Portfolio Pricing Research Articles

Idiosyncratic momentum and the cross‐section of stock returns: Further evidence

AbstractIn this article, we evaluate the profitability and economic source of the predictive power of the idiosyncratic momentum effect, by using five popular asset pricing models to construct the idiosyncratic momentum. We show that all five idiosyncratic momentum strategies produce similar return predictability and consistently outperform the conventional momentum strategy in the cross‐sectional pricing of equity portfolios and individual stocks. This positive effect of idiosyncratic momentum on returns is consistent with the investment capital asset pricing model (CAPM). Further analysis reveals that the firm‐level idiosyncratic momentum effect cannot extend to the aggregate stock market.

A Nonparametric Approach to Bond Portfolio Immunization

We consider the problem of short term immunization of a bond-like obligation with respect to changes in interest rates using a portfolio of bonds. In the case that the zero-coupon yield curve belongs to a fixed low-dimensional manifold, the problem is widely known as parametric immunization. Parametric immunization seeks to make the sensitivities of the hedged portfolio price with respect to all model parameters equal to zero. However, within a popular approach of nonparametric (smoothing spline) term structure estimation, parametric hedging is not applicable right away. We present a nonparametric approach to hedging a bond-like obligation allowing for a general form of the term structure estimator with possible smoothing. We show that our approach yields the standard duration based immunization in the limit when the amount of smoothing goes to infinity. We also recover the industry best practice approach of hedging based on key rate durations as another particular case. The hedging portfolio is straightforward to calculate using only basic linear algebra operations.

Neural Stochastic Control Application : Optimal Portfolio Allocation

Neural Control (NC) is a growing field in stochastic optimal control applied to various dynamical systems such as quadcopter. NC is a non-parametric, learning based computational scheme, capable of handling high dimensional control problems. In this paper, “NC” principles have been applied to the problem of optimal investment portfolio allocation, which is commonly solved by the usage of covariance matrix in a Markowitz framework. Therefore, the portfolio quadratic optimization problem has been first re-cast into a generic stochastic control framework with non-linear dependencies and then solved using various Neural Networks (NNs). In NC context, literature on stacked Feed-Forward for time independent control has been recalled to formulate the properties of new NN architectures: Long Short-Term Memory (LSTM) and Attention LSTM for time-dependent control. Final section presents the numerical results of control weights convergence and dynamics using Monte Carlo simulation of portfolio asset prices (Geometric Brownian Motion) through various scenarios: first using stationary regime of volatility and correlation, second using non-stationary regime. Thus, behaviour of three NN architectures has been studied in those scenarios. Results suggest the further development of NC in domain of portfolio allocation as alternative of covariance matrix based solutions and related finance, economic supply and demand problems.

Asset Pricing vs Asset Expected Returning in Factor Models

We propose a simple extension of the classical linear factor model where the deviations of a portfolio price from its permanent component emerge as a predictor for portfolio returns. Intuitively, when asset prices are above their trend, next period expected returns are lower. We discuss price deviations and return predictability within a simple model of diagnostic expectations. Furthermore, the price deviations are transitory if the factor model is able to track the buy-and-hold asset portfolio. Hence, the price deviations are useful to compare and select factor models. We conclude by studying the implications of our model for conditional asset pricing.

Institutional investors' stocks portfolio strategies and commodity prices: a cross-correlation analysis in a financialisation context

New institutional players entered the futures markets with additional important capital inflows from 2000s onwards. Generally, labelled by the term financialisation of commodity markets, integration between traditional financial assets and futures markets raised several concerns for potential spill-over effects on commodity price levels and return volatilities. The aim of this paper is to empirically investigate the plausibility of a lead-lag relationship ignited by institutional investors portfolio management strategies. In order to pursue this goal, a cross-correlation function (ccf) is applied to stock exchange indexes and main international commodity spot prices. The key findings are that the overall effects of such an increased integration/participation appear quite controversial. Oil seem to support (with weak signals detected) this reaction mechanism. Substantially, not significant results are observable for industrial and agricultural commodities.

Modelling Intervalling Effect of High Frequency Trading on Portfolio Volatility

This paper theoretically examines whether reducing the minimum trading interval could affect portfolio volatility. Modelling the underlying de-trended asset price with Ornstein Uhlenbeck process, the paper investigates the volatility of portfolios that employ buy and hold strategy and momentum strategy. The paper presents theoretical evidence that the fast trading could increase portfolio price fluctuation and hence potentially suggests another cost of high frequency trading, besides the well-known damages including herding, aggressive trading strategies dark pools, immediate-or-cancel type orders.

Assortment Portfolio Price Positioning of a Transnational Corporation in the Ukrainian Market

The paper deals with the critical stages of the evolution of theoretical approaches to the positioning strategies content. On this basis, the key approaches to the market positioning processes management formulated. The correlation between the concept of creating consumer value and the price positioning strategy proved. The algorithm of the process of forming and implementing the strategy of assortment portfolio price positioning of a transnational corporation on the Ukrainian market proposed.

Quantifying Model Performance

A model/hedging performance is relatively poorly covered in the literature. This is particularly valid for general portfolios including both vanilla and exotic instruments. Practitioners generally use so called \pnl explain which measures whether portfolio price movements can be explained by changes in risk factors for corresponding sensitivities. It gives, however, relatively little information about the model performance. In this paper, we address the problem under a new angle of the payoff replication along the whole portfolio life and come up with a metric which can classify different hedging strategies, in both relative and absolute ways. We confirm our theoretical results with numerical experiments. Initially we generate scenarios using the Heston model: the obtained numerical classification of different hedging strategies naturally agrees with a common sense. Moreover, this classification remains valid for all the Heston evolution scenarios (Monte Carlo paths) with tiny exceptions. Subsequently, we repeat these tests using real market data. Contrary to previous finding in the literature, we conclude that more sophisticated models, such as the Heston model, do in fact outperform simpler models, such as the Black-Scholes model, according to the metric introduced in this paper. These results contradict previous findings in the literature, so we supplement our numerical tests with the corresponding P\&L explained results. In accordance with the literature, the latter metric cannot identify more sophisticated models as performing better than simple models, however the metric introduced in this paper can.

Utility Maximization for an Investor with Asymmetric Attitude to Gains and Losses over the Mean–Variance Efficient Frontier

This paper examines the problem of choosing the optimal portfolio for an investor with asymmetric attitude to gains and losses described in the cumulative prospect theory (CPT) of A. Tversky and D. Kahneman. We consider the problem of finding the portfolio that maximizes CPT-utility over the set of all feasible portfolios along the mean–variance efficient frontier under some conditions on the stochastic behavior both of the portfolio price and the discount factor. It should be noted that since the value function of the CPT-utility is S-shaped and not-differentiable in the origin, numerical approach to the optimization of CPT-utility is not trivial. We introduce a model with stochastic behavior of both the portfolio price and a discount factor. Following routing procedure for Black-Scholes environment we find the closed-form solution for CPT-utility in a simple particular case. Nevertheless, it turned out that in the general case the closed-form solution can not be obtained. Therefore, we use computational methods for studying the properties of CPT-utility. The paper shows that this approach to the optimal portfolio choice for CPT-investor might be promising.

The Limits to Volatility Predictability: Quantifying Forecast Accuracy Across Horizons

Volatility forecasting is crucial for portfolio management, risk management, and pricing of derivative securities. Still, little is known about how far ahead one can forecast volatility. First, in this paper we introduce the notions of the spot and forward predicted volatilities and propose to describe the term structure of volatility predictability by the spot and forward forecast accuracy curves. Then, by employing a few popular time-series volatility models, we perform a comprehensive empirical study on the horizon of volatility predictability. Our results suggest that, whereas the spot volatility can be predicted over horizons that extend to 35 weeks, the horizon of the forward volatility predictability is rather short and limited to approximately 7.5 weeks. Finally, we suggest a plausible explanation for why standard models fail to provide sensible longer-horizon volatility forecasts.

Foreign Portfolio Investment and Price Discovery of Indian Equity Stocks

Foreign Portfolio Investment and Price Discovery of Indian Equity Stocks

Pricing, Loss and Sensitivity Analysis of Barrier Options via Regression

Monte Carlo simulation methods for the valuation of financial instruments have become a staple of empirical economic analysis. A specific focus is dedicated to the feasibility of the stock price losses and barrier options as well as the sensitivity of partial time barrier options in dependence on their barrier values, which, to the best of our knowledge, hasn't been analysed in academic papers so far. This paper compares the performance of analytical results with empirical regression-based Monte Carlo outcomes, as proposed by Broadie et. al. (2015), where outer stage samples are used to generate financial risk factors and an inner stage simulation is applied to price the barrier options given the outer stage scenarios. Furthermore, potential correlation characteristics between portfolio pricing and loss regarding infinitesimal small changes in the underlying asset (Delta) are exhibited. Thus, a theoretical assumption approach to the explicit loss function is considered, as yet such an analytical loss function has not occurred in related literature.

Diversification in Lottery-Like Features and Portfolio Pricing Discounts

Diversification in Lottery-Like Features and Portfolio Pricing Discounts

Simulating the Case against Stock Picking

In simulating a portfolio of 100 security prices the median terminal price was regularly below that of the average mean portfolio price. In that picking a single security would likely choose the median terminal price, a case can be made against stock picking.

A Model-Point Approach to Indifference Pricing of Life Insurance Portfolios with Dependent Lives

In this paper, we study the pricing of life insurance portfolios in the presence of dependent lives. We assume that an insurer with an initial exposure to n mortality-contingent contracts wanted to acquire a second portfolio constituted of m individuals. The policyhold-ers' lifetimes in these portfolios are correlated with a Farlie-Gumbel-Morgenstern (FGM) copula, which induces a dependency between the two portfolios. In this setting, we compute the indifference price charged by the insurer endowed with an exponential utility. The optimal price is characterized as a solution to a backward differential equation (BSDE). The latter can be decomposed into (n − 1)n! auxiliary BSDEs. In this general case, the derivation of the indifference price is computationally infeasible. Therefore, while focusing on the example of death benefit contracts, we develop a model point based approach in order to ease the computation of the price. It consists on replacing each portfolio with a single policyholder that replicates some risk metrics of interest. Also, the two representative agents should adequately reproduce the observed dependency between the initial portfolios.

Return Ambiguity, Portfolio Choice, and Asset Pricing

We formulate a tractable continuous-time rational expectations model in which the agent is ambiguity averse and would like to robustify asset return specification. Ambiguity affects the portfolio rule and asset pricing both individually and collectively with risk. Independently existing ambiguity premium helps to explain why investors appear to underinvest in risky assets and do not exploit the asymptotic arbitrage opportunity emerged from trading inertia where return volatility (risk) is nearly zero (Campbell and Cochrane, 1999). Calibration of our Lucas-style model to the annual and quarterly U.S. asset prices and consumption data yields a relative risk aversion coefficient of five, and attributes 23%, 41%, and 36% of the equity premium to risk, ambiguity, and their interactions, respectively.

WRONG-WAY RISK CVA MODELS WITH ANALYTICAL EPE PROFILES UNDER GAUSSIAN EXPOSURE DYNAMICS

We consider two classes of wrong-way risk models in the context of CVA: static (resampling) and dynamic (reduced form). Although both potentially suffer from arbitrage problems, their tractability makes them appealing to the industry and therefore deserve additional study. For example, Gaussian copula-based resampling and reduced-form with “Hull–White intensities” yield analytical expected positive exposure (EPE) profiles when the portfolio price process (i.e. exposure process) is Gaussian. However, the first approach disregards credit volatility whilst the second can provide default probabilities larger than 1. We therefore enlarge the study by introducing a new dynamic approach for credit risk, consisting in the straight modeling of the survival (Azéma supermartingale) process using the [Formula: see text]-martingale. This method is appealing in that it helps fixing some drawbacks of the above models. Indeed, it is a dynamic method (it disentangles correlation and credit volatility) that preserves probabilities in [Formula: see text] without affecting the analytical tractability of the model. In particular, calibration to any valid default probability curve is automatic and the closed-form expression for the EPE profiles remains available under Gaussian exposures. For each approach, we derive analytically the EPE profiles (conditional upon default) associated to prototypical exposure processes of Forward Rate Agreement (FRA) and Interest Rate Swap (IRS) in all cases and provide a comparison and discuss the implied Credit Valuation Adjustment (CVA) figures.

Is economic uncertainty priced in the cross-section of stock returns?

We investigate the role of economic uncertainty in the cross-sectional pricing of individual stocks and equity portfolios. We estimate stock exposure to an economic uncertainty index and show that stocks in the lowest uncertainty beta decile generate 6% more annualized risk-adjusted return compared to stocks in the highest uncertainty beta decile. We find that the uncertainty premium is driven by the outperformance (underperformance) by stocks with negative (positive) uncertainty beta. Our results indicate that uncertainty-averse investors demand extra compensation to hold stocks with negative uncertainty beta and they are willing to pay high prices for stocks with positive uncertainty beta.

Diversification in Lottery-Like Features and Portfolio Pricing Discounts

Why do portfolios often trade at discounts relative to the sum of their components? I provide a new explanation based on the diversification in lottery-like features. I argue that portfolios trade at discounts when their components exhibit a strong lottery-like feature but a low tendency of producing extreme payoffs together. This discount can be partially mitigated if lottery-like components tend to produce extreme payoffs at the same time. I use the closed-end fund setting to provide empirical supports for this explanation. My findings support prospect theory from an alternative perspective and provide a novel explanation for the closed-end fund puzzle.

A Factor Portfolio Asset Pricing Model Based on the Optimal Distribution of Risk and Return

After analyzing the relationship and risk type of factors, this paper proposes a new factor pricing model consisting of factor portfolios derived from the optimal distribution of risk and return. The new factor model outperforms the Sharp-Lintner (1964, 1965) CAPM, the Fama-French (1993) three-factor model, the Carhart (1997) four-factor model, the Hou-Xue-Zhang (2015) four-factor model and the Fama-French (2015, 2016) five-factor model in 16 of the 17 mimicking pricing portfolios. So far portfolios used for tests are limited and there are no studies investigating robustness of the models. To solve this problem, we propose using time-series simulation tests and anomaly portfolio reconstruction tests to conduct the robustness tests and GRS tests. The empirical results confirm our new factor models are more robust and has higher pricing power than others. The research of this paper is helpful to expand the modern financial pricing theory, and has a certain reference value for financial practice.