of futures volatility and to demonstrate the application of big data analytics in the financial spectrum in terms of volatility forecasting. We further unveil that order imbalance estimation might incorporate abundant information to reflect price jumps and other trading information in the futures market. Including order imbalance information helps our model capture underpinned market rules such as supply and demand, which lightens the information loss during the model formation. Our empirical results suggest that the volatility forecasting accuracy of the XGBoost method considerably beats the GARCH-jump and HAR-jump models in both crude oil futures market and electricity futures market. Our results could also produce plentiful research implications for both policy makers and energy futures market participants.     

FX volatility forecasts and the informational content of market data for volatility

The paper examines the medium-term forecasting ability of several alternative models of currency volatility. The data period covers more than eight years of daily observations, January 1991 to March 1999, for the spot exchange rate, 1- and 3-month volatility of the DEM/JPY, GBP/DEM, GBP/USD, USD/CHF, USD/DEM and USD/JPY. Comparing with the results of ‘pure’ time series models, the reported work investigates whether market implied volatility data can add value in terms of medium-term forecasting accuracy. This is done using data directly available from the marketplace in order to avoid the potential biases arising from ‘backing out’ volatility from a specific option pricing model. On the basis of the over 34 000 out-of-sample forecasts produced, evidence tends to indicate that, although no single volatility model emerges as an overall winner in terms of forecasting accuracy, the ‘mixed’ models incorporating market data for currency volatility perform best most of the time.     

Evaluating the performance of futures hedging using factors-driven realized volatility

The complexity and uncertainty of the financial market mainly stem from the rich market internal transaction information and a wide range effect of external factors. To this end, this paper proposes the combination factors-driven forecasting method to predict realized volatilities of the CSI 300 index and index futures. Based on the volatilities predicted by the proposed method, we further evaluate the ex-ante hedging performance in comparison to the conventional HAR model as well as GARCH-type models. The empirical results indicate that the factors-driven realized volatility model significantly dominates the other commonly used models in terms of hedging effectiveness. Furthermore, the superiority of the proposed method is robust in different market conditions, including significant rising or falling and abnormal market fluctuations in the COVID-19 pandemic, and in different index markets. Therefore, this paper improves the prediction accuracy of volatility by integrating market internal transaction information and external factor information, and the proposed method in this paper can be used by investors to obtain an excellent hedging effect.     

Volatility estimation via hidden Markov models

We propose a stochastic volatility model where the conditional variance of asset returns switches across a potentially large number of discrete levels, and the dynamics of the switches are driven by a latent Markov chain. A simple parameterization overcomes the commonly encountered problem in Markov-switching models that the number of parameters becomes unmanageable when the number of states in the Markov chain increases. This framework presents some interesting features in modelling the persistence of volatility, and that, far from being constraining in data fitting, it performs comparably well as other popular approaches in forecasting short-term volatility.     

Semi-parametric Bayesian tail risk forecasting incorporating realized measures of volatility

Realized measures employing intra-day sources of data have proven effective for dynamic volatility and tail-risk estimation and forecasting. Expected shortfall (ES) is a tail risk measure, now recommended by the Basel Committee, involving a conditional expectation that can be semi-parametrically estimated via an asymmetric sum of squares function. The conditional autoregressive expectile class of model, used to implicitly model ES, has been extended to allow the intra-day range, not just the daily return, as an input. This model class is here further extended to incorporate information on realized measures of volatility, including realized variance and realized range (RR), as well as scaled and smoothed versions of these. An asymmetric Gaussian density error formulation allows a likelihood that leads to direct estimation and one-step-ahead forecasts of quantiles and expectiles, and subsequently of ES. A Bayesian adaptive Markov chain Monte Carlo method is developed and employed for estimation and forecasting. In an empirical study forecasting daily tail risk measures in six financial market return series, over a seven-year period, models employing the RR generate the most accurate tail risk forecasts, compared to models employing other realized measures as well as to a range of well-known competitors.     

Oil futures volatility predictability: New evidence based on machine learning models

This paper comprehensively examines the connection between oil futures volatility and the financial market based on a model-rich environment, which contains traditional predicting models, machine learning models, and combination models. The results highlight the efficiency of machine learning models for oil futures volatility forecasting, particularly the ensemble models and neural network models. Most interestingly, we consider the “forecast combination puzzle” in machine learning models, and find that combination models continue to have more satisfactory performances in all types of situations. We also discuss the model interpretability and each indicator's contribution to the prediction. Our paper provides new insights for machine learning methods' applications in futures market volatility prediction, which is helpful for academics, policy-makers, and investors.     

“Social Security—Adequacy,Equity, and Progressiveness: A Review of Criteria Based on Experience In Canada and the United States”, Robert L. Brown,January 2000

Abstract

We consider the valuation of credit default swaps (CDSs) under an extended version of Merton’s structural model for a firm’s corporate liabilities. In particular, the interest rate process of a money market account, the appreciation rate, and the volatility of the firm’s value have switching dynamics governed by a finite-state Markov chain in continuous time. The states of the Markov chain are deemed to represent the states of an economy. The shift from one economic state to another may be attributed to certain factors that affect the profits or earnings of a firm; examples of such factors include changes in business conditions, corporate decisions, company operations, management strategies, macroeconomic conditions, and business cycles. In this article, the Esscher transform, which is a well-known tool in actuarial science, is employed to determine an equivalent martingale measure for the valuation problem in the incomplete market setting. Systems of coupled partial differential equations (PDEs) satisfied by the real-world and risk-neutral default probabilities are derived. The consequences for the swap rate of a CDS brought about by the regimeswitching effect of the firm’s value are investigated via a numerical example for the case of a two-state Markov chain. We perform sensitivity analyses for the real-world default probability and the swap rate when different model parameters vary. We also investigate the accuracy and efficiency of the PDE approach by comparing the numerical results from the PDE approach to those from the Monte Carlo simulation.     

Chinese agricultural futures volatility: New insights from potential domestic and global predictors

This paper investigates whether the potential predictors from China and globally can efficiently predict Chinese agricultural futures volatility by adopting the REGARCH-MIDAS framework. We highlight the predictability of numerous Chinese potential predictors for forecasting ten agricultural futures volatility, which is relatively better than that of global potential predictors. Robustness tests such as different realized measure and different forecasting window confirm the above conclusions. Performances of predictors during different volatility levels, before and during the COVID-19 pandemic are further discussed. This paper tries to shed new light on the volatility prediction of Chinese agricultural futures markets.