International Journal of Theoretical and Applied Finance

In this paper we try to bring forward evidence on the practical application of the Grassberger–Procaccia algorithm, in particular on the determination of the "delay time" parameter. For this purpose, we analyze the results obtained from applying the main methods proposed to calculate this delay time for series simulated from well-known chaotic systems. As the most relevant result we conclude that, in general, all the methods display inadequate behavior, except for that based on the previous filtering of the series according to singular value decomposition. In a second stage we apply the same study to three financial series, with the results appearing to confirm the advantage of this method.

We use a path integral approach for solving the stochastic equations underlying the financial markets, and show the equivalence between the path integral and the usual SDE and PDE methods. We analyze both the one-dimensional and the multi-dimensional cases, with point dependent drift and volatility, and describe a covariant formulation which allows general changes of variables. Finally we apply the method to some economic models with analytical solutions. In particular, we evaluate the expectation value of functionals which correspond to quantities of financial interest.

This paper conducts a thorough and detailed investigation on the implications of stochastic volatility and random jump on option prices. Both stochastic volatility and jump-diffusion processes admit asymmetric and fat-tailed distribution of asset returns and thus have similar impact on option prices compared to the Black–Scholes model. While the dynamic properties of stochastic volatility model are shown to have more impact on long-term options, the random jump is shown to have relatively larger impact on short-term near-the-money options. The misspecification risk of stochastic volatility as jump is minimal in terms of option pricing errors only when both the level of kurtosis of the underlying asset return distribution and the level of volatility persistence are low. While both asymmetric volatility and asymmetric jump can induce distortion of option pricing errors, the skewness of jump offers better explanations to empirical findings on implied volatility curves.

A new valuation method is proposed that can be mathematically viewed as a numerical shortcut to approximately solve the partial differential equation written for Expected Instantaneous Return. The method derives OAS as "static spread plus cost of convexity" and is based on some simplified parametric assumption about the static spread's time behavior. The modeling and numerical procedure details are disclaimed with proven accuracy and time efficiency in option-adjusted valuation.

The method is especially effective for multi-scenario pricing, portfolio pricing, risk management and reporting, and for quantifying the impact of "non-traded" factors on reward and risk of holding mortgages. A systematic methodology for comprehensive multi-factor analysis is covered.

A portfolio of different stocks and a risk-less security whose composition is dynamically maintained stable by trading shares at any time step leads to a growth of the capital with a nonrandom rate. This is the key for the theory of optimal-growth investment formulated by Kelly. In presence of transaction costs, the optimal composition changes and, more important, it turns out that the frequency of transactions must be reduced. This simple observation leads to the definition of an optimal lag between two rearrangement of the portfolio. This idea is tested against an investment in a risky asset and a risk-less one. The price of the first is proportional to NYSE composite index while the price of the second grows according to the American Discount Rate.