Common volatility in major stock index futures markets

The main objective of this paper is to investigate whether the US, UK and Japanese stock index futures markets have a similar volatility process. For this purpose, the common feature analysis proposed by Engle and Susmel (1993) is applied to 1988–1991 data after first removing price innovations using vector autoregression. The results suggest that the three markets have a single common factor generating volatilities among markets.     

Multiple structural changes in the tail behavior: Evidence from stock index futures returns

The study provides an approach of nonlinear analysis for detecting multiple structural changes in the tails of financial returns distributions. The proposed approach can simultaneously determine the number of structural breaks in a series of tail-indexes and estimate the mean tail-index levels in distinct regimes. The method is applied to the tail behavior of DJIA futures returns. The sample period covered various recent financial crises, and ran from October 1999 to December 2003, enabling assessment of the relationships between changes in the tail shape of returns distribution and known extreme events in financial markets. The empirical results demonstrate the existence of at least one break point in the left and common tails of the DJIA futures returns distribution during the sample period, and the mean tail-index levels in different regimes classified by estimated break points display an increase in the left and common tails that coincides the phenomenon of gradually decreasing financial turbulence in DJIA futures contracts. The empirical evidence indicates that the structural changes in the tail behavior of the distribution of DJIA futures returns are associated more with negative shocks than positive ones, creating differences in risk management between long and short investors in futures markets.     

The behavior of extreme values in Germany's stock index futures: An application to intradaily margin setting

Recent defaults and large financial losses attributed to derivative security investing point to an area of finance not often researched, i.e. the probabilities of observing extreme occurrences. This paper examines this behavior for German stock index futures (FDAX) contracts. Its empirical results indicate that large FDAX intradaily price changes follow a Fréchet extreme value distribution and the extreme value distribution probabilities may be confidently used to help set intradaily margin levels.     

股价指数的记忆性特征

本文通过ARFIMA模型(分整自回归滑动平均模型)分析了上证日指数、周指数、月指数序列的记忆性特征,说明股票价格日指数具有长记忆性、周指数具有中等记忆性、月指数具有短期记忆性,这一结论说明了中国股票市场是非有效的,其本身隐含一定的政策含义。     

Data-driven hedging of stock index options via deep learning

We develop deep learning models to learn the hedge ratio for S&P500 index options from options data. We compare different combinations of features and show that with sufficient training data, a feedforward neural network model with time to maturity, the Black-Scholes delta and market sentiment as inputs performs the best in the out-of-sample test under daily hedging. This model significantly outperforms delta hedging and a data-driven hedging model. Our results also demonstrate the importance of market sentiment for hedging.     

Nonlinearity, data-snooping, and stock index ETF return predictability

This paper examines daily return predictability for eighteen international stock index ETFs. The out-of-sample tests are conducted, based on linear and various popular nonlinear models and both statistical and economic criteria for model comparison. The main results show evidence of predictability for six of eighteen ETFs. A simple linear autoregression model, and a nonlinear-in-variance GARCH model, but not several popular nonlinear-in-mean models help outperform the martingale model. The allowance of data-snooping bias using White’s Reality Check also substantially weakens otherwise apparently strong predictability.     

Hedging insurance books

Complex insurance risks typically have multiple exposures. If available, options on multiple underliers with a short maturity can be employed to hedge this exposure. More precisely, the present value of aggregate payouts is hedged using least squares, ask price minimization, and ask price minimization constrained to long only option positions. The proposed hedges are illustrated for hypothetical Variable Annuity contracts invested in the nine sector ETF’s of the US economy. We simulate the insurance accounts by simulating risk-neutrally the underliers by writing them as transformed correlated normals; the physical and risk-neutral evolution is taken in the variance gamma class as a simple example of a non-Gaussian limit law. The hedges arising from ask price minimization constrained to long only option positions delivers a least cost and most stable result.     

ARCH类模型研究及其在沪市A股中的应用

本文主要介绍ARCH(AutoregressiveConditionalHeteroskedasticity)模型、GARCH模型和E GARCH模型 ,分析这些模型的特点和适用范围 ,并在模型中引入t分布取代正态分布假设 ,最后利用这些模型对上证指数进行了实证分析。     

实战汇率避险

今年以来,随着国内外经济金融形势的变化,人民币对外不断升值,人民币汇率形成机制改革的进一步深化,银行组合型汇率避险产品不断丰富,满足了企业规避汇率风险及财务运作需求,准确了解这些产品的特征和运转模式,在很大程度上有利于CFO们全面及时摆布企业资产,并力求取得收益最大化和风险最小化。     

Hedging Point Policy Improvement

This paper deals with the production and the corrective maintenance planning control problem for failure-prone manufacturing systems. The introduction of corrective maintenance increases the availability of the manufacturing system, which guarantees the improvement of the system productivity if the production planning is well done. For the one-machine/one-part system, we show that our planning control problem is more efficient than the one given by Akella and Kumar. We also show that the hedging level is lower than the one obtained by Akella and Kumar.     

Birnbaum‐Saunders autoregressive conditional range model applied to stock index data

This article proposes a new approach to the conditional autoregressive range (CARR) model using the Birnbaum‐Saunders (BS) distribution. The model aims to develop volatility clustering, which incorporates extreme fluctuations, using a time‐varying evolution of the range process called the BSCARR model. Furthermore, diagnosis analysis tools for diagnosis analysis were developed to evaluate the goodness of fit, such as residual analysis, global influence measures based on Cook's distance, and local influence analysis. For illustrative purposes, three real financial market indices are analyzed. A comparison with classical CARR models was also carried out in these examples. The results indicated that the proposed model outperformed some existing models in the literature, especially a recent CARR model based on the gamma distribution even under the presence of atypical cases (observed values).     

Unsupervised extreme learning machine and support vector regression hybrid model for predicting energy commodity futures index

Support vector regression (SVR) has been successfully applied in various domains, including predicting the prices of different financial instruments like stocks, futures, options, and indices. Because of the wide variation in financial time-series data, instead of using only a single standard prediction technique like SVR, we propose a hybrid model called USELM-SVR. It is a combination of unsupervised extreme learning machine (US-ELM)-based clustering and SVR forecasting. We assessed the feasibility and effectiveness of this hybrid model using a case study, predicting the one-, two-, and three-day ahead closing values of the energy commodity futures index traded on the Multi Commodity Exchange in India. Our experimental results show that the USELM-SVR is viable and effective, and produces better forecasts than our benchmark models (standard SVR, a hybrid of SVR with self-organizing map (SOM) clustering, and a hybrid of SVR with k-means clustering). Moreover, the proposed USELM-SVR architecture is useful as an alternative model for prediction tasks when we require more accurate predictions.     

Cooperative Hedging in Incomplete Markets

Abstract

The problem of (partial) hedging contingent claims for a single agent is well studied. This paper studies the problem for the multiagent case in incomplete markets. For this case, a cooperative hedging game is posed as follows: First, all agents contribute some money and collect the money together as the initial total capital, then invest the initial total capital in a trading strategy, and, finally, divide the terminal wealth of the trading strategy and each of them gets a part. We give a characterization of the optimal cooperative hedging strategy and prove that the core of the cooperative hedging game, as a cooperative game with side payment, is nonempty.     

Hedging for the long run

In the years following the publication of Black and Scholes (J Political Econ, 81(3), 637?C654, 1973), numerous alternative models have been proposed for pricing and hedging equity derivatives. Prominent examples include stochastic volatility models, jump-diffusion models, and models based on Lévy processes. These all have their own shortcomings, and evidence suggests that none is up to the task of satisfactorily pricing and hedging extremely long-dated claims. Since they all fall within the ambit of risk-neutral valuation, it is natural to speculate that the deficiencies of these models are (at least in part) attributable to the constraints imposed by the risk-neutral approach itself. To investigate this idea, we present a simple two-parameter model for a diversified equity accumulation index. Although our model does not admit an equivalent risk-neutral probability measure, it nevertheless fulfils a minimal no-arbitrage condition for an economically viable financial market. Furthermore, we demonstrate that contingent claims can be priced and hedged, without the need for an equivalent change of probability measure. Convenient formulae for the prices and hedge ratios of a number of standard European claims are derived, and a series of hedge experiments for extremely long-dated claims on the S&P 500 total return index are conducted. Our model serves also as a convenient medium for illustrating and clarifying several points on asset price bubbles and the economics of arbitrage.     

ARIMA模型与ARCH模型在香港股指预测方面的应用比较

由于香港金融市场易受政治或投机等因素的影响而使股价波动呈现出不确定性 ,而ARCH模型又善于描述这种不确定性 ,因而本文试图将ARCH模型和ARIMA模型在香港股指预测方面的应用进行对比 ,以期为投资者选择模型进行预测时提供参考     

Optimal Cross Hedging of Insurance Derivatives

Abstract

We consider insurance derivatives depending on an external physical risk process, for example, a temperature in a low dimensional climate model. We assume that this process is correlated with a tradable financial asset. We derive optimal strategies for exponential utility from terminal wealth, determine the indifference prices of the derivatives, and interpret them in terms of diversification pressure. Moreover, we check the optimal investment strategies for standard admissibility criteria. Finally, we compare the static risk connected with an insurance derivative to the reduced risk due to a dynamic investment into the correlated asset. We show that dynamic hedging reduces the risk aversion in terms of entropic risk measures by a factor related to the correlation.     

考虑结算风险的套期保值研究

期货市场的风险转移功能主要通过套期保值策略来实现,期货市场套期保值的关键问题是套期保值比率的确定。现有套期保值研究侧重于规避价格风险,忽略了期货市场另一个重要的风险因素-结算风险。本文通过建立考虑结算风险的期货套期保值决策模型,有效地平衡了套期保值过程中的价格风险与结算风险。具体特色一是将套保者的结算风险厌恶态度直接反映到套期比的计算中,体现了结算风险对套期保值决策的影响;二是在一定条件下,本模型的套期比趋近于最小方差套期比;三是利用ARMA时间序列方法预测期货与现货的价格走势,有效地反映了期货价格一阶平稳和季节性变化规律,使估计的套期比更加精确可靠。     

Hedging with temporary price impact

We consider the problem of hedging a European contingent claim in a Bachelier model with temporary price impact as proposed by Almgren and Chriss (J Risk 3:5–39, 2001). Following the approach of Rogers and Singh (Math Financ 20:597–615, 2010) and Naujokat and Westray (Math Financ Econ 4(4):299–335, 2011), the hedging problem can be regarded as a cost optimal tracking problem of the frictionless hedging strategy. We solve this problem explicitly for general predictable target hedging strategies. It turns out that, rather than towards the current target position, the optimal policy trades towards a weighted average of expected future target positions. This generalizes an observation of Gârleanu and Pedersen (Dynamic portfolio choice with frictions. Preprint, 2013b) from their homogenous Markovian optimal investment problem to a general hedging problem. Our findings complement a number of previous studies in the literature on optimal strategies in illiquid markets as, e.g., Gârleanu and Pedersen (Dynamic portfolio choice with frictions. Preprint, 2013b), Naujokat and Westray (Math Financ Econ 4(4):299–335, 2011), Rogers and Singh (Math Financ 20:597–615, 2010), Almgren and Li (Option hedging with smooth market impact. Preprint, 2015), Moreau et al. (Math Financ. doi: 10.1111/mafi.12098, 2015), Kallsen and Muhle-Karbe (High-resilience limits of block-shaped order books. Preprint, 2014), Guasoni and Weber (Mathematical Financ. doi: 10.1111/mafi.12099, 2015a; Nonlinear price impact and portfolio choice. Preprint, 2015b), where the frictionless hedging strategy is confined to diffusions. The consideration of general predictable reference strategies is made possible by the use of a convex analysis approach instead of the more common dynamic programming methods.     

Forecasting stock index price based on M-factors fuzzy time series and particle swarm optimization

In real time, one observation always relies on several observations. To improve the forecasting accuracy, all these observations can be incorporated in forecasting models. Therefore, in this study, we have intended to introduce a new Type-2 fuzzy time series model that can utilize more observations in forecasting. Later, this Type-2 model is enhanced by employing particle swarm optimization (PSO) technique. The main motive behind the utilization of the PSO with the Type-2 model is to adjust the lengths of intervals in the universe of discourse that are employed in forecasting, without increasing the number of intervals. The daily stock index price data set of SBI (State Bank of India) is used to evaluate the performance of the proposed model. The proposed model is also validated by forecasting the daily stock index price of Google. Our experimental results demonstrate the effectiveness and robustness of the proposed model in comparison with existing fuzzy time series models and conventional time series models.