HOW CLOSE ARE THE OPTION PRICING FORMULAS OF BACHELIER AND BLACK–MERTON–SCHOLES?

We compare the option pricing formulas of Louis Bachelier and Black–Merton–Scholes and observe—theoretically as well as for Bachelier's original data—that the prices coincide very well. We illustrate Louis Bachelier's efforts to obtain applicable formulas for option pricing in pre-computer time. Furthermore we explain—by simple methods from chaos expansion—why Bachelier's model yields good short-time approximations of prices and volatilities.     

Small‐time,large‐time,and asymptotics for the Rough Heston model

We characterize the behavior of the Rough Heston model introduced by Jaisson and Rosenbaum (2016, Ann. Appl. Probab., 26, 2860–2882) in the small‐time, large‐time, and (i.e., ) limits. We show that the short‐maturity smile scales in qualitatively the same way as a general rough stochastic volatility model , and the rate function is equal to the Fenchel–Legendre transform of a simple transformation of the solution to the same Volterra integral equation (VIE) that appears in El Euch and Rosenbaum (2019, Math. Financ., 29, 3–38), but with the drift and mean reversion terms removed. The solution to this VIE satisfies a space–time scaling property which means we only need to solve this equation for the moment values of and so the rate function can be efficiently computed using an Adams scheme or a power series, and we compute a power series in the log‐moneyness variable for the asymptotic implied volatility which yields tractable expressions for the implied vol skew and convexity which is useful for calibration purposes. We later derive a formal saddle point approximation for call options in the Forde and Zhang (2017) large deviations regime which goes to higher order than previous works for rough models. Our higher‐order expansion captures the effect of both drift terms, and at leading order is of qualitatively the same form as the higher‐order expansion for a general model which appears in Friz et al. (2018, Math. Financ., 28, 962–988). The limiting asymptotic smile in the large‐maturity regime is obtained via a stability analysis of the fixed points of the VIE, and is the same as for the standard Heston model in Forde and Jacquier (2011, Finance Stoch., 15, 755–780). Finally, using Lévy's convergence theorem, we show that the log stock price tends weakly to a nonsymmetric random variable as (i.e., ) whose moment generating function (MGF) is also the solution to the Rough Heston VIE with , and we show that tends weakly to a nonsymmetric random variable as , which leads to a nonflat nonsymmetric asymptotic smile in the Edgeworth regime, where the log‐moneyness as , and we compute this asymptotic smile numerically. We also show that the third moment of the log stock price tends to a finite constant as (in contrast to the Rough Bergomi model discussed in Forde et al. (2020, Preprint) where the skew flattens or blows up) and the process converges on pathspace to a random tempered distribution which has the same law as the hyper‐rough Heston model, discussed in Jusselin and Rosenbaum (2020, Math. Finance, 30, 1309–1336) and Abi Jaber (2019, Ann. Appl. Probab., 29, 3155–3200).     

Response to the Letter to the Editor on ‘On Quantile-based Asymmetric Family of Distributions: Properties and Inference’

Rubio (2020) points out an identification problem for the four-parameter family of two-piece asymmetric densities introduced by Nassiri & Loris (2013). This implies that statistical inference for that family is problematic. Establishing probabilistic properties for this four-parameter family however still makes sense. For the three-parameter family, there is no identification problem. The main contribution in Gijbels et al. (2019a) is to provide asymptotic results for maximum likelihood and method-of-moments estimators for all members of the three-parameter quantile-based asymmetric family of distributions.     

Lifetime investment and consumption with recursive preferences and small transaction costs

We investigate the effects of small proportional transaction costs on lifetime consumption and portfolio choice. The extant literature has focused on agents with additive utilities. Here, we extend this analysis to the archetype of nonadditive preferences: the isoelastic recursive utilities proposed by Epstein and Zin.     

Interview with Nancy Reid

Nancy Reid was born in September 1952 in Niagara Falls, Canada. She graduated from the University of Waterloo with a Bachelor in Mathematics and a major in Statistics in 1974. She pursued her training in Statistics at the University of British Columbia (UBC) where she obtained a Master's in Applied Mathematics in 1976 and at Stanford University, where she graduated with a PhD in Statistics in 1979. After spending one year at Imperial College in London visiting Sir David Cox, she joined UBC as an Assistant Professor in the Department of Mathematics, where she had an important role in the creation of the Department of Statistics. In 1986, she moved to the University of Toronto, where she has been since then as a faculty in the Department of Statistics. Nancy has served as Chair of the Department between 1997 and 2002. Nancy's research in conditional inference, higher order asymptotics and composite likelihood has been influential in Statistics. Her outstanding contributions to Statistics were recognized nationally and internationally with many awards, including the President's Award of the Committee of Presidents of Statistical Societies (COPSS), Gold Medal awarded by the Statistical Society of Canada and Elected Foreign Associate of the National Academy of Sciences. She received the Doctor of Mathematics, Honoris Causa, University of Waterloo. Nancy served with distinction as Editor of the Canadian Journal of Statistics and President of the Statistical Society of Canada and President of the Institute of Mathematical Statistics. In 2014, she was appointed as Officer of the Order of Canada for her outstanding achievements, exemplary leadership and service to Canadians. The following conversation took place at the JSM 2016 in Chicago, on August 2 and 3, 2016.     

A ruin model with a resampled environment

ABSTRACT

This paper considers a Cramér–Lundberg risk setting, where the components of the underlying model change over time. We allow the more general setting of the cumulative claim process being modeled as a spectrally positive Lévy process. We provide an intuitively appealing mechanism to create such parameter uncertainty: at Poisson epochs, we resample the model components from a finite number of d settings. It results in a setup that is particularly suited to describe situations in which the risk reserve dynamics are affected by external processes. We extend the classical Cramér–Lundberg approximation (asymptotically characterizing the all-time ruin probability in a light-tailed setting) to this more general setup. In addition, for the situation that the driving Lévy processes are sums of Brownian motions and compound Poisson processes, we find an explicit uniform bound on the ruin probability. In passing we propose an importance-sampling algorithm facilitating efficient estimation, and prove it has bounded relative error. In a series of numerical experiments we assess the accuracy of the asymptotics and bounds, and illustrate that neglecting the resampling can lead to substantial underestimation of the risk.     

Distribution–free confidence intervals for the difference between quantiles

An asymptotically distribution–free confidence interval for the difference of the p –th quantiles of two distributions was presented by ALBERS and LOHNBERG (1984). A modification of their procedure is presented for use when the sample sizes are specified.     

On piecewise linear density estimators

We study piecewise linear density estimators from the L ₁ point of view: the frequency polygons investigated by S cott (1985) and J ones et al. (1997), and a new piecewise linear histogram. In contrast to the earlier proposals, a unique multivariate generalization of the new piecewise linear histogram is available. All these estimators are shown to be universally L ₁ strongly consistent. We derive large deviation inequalities. For twice differentiable densities with compact support their expected L ₁ error is shown to have the same rate of convergence as have kernel density estimators. Some simulated examples are presented.     

Inference for Optimal Split Point in Conditional Quantiles

In this paper we show the occurrence of cubic-root asymptotics in misspecified conditional quantile models where the approximating functions are restricted to be binary decision trees. Inference procedure for the optimal split point in the decision tree is conducted by inverting a t-test or a deviation measure test, both involving Chernoff type limiting distributions. In order to avoid estimating the nuisance parameters in the complicated limiting distribution, subsampling is proved to deliver the correct confidence interval/set.