If returns are not predictable, dividend growth must be predictable, to generate the observed variation in divided yields. I find that the absence of dividend growth predictability gives stronger ...evidence than does the presence of return predictability. Long-horizon return forecasts give the same strong evidence. These tests exploit the negative correlation of return forecasts with dividend-yield autocorrelation across samples, together with sensible upper bounds on dividend-yield autocorrelation, to deliver more powerful statistics. I reconcile my findings with the literature that finds poor power in long-horizon return forecasts, and with the literature that notes the poor out-of-sample R² of return-forecasting regressions.
CVXGEN is a software tool that takes a high level description of a convex optimization problem family, and automatically generates custom C code that compiles into a reliable, high speed solver for ...the problem family. The current implementation targets problem families that can be transformed, using disciplined convex programming techniques, to convex quadratic programs of modest size. CVXGEN generates simple, flat, library-free code suitable for embedding in real-time applications. The generated code is almost branch free, and so has highly predictable run-time behavior. The combination of regularization (both static and dynamic) and iterative refinement in the search direction computation yields reliable performance, even with poor quality data. In this paper we describe how CVXGEN is implemented, and give some results on the speed and reliability of the automatically generated solvers.
Expected Idiosyncratic Skewness Boyer, Brian; Mitton, Todd; Vorkink, Keith
The Review of financial studies,
01/2010, Volume:
23, Issue:
1
Journal Article
Peer reviewed
We test the prediction of recent theories that stocks with high idiosyncratic skewness should have low expected returns. Because lagged skewness alone does not adequately forecast skewness, we ...estimate a cross-sectional model of expected skewness that uses additional predictive variables. Consistent with recent theories, we find that expected idiosyncratic skewness and returns are negatively correlated. Specifically, the Fama-French alpha of a low-expected-skewness quintile exceeds the alpha of a high-expected-skewness quintile by 1.00% per month. Furthermore, the coefficients on expected skewness in Fama-MacBeth cross-sectional regressions are negative and significant. In addition, we find that expected skewness helps explain the phenomenon that stocks with high idiosyncratic volatility have low expected returns.
Statistics and Data Analysis for Financial Engineering provides an overview of the methods and techniques used to extract quantitative information from enormous amounts of data. The text includes R ...Labs with real-data exercises, and integrates graphical and analytical methods for modeling and diagnosing modeling errors.
This paper investigates the dynamic relation between net individual investor trading and short-horizon returns for a large cross-section of NYSE stocks. The evidence indicates that individuals tend ...to buy stocks following declines in the previous month and sell following price increases. We document positive excess returns in the month following intense buying by individuals and negative excess returns after individuals sell, which we show is distinct from the previously shown past return or volume effects. The patterns we document are consistent with the notion that risk-averse individuals provide liquidity to meet institutional demand for immediacy.
A nonlinear MPC framework is presented that is suitable for dynamical systems with sampling times in the (sub)millisecond range and that allows for an efficient implementation on embedded hardware. ...The algorithm is based on an augmented Lagrangian formulation with a tailored gradient method for the inner minimization problem. The algorithm is implemented in the software framework GRAMPC and is a fundamental revision of an earlier version. Detailed performance results are presented for a test set of benchmark problems and in comparison to other nonlinear MPC packages. In addition, runtime results and memory requirements for GRAMPC on ECU level demonstrate its applicability on embedded hardware.
In a model with heterogeneous-risk-aversion agents facing margin constraints, we show how securities' required returns increase in both their betas and their margin requirements. Negative shocks to ...fundamentals make margin constraints bind, lowering risk-free rates and raising Sharpe ratios of risky securities, especially for high-margin securities. Such a funding-liquidity crisis gives rise to "bases," that is, price gaps between securities with identical cash-flows but different margins. In the time series, bases depend on the shadow cost of capital, which can be captured through the interest-rate spread between collateralized and uncollateralized loans and, in the cross-section, they depend on relative margins. We test the model empirically using the credit default swap—bond bases and other deviations from the Law of One Price, and use it to evaluate central banks' lending facilities.
Computational finance is an interdisciplinary field which joins financial mathematics, stochastics, numerics and scientific computing. Its task is to estimate as accurately and efficiently as ...possible the risks that financial instruments generate. This volume consists of a series of cutting-edge surveys of recent developments in the field written by leading international experts. These make the subject accessible to a wide readership in academia and financial businesses.The book consists of 13 chapters divided into 3 parts: foundations, algorithms and applications. Besides surveys of existing results, the book contains many new previously unpublished results.Contents:Foundations:Multilevel Monte Carlo Methods for Applications in Finance (Mike Giles and Lukasz Szpruch)Convergence of Numerical Methods for SDEs in Finance (Peter Kloeden and Andreas Neuenkirch)Inverse Problems in Finance (J Baumeister)Asymptotic and Non Asymptotic Approximations for Option Valuation (R Bompis and E Gobet)Algorithms:Discretization of Backward Stochastic Volterra Integral Equations (Christian Bender and Stanislav Pokalyuk)Semi-Lagrangian Schemes for Parabolic Equations (Kristian Debrabant and Espen Robstad Jakobsen)Derivative-Free Weak Approximation Methods for Stochastic Differential Equations (Kristian Debrabant and Andreas Röβler)Wavelet Solution of Degenerate Kolmogoroff Forward Equations (Oleg Reichmann and Christoph Schwab)Randomized Multilevel Quasi-Monte Carlo Path Simulation (Thomas Gerstner and Marco Noll)Applications:Drift-Free Simulation Methods for Pricing Cross-Market Derivatives with LMM (J L Fernández, M R Nogueiras, M Pou and C Vázquez)Application of Simplest Random Walk Algorithms for Pricing Barrier Options (M Krivko and M V Tretyakov)Coupling Local Currency Libor Models to FX Libor Models (John Schoenmakers)Dimension-Wise Decompositions and Their Efficient Parallelization (Philipp Schröder, Peter Mlynczak and Gabriel Wittum)Readership: Graduate students and researchers in finance, engineering and operations research.
We examine the accuracy and contribution of the Merton distance to default (DD) model, which is based on Merton's (1974) bond pricing model. We compare the model to a "naive" alternative, which uses ...the functional form suggested by the Merton model but does not solve the model for an implied probability of default. We find that the naive predictor performs slightly better in hazard models and in out-of-sample forecasts than both the Merton DD model and a reduced-form model that uses the same inputs. Several other forecasting variables are also important predictors, and fitted values from an expanded hazard model outperform Merton DD default probabilities out of sample. Implied default probabilities from credit default swaps and corporate bond yield spreads are only weakly correlated with Merton DD probabilities after adjusting for agency ratings and bond characteristics. We conclude that while the Merton DD model does not produce a sufficient statistic for the probability of default, its functional form is useful for forecasting defaults.
•A real option is a contract which gives its holder the flexibility to expand the scale of an investment project or production. Real options are often used to hedge risks or capture opportunities in ...investments. In this paper, we establish a mathematical model for pricing a real option of expansion whose underlying asset price and its volatility/variance satisfy two separate stochastic equations. Based on Ito’s lemma and a hedging technique, we show that the option price satisfies a 2nd-order parabolic partial differential equation (PDE) in two spatial dimensions. We also derive the boundary and terminal conditions for the PDE and some of these conditions are also determined by PDEs.•We propose a novel 9-point finite difference scheme with a upwind technique is designed for solving the PDE system, as well that for determining the terminal (payoff) condition, established. We show that the coefficient matrix of the system from this discretization is an M-matrix and the numerical solution generated by the finite difference scheme converge to the exact one by proving that the scheme is consistent, monotone and stable.•Extensive numerical experiments on the model and numerical methods using a model investment problem in an iron-ore industry have been performed. The numerical results show that our model and numerical methods for solving the model are able to produce numerical results which are financially meaningful.
In this paper we develop a PDE-based mathematical model for valuing real options on the expansion of an investment project whose underlying commodity price and its volatility follow their respective geometric Brownian motions. This mathematical model is of the form of a 2-dimensional Black-Scholes equation whose payoff condition is determined also by a PDE system. A novel 9-point finite difference scheme is proposed for the discretization of the spatial derivatives and the fully implicit time-stepping scheme is used for the time discretization of the PDE systems. We show that the coefficient matrix of the fully discretized system is an M-matrix and prove that the solution generated by this finite difference scheme converges to the exact one when the mesh sizes approach zero. To demonstrate the usefulness and effectiveness of the mathematical model and numerical method, we present a case study on a real option pricing problem in the iron-ore mining industry. Numerical experiments show that our model and methods are able to produce numerical results which are financially meaningful.
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