Claudio Tebaldi

A multifactor volatility Heston model

We model the volatility of a single risky asset using a multifactor (matrix) Wishart affine process, recently introduced in finance by Gourieroux and Sufana. As in standard Duffie and Kan affine models the pricing problem can be solved through the Fast Fourier Transform of Carr and Madan. A numerical illustration shows that this specification provides a separate fit of the long-term and short-term implied volatility surface and, differently from previous diffusive stochastic volatility models, it is possible to identify a specific factor accounting for the stochastic leverage effect, a well-known stylized fact of the FX option markets analysed by Carr and Wu.

Solvable Affine Term Structure Models

An Affine Term Structure Model (ATSM) is said to be solvable if the pricing problem has an explicit solution, i.e., the corresponding Riccati ordinary differential equations have a regular globally integrable flow. We identify the parametric restrictions which are necessary and sufficient for an ATSM with continuous paths, to be solvable in a state space , where , the domain of positive factors, has the geometry of a symmetric cone. This class of state spaces includes as special cases those introduced by Duffie and Kan (1996), and Wishart term structure processes discussed by Gourieroux and Sufana (2003). For all solvable models we provide the procedure to find the explicit solution of the Riccati ODE.

Multifractal Scaling in the Bak Tang Wiesenfeld sandpile and edge events

A widely applicable analysis of numerical data shows that, while the distribution of avalanche areas obeys finite size scaling, that of toppling numbers is universally characterized by a full, nonlinear multifractal spectrum. Boundary effects determine an unusual dependence on system size of the moment scaling exponents of the conditional toppling distribution at a given area. This distribution is also multifractal in the bulk regime. The resulting picture brings to light unsuspected physics of this long-studied prototype model.

Rare events and breakdown of simple scaling in the Abelian sandpile model

Due to intermittency and conservation, the Abelian sandpile in 2D obeys multifractal, rather than finite size scaling. In the thermodynamic limit, a vanishingly small fraction of large avalanches dominates the statistics and a constant gap scaling is recovered in higher moments of the toppling distribution. Thus, rare events shape most of the scaling pattern and preserve a meaning for effective exponents, which can be determined on the basis of numerical and exact results.

The Scale of Predictability

We introduce a new stylized fact: the hump-shaped behavior of slopes and coefficients of determination as a function of the aggregation horizon when running (forward/backward) predictive regressions of future excess market returns onto past economic uncertainty (as proxied by market variance, consumption variance, or economic policy uncertainty). To justify this finding formally, we propose a novel modeling framework in which predictability is specified as a property of low-frequency components of both excess market returns and economic uncertainty. We dub this property scale-specific predictability. We show that classical predictive systems imply restricted forms of scale-specific predictability. We conclude that for certain predictors, like economic uncertainty, the restrictions imposed by classical predictive systems may be excessively strong.

The Price of the Smile and Variance Risk Premia

In a tractable stochastic volatility model, we identify the price of the smile as the price of the unspanned risks traded in SPX option markets. The price of the smile reflects two persistent volatility and skewness risks, which imply a downward sloping term structure of low-frequency variance risk premia in normal times. In periods of distress, the term structure is upward sloping and dominated by a high-frequency premium for jump variance. This dichotomy is consistent with the puzzling skew sensitivities of option markets with credit-constrained intermediaries and it builds a challenge for many reduced-form and structural models of stochastic volatility.

A Persistence-Based Wold-Type Decomposition for Stationary Time Series

If the aggregate response of the economy to an exogenous shock is a superposition of effects which develop over different time scales, then the statistical estimation of low frequency components is difficult. In fact highly persistent shocks have generally low instantaneous volatility and are hidden by those shocks with high instantaneous volatility and fast decay. We refer to this situation as heterogeneity of persistence levels phenomenon. This paper introduces a new spectral approach which is applicable to the analysis of time series in the presence of persistence heterogeneity. A new linear decomposition of a time series is introduced which generalizes the Wold decomposition for stationary time series and the Beveridge-Nelson permanent transitory decomposition for non stationary integrated ones. In order to prove the relevance of this new methodology for financial valuation, we apply it to clarify some open issues which arise in the empirical analysis of gdp and inflation forecasting. JEL Classification Codes: E32, E43, E44, G12.The Classical Wold Decomposition Theorem allows to split a weakly stationary time series x into a non-deterministic component, driven by uncorrelated innovations, and a deterministic term. This decomposition is a special case of the Abstract Wold Theorem, which deals with isometric operators defined on Hilbert spaces. As the lag operator is isometric on the Hilbert space H_t(x) spanned by the sequence {x_{t-k}_k}, the Classical Wold Decomposition for time series obtains. Moreover, the emph{scaling operator} is isometric on the Hilbert space H_t(e), spanned by the classical Wold innovations of x, and it provides an Extended Wold Decomposition. Thus, the process x may be seen as a sum, across scales, of uncorrelated components that explain different layers of persistence, from temporary fluctuations to low-frequency shocks. Multiscale impulse response functions are, then, defined. Conversely, the sum of suitable uncorrelated components delivers a weakly stationary process. This decomposition fruitfully applies to ARMA and fractional ARIMA processes.

A Multivariate Model of Strategic Asset Allocation with Longevity Risk

This paper proposes a framework to evaluate the impact of longevity-linked securities on the risk-return trade-off for traditional portfolios. Generalized unexpected raise in life expectancy is a source of aggregate risk in the insurance sector balance sheets. Longevity-linked securities are a natural instrument to reallocate these risks by making them tradable in the financial market. This paper extends the strategic asset allocation model of Campbell and Viceira (2005) to include a longevity-linked investment in addition to equity and fixed income securities and describe the resulting term structure of risk-return trade-offs. The model highlights an unexpected predictability pattern of the survival probability estimates. The empirical valuation of the market price of longevity risk, based on prices for standardized annuities publicly offered by US insurance companies, confirms that longevity linked securities offer cheap funding opportunities to asset managers willing to leverage their investment portfolio.

BRANCHING PROCESSES AND EVOLUTION AT THE ENDS OF A FOOD CHAIN

In a critically self-organized model of punctuated equilibrium, boundaries determine peculiar scaling of the size distribution of evolutionary avalanches. This is derived by an inhomogeneous generalization of standard branching processes, extending previous mean field descriptions and yielding ν = 1/2 together with τ′ = 7/4, as distribution exponent of avalanches starting from species at the ends of a food chain. For the nearest neighbor chain one obtains numerically τ′ = 1.25±0.01, and τfirst′ = 1.35±0.01 for the first return times of activity, again distinct from bulk exponents.

A 'Coherent State Transform' Approach to Derivative Pricing

We propose an extension of the transform approach to option pricing introduced in Duffie, Pan and Singleton (Econometrica 68(6) (2000) 1343–1376) and in Carr and Madan (Journal of Computational Finance 2(4) (1999) 61–73). We term this extension the coherent state transform approach, it applies when the Markov generator of the factor process can be decomposed as a linear combination of generators of a Lie symmetry group. Then the family of group invariant coherent states determine the transform to price derivatives. We exemplify this procedure deriving a coherent state transform for affine jump-diffusion processes with positive state space. It improves the traditional FFT because inversion of the latter requires integration over an unbounded domain, while inversion of the coherent state transform requires integration over unit ball. We explicitly perform the pricing exercise for some contracts like the plain vanilla options on (credit) risky bonds and on the spread option.