Valuation of vulnerable American options with correlated credit risk

This article evaluates vulnerable American options based on the two-point Geske and Johnson method. In accordance with the Martingale approach, we provide analytical pricing formulas for European and multi-exercisable options under risk-neutral measures. Employing Richardson’s extrapolation gets the values of vulnerable American options. To demonstrate the accuracy of our proposed method, we use numerical examples to compare the values of vulnerable American options from our proposed method with the benchmark values from the least-square Monte Carlo simulation method. We also perform sensitivity analyses for vulnerable American options and show how the prices of vulnerable American options vary with the correlation between the underlying assets and the option writer’s assets.      

A semi-analytic method for valuing high-dimensional options on the maximum and minimum of multiple assets

Valuing high-dimensional options has many important applications in finance but when the true distributions are unknown or complex, numerical approximations must be used. Approximation methods based on Monte-Carlo simulation show a steep trade-off between estimation accuracy and computational efficiency. This article presents an alternative semi-analytic approximation method for pricing options on the maximum or minimum of multiple assets with unknown distributions. Computational efficiency is shown to improve significantly without sacrificing estimation accuracy. The method is illustrated with applications to options on underlying assets with mean-reverting prices, time-dependent correlations, and stochastic volatility The authors would like to thank the two anonymous referees, the associate editor, and Dr. Jess H. Chua at the University of Calgary for valuable comments and insights on this research. This research was partly supported by NUS grant R-146-000-059-112     

Assessing the Least Squares Monte-Carlo Approach to American Option Valuation

A detailed analysis of the Least Squares Monte-Carlo (LSM) approach to American option valuation suggested in Longstaff and Schwartz (2001) is performed. We compare the specification of the cross-sectional regressions with Laguerre polynomials used in Longstaff and Schwartz (2001) with alternative specifications and show that some of these have numerically better properties. Furthermore, each of these specifications leads to a trade-off between the time used to calculate a price and the precision of that price. Comparing the method-specific trade-offs reveals that a modified specification using ordinary monomials is preferred over the specification based on Laguerre polynomials. Next, we generalize the pricing problem by considering options on multiple assets and we show that the LSM method can be implemented easily for dimensions as high as ten or more. Furthermore, we show that the LSM method is computationally more efficient than existing numerical methods. In particular, when the number of assets is high, say five, Finite Difference methods are infeasible, and we show that our modified LSM method is superior to the Binomial Model.     

发电企业上市公司引用股票期权激励机制探讨

分析了股票期权激励制度给上市公司带来的效益以及在中国的应用模式,并按九个方面阐述了股票期权激励制度设计。     

Timing of earnings restatements: CEO equity compensation and market reaction

This study investigates the impact of chief executive officers’ (CEO) compensation on their choices regarding the timing of earnings restatements. The results indicate a negative relationship between options exercised and lags in disclosing the restated earnings, suggesting that managers who exercise options in a given year tend to release information quickly. This effect is more pronounced if the options are exercised after the dark period. We also find that the market penalises longer lags in the restatement disclosure. It seems that the CEO would try to optimise the timing of information release so as to balance the costs and benefits.     

The implications,challenges and benefits of a complexity-orientated Futures Studies

Complexity science is increasingly cited as an essential component of a Futures Studies (FS) capable of assisting with the wide-ranging and complex societal problems of the 21st century. Yet, the exact implications of complexity science for FS remain somewhat opaque. This paper explicitly sets out the challenges for FS that arise from six complexity science concepts: (1) irreversibility of time (2) path dependence 3) sensitivity to initial conditions (4) emergence and systemness (5) attractor states (6) complex causation. The discussion highlights the implications of these challenges for FS tools such as horizon scanning and weak signals, and sets out the benefits of overcoming the challenges to create an explicitly complexity-orientated FS. The discussion concludes with a set of questions summarising the challenge for FS from complexity science with the aim of stimulating a discussion as to how they can be met. The concluding remarks make some initial suggestions in this regard.     

Metaphors,semiotics and futures studies

Metaphor belongs to key concepts of semiotics. I have made my career in the field of semiotics and I appreciate the possibility to tell to the scientific community of futurists how a semiotician sees the various functions of metaphors and their connections to the future. The edited volume CLA 2.0 (Inayatullah & Milojevic, 2015) shows that in addition to metaphors, many futures researchers have found the general language-based approach of semiotics. The paper deals with three issues: first the theory of metaphors as such, much discussed in the semiotic literature; then what semiotics says about the future; and finally, what kind of semiotics we are considering here. I would propose to scrutinise the problem of metaphors and future in light of my own new theory which I call ‘existential semiotics’.     

REAL OPTIONS AND THE CROSS‐SECTION OF EXPECTED STOCK RETURNS

This paper surveys the theoretical literature investigating the effect of firms’ investment flexibility on the cross‐section of expected stock returns. Real options analysis derives firms’ value‐maximizing investment policies as functions of exogenous fundamental drivers of profitability and calculates firms’ market values as functions of the same variables. These functions yield the relationship between expected stock returns and firm fundamentals. Several plausible explanations for the value premium – the high average stock returns earned by firms with high book‐to‐market ratios – emerge from this literature.     

Real options and contingent convertibles with regime switching

We consider a firm with no assets in place but an option to invest in a project. The investment is irreversible but delayable in a regime-switching economy. The firm issues equity, straight bonds (SBs) and contingent convertibles (CoCos). We provide the closed-form prices for the firm׳s securities and the pricing and timing of the option. Our numerical analyses discover that issuing CoCos instead of SBs induces much less agency cost of debt. The agency cost is higher in a boom economy than in recession but the difference is small. There is a unique CoCos׳ conversion ratio such that the agency cost arrives at the minimum value zero. The inefficiencies arising from asset substitution and debt overhang are much more significant in recession than in boom. Only if the conversion ratio is not too small, the two inefficiencies disappear during boom periods. While the effects of the conversion rate on optimal capital structure and firm value and those of supervision and jump intensity on optimal CoCos׳ coupon are ambiguous and weak, the stricter the supervision or the longer the economy remains in recession, the less the option value and the optimal SBs׳ coupon.     

How much is the gap?—Efficient jump risk-adjusted valuation of leveraged certificates

This paper develops a novel and highly efficient numerical algorithm for the gap risk-adjusted valuation of leveraged certificates. The existing literature relies on Monte Carlo simulations, which are not fast enough to be used in a market-making environment. This is because issuers need to compute thousands of price updates per second. By valuing leveraged certificates as multi-window barrier options, we explicitly model random jumps that occur at known times, such as between the exchange closing and re-opening. Our algorithm combines the one-day transition probability with Simpson’s numerical integration rule. This yields a backward induction scheme which requires a significantly coarser spatial and time grid than finite-difference methods. We confirm its robustness and accuracy through Monte Carlo simulations.