Exchange options for catastrophe risk management

In this paper, we investigate the pricing issue and catastrophe risk management of exchange options. Exchange options allow the holder to exchange its stocks for another at maturity and can be seen as an extended version of catastrophe equity put options with another traded asset price as strike prices. Since option holders have to issue new shares to exercise the option, we illustrate the differences between option prices calculated using pre-exercise and post-exercise share prices. The effects of default risk on option prices and risk management are also considered. Finally, risk management analysis shows that exchange options can effectively hedge catastrophe risk.     

Non-transferable non-hedgeable executive stock option pricing

To value non-transferable non-hedgeable (NTNH) contingent claims and price executive stock options (ESOs), we use a replication argument to translate portfolios with NTNH derivatives into portfolios of primary assets (only) with stochastic portfolio constraints. By identifying stochastic discount factors and finding subjective prices of NTNH European and American ESOs, for block and continuous partial exercise, we derive executives׳ optimal exercise policies, and use these to find objective prices/costs of ESOs to firms. Through numerical simulations, we obtain policy implications regarding ESOs׳ incentivizing efficiency. For the first time, we demonstrate that, unlike under block exercise, subjective prices under continuous partial exercise may be higher than objective ones. Moreover, volatility regimes and executives׳ “other wealth” are important in ESO pricing, and are thus essential to empirical executive compensation studies.     

Implied Sharpe ratios of portfolios with options: Application to Nikkei futures and listed options

We propose a criterion for portfolio selection, implied excess Sharpe ratio. The implied excess Sharpe ratio is intended as an excess Sharpe ratio (versus the underlying stock) that investors can expect to enjoy from portfolios that include options and is a useful ex ante indicator that can be easily calculated. There are a variety of ways to include options in a portfolio, but we theoretically show that the combination that produces the largest implied excess Sharpe ratio is the best way to maximize the short-term Sharpe ratio. The selection process uses implied excess Sharpe ratio, which is easily calculated from stock lending fees implied by stock prices and actual stock lending fee. It does not require historical simulation or prediction of share price average growth rates and is highly transparent as it can be easily reproduced (at a low calculation cost). Hence, the implied excess Sharpe ratio is a simple but effective tool for investors seeking returns in exchange for a certain amount of risk that want to use the options market efficiently. The short-term Sharpe ratio is not necessarily the only criterion, but is a rational benchmark of portfolio performance closely related to criteria such as the long-term Sharpe ratio and maximum drawdown. To examine the benefit of the concept, we construct an investment strategy that automatically selects from multiple candidate portfolios that are made up of combinations of Nikkei futures and Nikkei listed options the portfolio with the largest implied excess Sharpe ratio. Back-testing shows that this investment strategy performs well over the long term as well.     

Valuation of piecewise linear barrier options

This paper discusses the valuation of piecewise linear barrier options that generalize classical barrier options. We establish formulas for joint probabilities of the logarithmic returns of the underlying asset and its partial running maxima when the process has a piecewise constant drift. In particular, we show that our results embrace the famous reflection principle as a special case, and that our established proposition delivers useful scalability for computing desired probabilities related to various types of barriers. We derive the closed-form prices of piecewise linear barrier options under the Black–Scholes framework, which are obtainable with little effort by relying on the derived probabilities. In addition, we provide numerical examples and discuss how option prices respond to several types of piecewise linear barriers.     

Option pricing and replication with transaction costs and dividends

This paper derives optimal perfect hedging portfolios in the presence of transaction costs within the binomial model of stock returns, for a market maker that establishes bid and ask prices for American call options on stocks paying dividends prior to expiration. It is shown that, while the option holder's optimal exercise policy at the ex-dividend date varies according to the stock price, there are intervals of values for such a price where the optimal policy would depend on the holder's preferences. Nonetheless, the perfect hedging assumption still allows the derivation of optimal hedging portfolios for both long and short positions of a market maker on the option.     

The values and incentive effects of options on the maximum or the minimum of the stock prices and market index

In this study, we employ the certainty equivalent principle to investigate cost efficiency and incentives of the options on the maximum or the minimum of the stock prices and market index levels. In addition, the options with averaging features are also considered. Numerical results show that options on the maximum are more cost efficient and incentive-efficient than traditional ones. As for options on the minimum, they are more cost efficient than traditional ones only when the weight in the options is not very large. However, options on the minimum also provide stronger incentives to increase stock prices than traditional ones.     

Pricing vulnerable options with stochastic liquidity risk

In this paper, we consider vulnerable options with stochastic liquidity risk. We employ liquidity-adjusted pricing models to describe the underlying stock price and option issuer’s assets. In addition, the correlation between these assets is stochastic, depending on the market liquidity measures. In the proposed framework, we derive closed forms of vulnerable European options with stochastic liquidity risk and then use them to illustrate the effects of stochastic liquidity risk on vulnerable option prices. Numerical results show that the effects of liquidity risk on the prices of out-of-the-money options or the options with a short maturity are not negligible.     

A note on the implied volatility of floating strike Asian options

Decisions in Economics and Finance - In this paper, we study the short-time behavior of the implied volatility for short-time floating strike Asian options. Our method is based on Malliavin...     

Option-based risk management of a bond portfolio under regime switching interest rates

In the present paper, we assume an economy with regime switching short rates and show how the Value at Risk of a financial position on zero-coupon bonds, hedged by buying protective put options under budget constraints, can be minimized by selecting optimal (regime-dependent) strike prices.     

Forecasting expected shortfall: Should we use a multivariate model for stock market factors?

Is univariate or multivariate modeling more effective when forecasting the market risk of stock portfolios? We examine this question in the context of forecasting the one-week-ahead expected shortfall of a stock portfolio based on its exposure to the Fama–French and momentum factors. Applying extensive tests and comparisons, we find that in most cases there are no statistically significant differences between the forecasting accuracy of the two approaches. This result suggests that univariate models, which are more parsimonious and simpler to implement than multivariate factor-based models, can be used to forecast the downside risk of equity portfolios without losses in precision.     

Volatility estimation from observed option prices

It is well established that the standard Black-Scholes model does a very poor job in matching the prices of vanilla European options. The implied volatility varies by both time to maturity and by the moneyness of the option. One approach to this problem is to use the market option prices to back out a local volatility function that reproduces the market prices. Since option price observations are only available for a limited set of maturities and strike prices, most algorithms require a smoothing technique to implement this approach. In this paper we modify the implementation of Andersen and Brotherton-Ratcliffe to provide another way of dealing with this issue. Numerical examples indicate that our approach is reasonably successful in reproducing the input prices.     

Multidimensional quasi-Monte Carlo Malliavin Greeks

The aim of this paper is extensively investigate the performance of the estimators for the Greeks of multidimensional complex path-dependent options obtained by the aid of Malliavin Calculus. The study analyses both the computation effort and the variance reduction in the Quasi-Monte Carlo simulation framework. For this purpose, we adopt the approach employed by Montero and Kohatsu-Higa to the multidimensional case. The multidimensional setting shows the convenience of the Malliavin Calculus approach over different techniques that have been previously proposed. Indeed, these techniques may be computationally expensive and do not provide enough flexibility for variance reduction. In contrast, the Malliavin approach provides a class of functions that return the same expected value (the Greek) with different accuracies. This versatility for variance reduction is not possible without the use of the generalized integral by part formula of Malliavin Calculus. In the multidimensional context, we find convenient formulas that permit to improve the localization technique, introduced in Fournié et al. and reduce both the computational cost and the variance. Moreover, we show that the parameters for the variance reduction can be obtained on the flight in the simulation. We illustrate the efficiency of the proposed procedures, coupled with the enhanced version of Quasi-Monte Carlo simulations as discussed in Sabino, for the numerical estimation of the Deltas of call, digital Asian-style and exotic basket options with a fixed and a floating strike price in a multidimensional Black-Scholes market. Given the fact that the gammas of a call option coincides, apart from a constant, with the deltas of digital options, this setting also covers the analysis of formulas tailored for the second order Greeks of call options.     

Valuation of options on the maximum of two prices with default risk under GARCH models

In this paper, we work under GARCH models to value options on the maximum or the minimum of two prices. In addition, we consider not only two underlying asset prices but also geometric average ones. Further, default risk is also incorporated in a reduced-form model. In the proposed framework, closed-form pricing formulae of options on the maximum with or without default risk are derived and then used to perform numerical examples.     

Optimal multiple stopping models of reload options and shout options

The reload provision in an employee stock option entitles its holder to receive one new (reload) option from the employer for each share tendered as the payment of strike upon the exercise of the stock option. The number of reloads allowed can be finite or infinite. The shout feature in a call option allows its holder to reset the option's strike price to the prevailing stock price upon shouting. We explore a symmetry relationship between the price functions of the employee reload options and shout call options. When the dividend yield of the underlying stock is zero, the value of the employee reload option can be expressed in terms of the price functions of a shout call option and a forward contract. For an employee reload option with an infinite number of allowable reloads, the payoff of the employee reload option can be related to the lookback feature of the stock price process. We also examine the optimal exercise policies of the multi-reload employee stock options. The behavior of the critical stock price at which the holder should exercise optimally is shown to depend on the relative magnitude of the dividend yield, interest rate and volatility. Our analysis of the multi-reload and multi-shout options contributes to the literature on optimal stopping policies of contingent claims with multiple stopping rights.     

Market selection of constant proportions investment strategies in continuous time

This paper studies the wealth dynamics of investors holding self-financing portfolios in a continuous-time model of a financial market. Asset prices are endogenously determined by market clearing. We derive results on the asymptotic dynamics of the wealth distribution and asset prices for constant proportions investment strategies. This study is the first step towards a theory of continuous-time asset pricing that combines concepts from mathematical finance and economics by drawing on evolutionary ideas.     

Lasso-based index tracking and statistical arbitrage long-short strategies

In this paper, we apply the lasso-type regression to solve the index tracking (IT) and the long-short investing strategies. In both cases, our objective is to exploit the mean-reverting properties of prices as reported in the literature. This method is an interesting technique for portfolio selection due to its capacity to perform variable selection in linear regression and to solve high-dimensional problems (which is the case if we consider broader indexes such as the S&P 500 or the Russell 1000). We use lasso to solve IT and long-short with three market benchmarks (S&P 100 and Russell 1000 – US stock market; and Ibovespa – Brazilian market), comprising data from 2010 to 2017. Also, we formed IT portfolios using cointegration (a method widely used for index tracking) to have a basis for comparison of the results using lasso. The findings for IT showed similar overall performance between portfolios using lasso and cointegration, with a slight advantage to cointegration in some cases. Nonetheless, lasso-based IT portfolios presented average monthly turnover at least 40% smaller, indicating that lasso generated portfolios that had not only a consistent tracking performance but also a considerable advantage in terms of transaction costs (represented by the average turnover).     

House prices and consumer welfare

We develop a new approach to measuring changes in consumer welfare due to changes in the price of owner-occupied housing. In our approach, an agent's welfare adjustment is defined as the transfer required to keep expected discounted utility constant given a change in current house prices. We demonstrate that, up to a first-order approximation, there is no aggregate change in welfare due to price increases in the existing housing stock. This follows from a simple market clearing condition where capital gains experienced by sellers are exactly offset by welfare losses to buyers. We show that this result holds (approximately) even in a model that accounts for changes in consumption and investment plans prompted by current house price changes. There can, however, be changes in welfare due to additions to the stock of housing, or to changes in the price of renovating and upgrading the existing stock of housing. For the United States, we estimate the welfare cost of house price appreciation to be an average of $127 per household per year over the 1984–1998 period.     

Hedging inflation with individual US stocks: A long-run portfolio analysis

This paper examines whether individual stocks can act as inflation hedgers. We focus on longer investment horizons and construct in- and out-of-sample portfolios based on the long-run relationship (cointegration) of stock prices with respect to consumer prices. Empirical evidence suggests that investors are better off by holding a portfolio of stocks with higher long-run betas as part of asset selection and allocation strategy. Stocks that outperform inflation tend to be drawn from the Energy and Industrial sectors. Finally, we observe that the companies average inflation hedging ability declined steadily over the past ten years, while the number of firms that hedge inflation has decreased considerably after the recent downturn of the US economy.     

Optimal consumption and investment under partial information

We consider a stock market model where prices satisfy a stochastic differential equation with a stochastic drift process. The investor’s objective is to maximize the expected utility of consumption and terminal wealth under partial information; the latter meaning that investment decisions are based on the knowledge of the stock prices only. We derive explicit representations of optimal consumption and trading strategies using Malliavin calculus. The results apply to both classical models for the drift process, a mean reverting Ornstein-Uhlenbeck process and a continuous time Markov chain. The model can be transformed to a complete market model with full information. This allows to use results on optimization under convex constraints which are used in the numerical part for the implementation of more stable strategies. Supported by the Austrian Science Fund FWF, project P17947-N12. We thank two anonymous referees for their comments which led to a considerable improvement of the paper.     

A Model Confidence Set approach to the combination of multivariate volatility forecasts

In predicting conditional covariance matrices of financial portfolios, practitioners are required to choose among several alternative options, facing a number of different sources of uncertainty. A first source is related to the frequency at which prices are observed, either daily or intradaily. Using prices sampled at higher frequency inevitably poses additional sources of uncertainty related to the selection of the optimal intradaily sampling frequency and to the construction of the best realized estimator. Likewise, the choices of model structure and estimation method also have a critical role. In order to alleviate the impact of these sources of uncertainty, we propose a forecast combination strategy based on the Model Confidence Set [MCS] to adaptively identify the set of most accurate predictors. The combined predictor is shown to achieve superior performance with respect to the whole model universe plus three additional competitors, independently of the MCS or portfolio settings.