Dynamic Strategies, Asset Pricing Models, and the Out-of-Sample Performance of the Tangency Portfolio

This paper introduces a new algorithm for exploiting time-series predictability-based patterns to obtain an abnormal return, or alpha, with respect to a given benchmark asset pricing model. The algorithm proposes a deterministic daily market timing strategy that decides between being fully invested in a risky asset or in a risk-free asset, with the trading rule represented by a parametric perceptron. The optimal parameters are sought in-sample via differential evolution to directly maximize the alpha. Successively using two modern asset pricing models and two different portfolio weighting schemes, the algorithm was able to discover an undocumented anomaly in the United States stock market cross-section, both out-of-sample and using small transaction costs. The new algorithm represents a simple and flexible alternative to technical analysis and forecast-based trading rules, neither of which necessarily maximizes the alpha. This new algorithm was inspired by recent insights into representing reinforcement learning as evolutionary computation.

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Pricing office rents in Sydney CBD: testing the water on automated rent reviews

Property Management ◽

10.1108/pm-10-2020-0070 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Olga Filippova ◽

Jeremy Gabe ◽

Michael Rehm

Keyword(s):

Asset Pricing ◽

Real Estate ◽

Percentage Error ◽

Residential Real Estate ◽

Pricing Models ◽

Content Type ◽

Market Rent ◽

Absolute Percentage Error ◽

Asset Pricing Models ◽

Out Of Sample

PurposeAutomated valuation models (AVMs) are statistical asset pricing models omnipresent in residential real estate markets, where they inform property tax assessment, mortgage underwriting and marketing. Use of these asset pricing models outside of residential real estate is rare. The purpose of the paper is to explore key characteristics of commercial office lease contracts and test an application in estimating office market rental prices using an AVM.Design/methodology/approachThe authors apply a semi-log ordinary least squares hedonic regression approach to estimate either contract rent or the total costs of occupancy (TOC) (“grossed up” rent). Furthermore, the authors adopt a training/test split in the observed leasing data to evaluate the accuracy of using these pricing models for prediction. In the study, 80% of the samples are randomly selected to train the AVM and 20% was held back to test accuracy out of sample. A naive prediction model is used to establish accuracy prediction benchmarks for the AVM using the out-of-sample test data. To evaluate the performance of the AVM, the authors use a Monte Carlo simulation to run the selection process 100 times and calculate the test dataset's mean error (ME), mean absolute error (MAE), mean absolute percentage error (MAPE), median absolute percentage error (MdAPE), coefficient of dispersion (COD) and the training model's r-squared statistic (R2) for each run.FindingsUsing a sample of office lease transactions in Sydney CBD (Central Business District), Australia, the authors demonstrate accuracy statistics that are comparable to those used in residential valuation and outperform a naive model.Originality/valueAVMs in an office leasing context have significant implications for practice. First, an AVM can act as an impartial arbiter in market rent review disputes. Second, the technology may enable frequent market rent reviews as a lease negotiation strategy that allows tenants and property owners to share market risk by limiting concerns over high costs and adversarial litigation that can emerge in a market rent review dispute.

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A Generalized Weighted Monte Carlo Calibration Method for Derivative Pricing

Mathematics ◽

10.3390/math9070739 ◽

2021 ◽

Vol 9 (7) ◽

pp. 739

Author(s):

Hilmar Gudmundsson ◽

David Vyncke

Keyword(s):

Monte Carlo ◽

Asset Pricing ◽

Calibration Method ◽

Original Method ◽

Pricing Models ◽

Market Prices ◽

Asset Pricing Models ◽

Multiple Weights ◽

Out Of Sample ◽

Distinguishing Features

The weighted Monte Carlo method is an elegant technique to calibrate asset pricing models to market prices. Unfortunately, the accuracy can drop quite quickly for out-of-sample options as one moves away from the strike range and maturity range of the benchmark options. To improve the accuracy, we propose a generalized version of the weighted Monte Carlo calibration method with two distinguishing features. First, we use a probability distortion scheme to produce a non-uniform prior distribution for the simulated paths. Second, we assign multiple weights per path to fit with the different maturities present in the set of benchmark options. Our tests on S&P500 options data show that the new calibration method proposed here produces a significantly better out-of-sample fit than the original method for two commonly used asset pricing models.

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Hierarchical Time-Varying Estimation of Asset Pricing Models

Journal of Risk and Financial Management ◽

10.3390/jrfm15010014 ◽

2022 ◽

Vol 15 (1) ◽

pp. 14

Author(s):

Richard T. Baillie ◽

Fabio Calonaci ◽

George Kapetanios

Keyword(s):

Asset Pricing ◽

Bandwidth Selection ◽

Risk Premia ◽

Pricing Models ◽

Factor Loadings ◽

Asset Pricing Models ◽

Regression Framework ◽

Out Of Sample ◽

Validation Procedure ◽

Dynamic Asset Pricing

This paper presents a new hierarchical methodology for estimating multi factor dynamic asset pricing models. The approach is loosely based on the sequential Fama–MacBeth approach and developed in a kernel regression framework. However, the methodology uses a very flexible bandwidth selection method which is able to emphasize recent data and information to derive the most appropriate estimates of risk premia and factor loadings at each point in time. The choice of bandwidths and weighting schemes are achieved by a cross-validation procedure; this leads to consistent estimators of the risk premia and factor loadings. Additionally, an out-of-sample forecasting exercise indicates that the hierarchical method leads to a statistically significant improvement in forecast loss function measures, independently of the type of factor considered.

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