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In this paper, we study risk measures and portfolio problems based on a Stochastic Volatility Factor Model (SVFM). We analyze the sensitivity of Value at Risk (VaR) and Expected Shortfall (ES) to the changes in the parameters of the model. We compare the positions of a linear portfolio under assets following a SVFM, a Black–Scholes Model and a model with constant dependence structure. We consider an application to a portfolio of three selected Asian funds.

We consider HJM type models for the term structure of futures prices, where the volatility is allowed to be an arbitrary smooth functional of the present futures price curve. Using a Lie algebraic approach we investigate when the infinite dimensional futures price process can be realized by a finite dimensional Markovian state space model, and we give general necessary and sufficient conditions, in terms of the volatility structure, for the existence of a finite dimensional realization. We study a number of concrete applications including a recently developed model for gas futures. In particular we provide necessary and sufficient conditions for when the induced spot price is a Markov process. In particular we can prove that the only HJM type futures price models with spot price dependent volatility structures which generically possess a spot price realization are the affine ones. These models are thus the only generic spot price models from a futures price term structure point of view.

We consider a reduced-form credit risk model where default intensities and interest rate are functions of a not fully observable Markovian factor process, thereby introducing an information-driven default contagion effect among defaults of different issuers. We determine arbitrage-free prices of OTC products coherently with information from the financial market, in particular yields and credit spreads and this can be accomplished via a filtering approach coupled with an EM-algorithm for parameter estimation.

We propose an approach for pricing and hedging weather derivatives based on including forward looking information about the temperature available to the market. This is achieved by modeling temperature forecasts by a finite dimensional factor model. Temperature dynamics are then inferred in the short end. In analogy to interest rate theory, we establish conditions which guarantee consistency of a factor model with the martingale dynamics of temperature forecasts. Finally, we consider a specific two-factor model and examine in more detail pricing and hedging of weather derivatives in this context.

In this paper, we analyze factor uniqueness in the S&P 500 universe. The current theory of approximate factor models applies to infinite markets. In the limit of infinite markets, factors are unique and can be represented with principal components. If this theory would apply to realistic markets such as the S&P 500 universe, the quest for proprietary factors would be futile. We find that this is not the case: in finite markets of the size of the S&P 500 universe different factor models can indeed coexist. We compare three dynamic factor models: a factor model based on principal component analysis, a classical factor model based on industry, and a factor model based on cluster analysis. Dynamic behavior is represented by fitting vector autoregressive models to factors and using them to make forecasts. We analyze the uniqueness of factors using Procrustes analysis and correlation analysis. Forecasting performance of the factor models is analyzed by forming active portfolio strategies based on the forecasts for each model using sample data from the S&P 500 index in the 21-year period 1989–2010. We find that one or two factors which we can identify with global factors are common to all models, while the other factors for the factor models we analyzed are truly different. Models exhibit significant differences in performance with principal component analysis-based factor models appearing to behave better than the sector-based factor models.

This study investigates whether there is a "China-concept factor", a common variation of stock returns, for firms that are listed in Taiwan stock markets and have real investments in China. We employ a methodology similar to that used by Lamont et al. (2001) in examining whether there is a financial-constraints factor. Listed firms in Taiwan stock markets for the period 1990–2004 are used to form portfolios of firms based on observable characteristics related to their real investments in China. We find that firms investing heavily in China have stock returns moving together over time, which suggests that firms investing in China are subject to common shocks. Firms investing heavily in China are found to exhibit higher average stock returns. There exists a China-concept factor for firms listed in Taiwan stock market and have real investments in China.

This paper examines relative performance of alternative asset pricing models using individual security returns. The standard multivariate test used in studies comparing the performance of asset pricing models requires the number of stocks to be less than the number of time series observations, which requires grouping stocks into portfolios. This results in a loss of disaggregate stock information. We apply a different statistical test to overcome this problem and to investigate relative performance of alternative asset pricing models using individual security returns instead of portfolio returns. Our findings suggest that a parsimonious six-factor model that includes the momentum and orthogonal value factors outperforms all other models based on a number of measures as well as the average $F$-test. Unlike the standard multivariate test, we find that the average $F$-test has superior power to discriminate among competing models and does not reject all tested models.

We introduce a high-dimensional factor model with time-varying loadings. We cover both stationary and nonstationary factors to increase the possibilities of applications. We propose an estimation procedure based on two stages. First, we estimate common factors by principal components. In the second step, considering the estimated factors as observed, the time-varying loadings are estimated by an iterative generalized least squares procedure using wavelet functions. We investigate the finite sample features by some Monte Carlo simulations. Finally, we apply the model to study the Nord Pool power market’s electricity prices and loads.

We comprehensively investigate the robustness of well-known factor models to altered factor formation breakpoints. Deviating from the standard 30th and 70th percentile selection, we use an extensive set of anomaly test portfolios to uncover two main findings: First, there is a trade-off between specification and diversification. More centered breakpoints tend to result in less (idiosyncratic) risk. More extreme sorts lead to greater exposure to the underlying anomalies and thus to higher average returns. Second, the models are robust to varying degrees. Hou et al.’s model [2015, Digesting Anomalies: An Investment Approach, Review of Financial Studies 28, 650–705] is much more sensitive to changes in breakpoints than the Fama–French models.

The worldwide impact of the Global Financial Crisis (GFC) on stock markets, investors and fund managers has lead to a renewed interest in appropriate tools for robust risk management. Quantile regression is a powerful technique and deserves the interest of financial decision makers given its remarkable capabilities for capturing and explaining the behavior of financial return series across a distribution more effectively than ordinary least squares regression methods which are the standard tool. In this paper, we present quantile regression estimation as an attractive additional investment tool, which is more effective than Ordinary Least Squares (OLS) in analyzing information across the quantiles of a distribution. This translates into the more accurate calibration of asset pricing models and subsequent informational gains in portfolio formation. We present empirical evidence of the superior capabilities of quantile regression based techniques as applied across the quantiles of return distributions to derive information for portfolio formation. We show, via stocks in Dow Jones Industrial Index, that at times of financial shocks, such as the GFC, a portfolio of stocks formed using quantile regression in the context of the Fama–French three-factor model, performs better than the one formed using traditional OLS.

The standard multivariate test of Gibbons et al. (1989) used in studies examining relative performance of alternative asset pricing models requires the number of stocks to be less than the number of time-series observations, which requires stocks to be grouped into portfolios. This results in a loss of disaggregate stock information. We apply a new statistical test to get around this problem. We find that the multivariate average F-test developed by Hwang and Satchell (2014) has superior power to discriminate among competing models and does not reject tested models altogether, unlike the standard multivariate test. Application of the multivariate average F-test for examination of relative performance of asset pricing models demonstrate that a parsimonious 6-factor model with the market, size, orthogonal value, profitability, investment, and momentum factors outperforms all other models.

There is often a wide divergence between academic and practitioner views on risk, return, and portfolio construction. For example, academics focus primarily on purely quantitative measures or factors. Initially, the focus was on dividends, free cash flow, standard deviation, and beta. Later, additional factors analyzed by the academic community came into focus, such as size, style, liquidity, momentum, and quality. Practitioners, in contrast, often focus on a company’s products, its history, and the competitive dynamics of its industry. Furthermore, practitioners “discovered” anomalies, such as momentum, decades before they were rigorously analyzed and published by academics. The current distinction between the two groups is not merely quantitative versus qualitative. This chapter summarizes the viewpoints of the two camps — academic and practitioner — and suggest steps that may effectively combine the two schools of thought, at least to a certain degree using the Black–Litterman model and other qualitative techniques, such as stratifying asset pricing models. This analysis may result in a more robust investment, risk management, and portfolio construction process.

We construct liquidity and earnings-based factors and combine with the Market to describe stock returns. Liquidity and Liquidity Growth are significant factors across markets. Intercept tests show that the IELM (International Earnings, Liquidity, and Market) model fits the cross section in various country groupings. As previous research showed, a Liquidity Growth factor subsumes momentum in the U.S., and we test this across international markets. From 2001 through 2019, the momentum factor has a high mean and is significant in Europe and in the Asia-Pacific, except Japan. For this time period, however, momentum is not significant in North American and Japan. While the IELM model reduces the momentum intercept in North America, both IELM and Fama and French (2017) have trouble explaining momentum in Europe and Asia where momentum is pervasive.

Financial anomalies have been studied in the U.S. However, recent evidence suggests that what were initially identified as return anomalies have diminished in U.S. data. Have the identified regularities changed or are they persistent? Have historical and earnings forecasting data been a consistent and highly statistically significant source of excess returns? We test a number of financial anomalies of the 1980s–1990s and report that several models and strategies continue to produce statistically significant excess returns not absorbed by then-known factor models. We report that earnings forecasts, revisions, and breadth and price momentum have maintained their statistical significance during the May 1995–December 2017 time period. More importantly, we use expected return models and multi-factor models that are estimated and known at the start of our current analysis, assuring our readers of out-of-sample and post-publication verification of the models.

A data-driven approach for modeling volatility dynamics and comovements in financial markets is introduced. Special emphasis is given to multivariate conditionally heteroscedastic factor models in which the volatilities of the latent factors depend on their past values, and the parameters are driven by regime switching in a latent state variable. We propose an innovative indirect estimation method based on the generalized EM algorithm principle combined with a structured variational approach, that can handle models with large cross-sectional dimensions. Extensive Monte Carlo simulations and preliminary experiments with financial data show promising results.

An overview of risk measurement techniques for typical energy utilities is given. Most common calculated risk measures are explained among the often simple calculation methods used in practice. For a more sophisticated risk analysis, the various model classes proposed in the literature are reviewed. A three-factor model is explained in mathematical detail and its application to the practical modeling of energy prices is shown. This includes spot and futures prices in different time resolutions as well as the calibration of such models.