股指期货的最优套期保值率实证研究——基于沪深300指数期货仿真交易视角

在分析和比较常用的几种股指期货最优套期保值比率确定模型的基础上,基于风险最小化模型框架,利用沪深300指数期货合约模拟运行以来的样本数据,通过最小二乘回归模型、向量自回归模型、误差修正模型以及广义自回归条件异方差模型四种估计方法,对其最优套期保值比率进行了实证测算和绩效比较,提出了相应的政策建议和投资策略。     

Crypto quanto and inverse options

Over 90% of exchange trading on crypto options has always been on the Deribit platform. This centralized crypto exchange only lists inverse products because they do not accept fiat currency. Likewise, other major crypto options platforms only list crypto–stablecoin trading pairs in so-called direct options, which are similar to the standard crypto options listed by the CME except the US dollar is replaced by a stablecoin version. Until now a clear mathematical exposition of these products has been lacking. We discuss the sources of market incompleteness in direct and inverse options and compare their pricing and hedging characteristics. Then we discuss the useful applications of currency protected “quanto” direct and inverse options for fiat-based traders and describe their pricing and hedging characteristics, all in the Black–Scholes setting.     

Selective hedging strategies for crude oil futures based on market state expectations

This paper studies the ex-ante selective hedging strategies of crude oil futures contracts based on market state expectations and compares the hedging performances to the traditional minimum variance routine hedging strategies. The main advantage of the proposed method is that it achieves a trade-off between return and risk, rather than hedges risk at all costs. Specifically, we first use a multi-input Hidden Markov Model(HMM) to identify the market state, assess the market’s herding impact, and then integrate the findings of identification and measurement to forecast the price trend. We offer an adjustment criterion for the hedge ratios driven by GARCH²-type models based on the anticipated market state. We conducted an empirical analysis to examine the hedging effect of WTI and Brent crude oil futures, the results indicate that the proposed state-dependent hedging strategies are superior to the traditional model-driven hedging strategies concerning the hedged portfolio based on four criteria. The robustness check reveals that the proposed hedging strategies still outperform in different market situation. The findings can help traders in the crude oil markets, and the methodology can be applied to other energy markets.     

Use of high-frequency data to evaluate the performance of dynamic hedging strategies

The hedging performance results of generalized autoregressive conditional heteroskedasticity models are mixed; we address this herein by adopting an asymptotic setting to determine the relative performance of competing hedge ratios. The proxy variable is constructed through precise realized measures rather than through noisy squared returns because the substitution of the latent true hedged portfolio variance with a noisy proxy renders the loss function incapable of ranking forecasts consistently. The merits of allowing some features in modeling the spot–futures distribution are assessed. Empirical comparisons suggest that hedgers may favor the wrong model when the quality of the proxy variable deteriorates.     

On the economics of the longevity risk transfer market

We present a model of a longevity risk transfer market with different market players (primary insurers, reinsurers, and capital market investors) and investigate how market dynamics and the market players' roles evolve with progressing market saturation. We find that reinsurers' appetite for longevity risk is the key driver in the early stage of market development. Since diversification benefits with other businesses decrease with every transaction, the reinsurance market is intrinsically antimonopolistic. With the increasing saturation of the reinsurance sector as a whole, its competitiveness shrinks leading to rising expected risk-adjusted returns for capital market investors. We show that in a saturated market, reinsurers should assume the entire longevity risk from primary insurers, diversify it within their business mix, and subsequently pass on only specific (nondiversifiable) components of the longevity risk to the capital markets. Our findings provide valuable suggestions on how to make the best use of the market's limited risk absorption capacity.     

Neural network approximation for superhedging prices

This article examines neural network-based approximations for the superhedging price process of a contingent claim in a discrete time market model. First we prove that the α-quantile hedging price converges to the superhedging price at time 0 for α tending to 1, and show that the α-quantile hedging price can be approximated by a neural network-based price. This provides a neural network-based approximation for the superhedging price at time 0 and also the superhedging strategy up to maturity. To obtain the superhedging price process for $t>0$, by using the Doob decomposition, it is sufficient to determine the process of consumption. We show that it can be approximated by the essential supremum over a set of neural networks. Finally, we present numerical results.     

Corporate reputation and hedging activities

This study examines whether corporate reputation affects derivative hedging. We posit that high-reputation firms are more likely to engage in hedging due to greater reputation costs and/or their commitment to lower financial risks. We find that high-reputation firms are more likely to engage in hedging, especially when their hedging efforts or effects are more observable to stakeholders. We also find that high-reputation firms are less likely to disclose the notional values of hedging positions and that interest rate hedging by high-reputation firms is detrimental to firm value. Our results shed light on the impact of reputational concerns on corporate risk management and disclosure policies.     

衍生品交易盈亏披露会影响瞬时股价吗？——基于2020年A股上市公司的分析

对2020年A股上市公司披露信息中含衍生品交易盈亏信息的数据进行多期双重差分分析，发现衍生品信息无论利好还是利空，皆存在显著负面的公告溢价。衍生品披露当日瞬时股价、3～5个交易日窗口期的累计异常收益率显著下跌，且衍生品信息不会显著影响该股票长期价值。细分披露类型发现，定期披露的衍生品信息导致的瞬时股价波动较小，且具有更显著的公告盈余漂移。     

Multi-step barrier products and static hedging

This paper examines multi-step barrier options with an arbitrary payoff function using extended static hedging methods. Although there have been studies using extended reflection principles to obtain joint distribution functions for barrier options with complex barrier conditions, and static hedging methods to evaluate limited barrier options with well-known payoff functions, we obtain an explicit expression of barrier option price which has a general payoff function under the Black–Scholes framework assumption. The explicit multi-step barrier options prices we discuss in this paper are not only useful in that they can handle different levels and time steps barrier and all types of payoff functions, but can also extend to pricing of barrier options under finite discrete jump–diffusion models with a simple barrier. In the last part, we supplement the theory with numerical examples of various multi-step barrier options under the Black–Scholes or discrete jump–diffusion model for comparison purposes.     

Deep empirical risk minimization in finance: Looking into the future

Many modern computational approaches to classical problems in quantitative finance are formulated as empirical loss minimization (ERM), allowing direct applications of classical results from statistical machine learning. These methods, designed to directly construct the optimal feedback representation of hedging or investment decisions, are analyzed in this framework demonstrating their effectiveness as well as their susceptibility to generalization error. Use of classical techniques shows that over-training renders trained investment decisions to become anticipative, and proves overlearning for large hypothesis spaces. On the other hand, nonasymptotic estimates based on Rademacher complexity show the convergence for sufficiently large training sets. These results emphasize the importance of synthetic data generation and the appropriate calibration of complex models to market data. A numerically studied stylized example illustrates these possibilities, including the importance of problem dimension in the degree of overlearning, and the effectiveness of this approach.