Double continuation regions for American and Swing options with negative discount rate in Lévy models

In this paper, we study perpetual American call and put options in an exponential Lévy model. We consider a negative effective discount rate that arises in a number of financial applications including stock loans and real options, where the strike price can potentially grow at a higher rate than the original discount factor. We show that in this case a double continuation region arises and we identify the two critical prices. We also generalize this result to multiple stopping problems of Swing type, that is, when successive exercise opportunities are separated by i.i.d. random refraction times. We conduct an extensive numerical analysis for the Black–Scholes model and the jump‐diffusion model with exponentially distributed jumps.     

Semitractability of optimal stopping problems via a weighted stochastic mesh algorithm

In this paper, we propose a Weighted Stochastic Mesh (WSM) algorithm for approximating the value of discrete‐ and continuous‐time optimal stopping problems. In this context, we consider tractability of such problems via a useful notion of semitractability and the introduction of a tractability index for a particular numerical solution algorithm. It is shown that in the discrete‐time case the WSM algorithm leads to semitractability of the corresponding optimal stopping problem in the sense that its complexity is bounded in order by with being the dimension of the underlying Markov chain. Furthermore, we study the WSM approach in the context of continuous‐time optimal stopping problems and derive the corresponding complexity bounds. Although we cannot prove semitractability in this case, our bounds turn out to be the tightest ones among the complexity bounds known in the literature. We illustrate our theoretical findings by a numerical example.     

选择数量对满意度的影响——一个有调节的中介模型

增加选择数量是否会提高满意度是经济学、心理学和营销学的热点问题。当消费者对某一刺激目标同时存在积极和消极的评价时，即矛盾态度怎样影响选择数量和满意度的关系？2个实验结果表明，选择数量对满意度的影响是通过决策困难水平实现的，当选择数量影响决策困难水平时，会产生选择超载。矛盾态度对决策困难水平与满意度的关系有调节作用，对矛盾态度较高者，选择“多多益善”，选择数量增加使消费者满意度提高；相反，对矛盾态度较低者，选择“适可而止”，选择数量增加使消费者满意度降低。     

Good jump,bad jump,and option valuation

I develop a new class of closed‐form option pricing models that incorporate variance risk premium and symmetric or asymmetric double exponential jump diffusion. These models decompose the jump component into upward and downward jumps using two independent exponential distributions and thus capture the impact of good and bad news on asset returns and option prices. The empirical results show that the model with an asymmetric double exponential jump diffusion improves the fit on Shanghai Stock Exchange 50ETF returns and options and provides relatively better in‐ and out‐of‐sample pricing performance.     

Relevance of the disposition effect on the options market: New evidence

A moneyness‐based propensity to sell (MPS) measure, at the aggregate level, determines the propensity of option holders to exercise their winning relative to losing positions. Using data on individual stock and S&P 500 Index options, we find that the MPS measure has significant predictive power over the cross section of delta‐hedged option returns. We test the disposition effect in the options market based on a long–short strategy that exploits price distortions induced by the disposition bias. More pronounced evidence of the disposition bias is found for individual at‐the‐money call options than put options where the significance of abnormal returns remains robust across different subsamples even after we control for the portfolio option greeks and market‐based risk factors. The profitability of the long–short strategy is related to limit‐to‐arbitrage proxies suggesting that behavioral explanations help explain the positive relation between the MPS measure and delta‐hedged option returns.     

Machine learning for pricing American options in high-dimensional Markovian and non-Markovian models

In this paper we propose two efficient techniques which allow one to compute the price of American basket options. In particular, we consider a basket of assets that follow a multi-dimensional Black–Scholes dynamics. The proposed techniques, called GPR Tree (GRP-Tree) and GPR Exact Integration (GPR-EI), are both based on Machine Learning, exploited together with binomial trees or with a closed form formula for integration. Moreover, these two methods solve the backward dynamic programing problem considering a Bermudan approximation of the American option. On the exercise dates, the value of the option is first computed as the maximum between the exercise value and the continuation value and then approximated by means of Gaussian Process Regression. The two methods mainly differ in the approach used to compute the continuation value: a single step of the binomial tree or integration according to the probability density of the process. Numerical results show that these two methods are accurate and reliable in handling American options on very large baskets of assets. Moreover we also consider the rough Bergomi model, which provides stochastic volatility with memory. Despite that this model is only bidimensional, the whole history of the process impacts on the price, and how to handle all this information is not obvious at all. To this aim, we present how to adapt the GPR-Tree and GPR-EI methods and we focus on pricing American options in this non-Markovian framework.     

Modelling economic hysteresis losses caused by sunk adjustment costs

Transition from one economic equilibrium to another as a consequence of shocks is often associated with sunk adjustment costs. Firm-specific sunk market entry investments (or sunk market exit costs) in case of a reaction to price shocks are an example. These adjustment costs lead to a dynamic supply pattern similar to hysteresis. In analogy to “hysteresis losses” in ferromagnetism, the authors explicitly model dynamic adjustment losses in the course of market entry and exit cycles. They start from the micro level of a single firm and use explicit aggregation tools from hysteresis theory in mathematics and physics to calculate dynamic losses. The authors show that strong market fluctuations generate disproportionately large hysteresis losses for producers. This could give a reason for the implementation of stabilizing measures and policies to prevent strong (price) variations or, alternatively, to reduce the sunk entry and exit costs.     

A generalized algorithm for duration and convexity of option embedded bonds

This article derives a generalized algorithm for duration and convexity of option embedded bonds that provides a convenient way of estimating the dollar value of 1 basis point change in yield known as DV01, an important metric in the bond market. As delta approaches 1, duration of callable bonds approaches zero once the bond is called. However, when the delta is zero, the short call is worthless and duration of callable will be equal to that of a straight bond. On the other hand, the convexity of a callable bond follows the same behaviour when the delta is 1 as shown in Dunetz and Mahoney (1988) as well as in Mehran and Homaifar’s (1993) derivations. However, in the case when delta is zero, the convexity of a callable bond approaches zero as well, which is in stark contrast to the non-zero convexity derived in Dunetz and Mahoney’s paper. Our generalized algorithm shows that duration and convexity nearly symmetrically underestimate (overestimate) the actual price change by 11/10 basis points for ± 100 basis points change in yield. Furthermore, our algorithm reduces to that of MH for convertible bonds assuming the convertible bond is not callable.     

使用粒子群算法解决期权定价模型参数校准问题——以heston模型为例

期权定价模型的参数校准问题是一个常见的难题,以heston 模型为例,定价时需要估计6 个参数,参数估计问题实质上是高维非线性规划问题,由于估参函数的性质不好,一般的估参方法常常失效。使用粒子群（PSO）智能算法可以改善该模型的参数校准问题,因为粒子群算法具有内在随机性,因此参数估计中的局部极小值问题可以被较好地解决。使用2017 年12 月20 日的香港恒生指数期权作为估计样本,并对2017 年12 月25 日的期权进行样本外预测,数值结果表明使用heston 模型对期权进行定价并配合粒子群算法估计参数具有良好的定价效果。