This is the accepted version of the paper.This version of the publication may differ from the final published version. (2008), and, if we restrict our attention only to lognormally distributed returns, also Veµ ceµ r (2002). While the existing convolution algorithms compute the density of the underlying state variable by moving forward on a suitably de…ned state space grid our new algorithm uses backward price convolution, which resembles classical lattice pricing algorithms. For the …rst time in the literature we provide an analytical upper bound for the pricing error caused by the truncation of the state space grid and by the curtailment of the integration range. We highlight the bene…ts of the new scheme and benchmark its performance against existing …nite di¤erence, Monte Carlo, and forward density convolution algorithms. Permanent repository link

Recent advances in pension product development seem to favour alternatives to the risk free asset often used in the financial theory as a performance standard for measuring the value generated by an investment or a reference point for determining the value of a financial instrument. To this end, in this paper, we apply the simplest machine learning technique, namely, a fully nonparametric smoother with the covariates and the smoothing parameter chosen by cross-validation to forecast stock returns in excess of different benchmarks, including the short-term interest rate, long-term interest rate, earnings-by-price ratio, and the inflation. We find that, net-of-inflation, the combined earnings-by-price and long-short rate spread form our best-performing two-dimensional set of predictors for future annual stock returns. This is a crucial conclusion for actuarial applications that aim to provide real-income forecasts for pensioners.

This is the accepted version of the paper.This version of the publication may differ from the final published version. Further, a sentiment-based trading simulation exercise on the sale and purchase of vessels shows that investors can benefit from higher returns compared to the buy-and-hold benchmark, while partially offsetting the highly volatile nature of the shipping industry. Permanent

This is the accepted version of the paper.This version of the publication may differ from the final published version. We present a joint Monte Carlo-Fourier transform sampling scheme for pricing derivative products under a CGMY model exhibiting jumps of infinite activity and finite or infinite variation. The approach relies on numerical transform inversion with computable error estimates, which allow generating the unknown cumulative distribution function (CDF) of the CGMY process increments at the desired accuracy level. We use this to generate samples and simulate the entire trajectory of the process without need of truncating the process small jumps. We illustrate the computational efficiency of the proposed method by comparing it to the existing methods in the literature on pricing a wide range of option contracts, including path-dependent univariate and multivariate products. Permanent repository link:The authors would like to thank Gianluca Fusai for interesting comments to a previous version of this paper, Russell Gerrard for his valuable contribution that helped improve the paper, Gerald Rickayzen for useful discussions, and Michele Bianchi, Reiichiro Kawai and Hiroki Masuda for useful suggestions on the implementation of the SR and AR sampling schemes. Usual caveat applies.JEL Classification: G12, G13, C63 †Corresponding author.

This is the accepted version of the paper.This version of the publication may differ from the final published version. We propose an accurate method for pricing arithmetic Asian options on the discrete or continuous average in a general model setting by means of a lower bound approximation. In particular, we derive analytical expressions for the lower bound in the Fourier domain. This is then recovered by a single univariate inversion and sharpened using an optimization technique. In addition, we derive an upper bound to the error from the lower bound price approximation. Our proposed method can be applied to computing the prices and price sensitivities of Asian options with fixed or floating strike price, discrete or continuous averaging, under a wide range of stochastic dynamic models, including exponential Lévy models, stochastic volatility models, and the constant elasticity of variance diffusion. Our extensive numerical experiments highlight the notable performance and robustness of our optimized lower bound for different test cases. Permanent repository linkKey words : arithmetic Asian options; CEV diffusion; stochastic volatility models; Lévy processes; discrete average; continuous average MSC2000 subject classification : Primary: 91B70, 91B25, 60J25; Secondary: 60H30, 91B24 OR/MS subject classification : Primary: asset pricing, diffusion, Markov processes, stochastic model applications 1. Introduction. We develop accurate analytical pricing formulae for discretely and continuously monitored arithmetic Asian options under general stochastic asset models, including exponential Lévy models, stochastic volatility models, and the constant elasticity of variance diffusion. The payoff of the arithmetic Asian option depends on the arithmetic average price of the underlying asset monitored over a pre-specified period. For more than two decades, much effort has been put into the research on efficient methodologies for computing the price of this option or, in general, expected values of functionals of the average value, under different model assumptions for the underlying. Developing such methods is of considerable practical importance as arithmetic averages see wide application in many fields of finance. Amongst others, we mention uses in computing net present value in project valuation (see [72]), optimal capacity planning under average demand uncertainty for a single firm (see [32]) and stock-swap merger proposals (see [60]). Weighted arithmetic averages also appear in technical analysis and in algorithmic trading; for example, we recall the moving average trading rule and its use from an asset allocation perspective (see [75]). Moving average automatic trading strategies set buying and selling orders depending on the position of the average price for a given period with respect to the current market price (see [50]). Finally, weighted arithmetic average indexes are used as trading benchmarks in pension plans (see [11]).Arithmetic Asian options are very popular among derivatives traders and risk managers. Their...

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This is the accepted version of the paper. This version of the publication may differ from the final published version. Permanent repository link: http://openaccess.city.ac.uk/12201/ Link to published version: http://dx.

Stock return predictability by investor sentiment has been subject to constant updating, but reaching a decisive conclusion seems rather challenging as academic research relies heavily on US data. We provide fresh evidence on stock return predictability in an international setting and show that shipping investor sentiment is a common leading indicator for financial markets. We establish out-of-sample predictability and demonstrate that investor sentiment is also economically significant in providing utility gains to a mean-variance investor. Finally, we find evidence that the predictive power of sentiment works best when negative forecasts are also taken into account.