Asset Price Dynamics Research Articles

Equity-linked annuity valuation under fractional jump-diffusion financial and mortality models

The valuation of equity-linked annuity (EIA) has been extensively studied, while few papers consider the impact of long-range dependence (LRD) features on financial and mortality dynamics in EIAs' valuation. To characterise the LRD feature, we adopt the fractional Brownian motion (FBM) in modelling the dynamics of asset prices and mortality intensities, in which clustering jumps are captured by the compound Hawkes process. Besides, the interest rate dynamic is driven by the FBM and correlates to the asset price dynamic. Numerical analyses show that ignorance of the LRD feature and the jump component in the modelling framework could lead to a potential deficiency in the reserve and solvency capital of EIA policies.

Higher order expectations, learning, and sentiment pricing dynamics

We present a dynamic asset pricing model combining individual investor sentiment, higher order expectations with learning. In the basic model, the forward-looking expectation of individual investors is distorted by individual sentiment and higher order expectations, so prices react more sluggishly to changes in fundamentals of the asset. We find that investor sentiment plays a significant role in the effect of higher order expectations on asset pricing. Investor sentiment not only makes the price tightly anchor to the initial price, but also increases the sentiment drift of the price. Higher order expectations exhibit inertia, therefore aggravating the anchor to the initial price. With the increase of the order, more and more investor sentiment is integrated into the prices, amplifying the bias of pubic signal relative to fundamentals. When individual sentiment investors learn valuable public information through price system in the long term, the information component of the equilibrium price increases, thus drawing the asset price back toward the rational expected value. The model could offer a partial explanation to the inertia and drift in the price path

Integration of Modified Heston Model (Jump and Sentiment Factor) with Artificial Neural Networks (ANN) for Volatility Forecasting in Financial Markets

This study examined a new methodology for predicting volatility in financial markets by combining the modified Heston model (JSF) with Artificial Neural Networks (ANN). Forecasting volatility is essential in various financial contexts, such as risk management, option pricing and portfolio optimization. Although the traditional Heston model is effective but it does not account for additional elements like jumps and investor sentiment factors, which impact asset price dynamics. To address this limitation, the extended Heston model with jump and sentiment variables helped in enhancing its ability to capture complex market trends. We utilized ANN's predictive capabilities on the modified Heston model to improve volatility predictions using historical random market data from NASDAQ Composite. The ANN was trained on simulated data from the modified Heston model and assessed on its effectiveness in forecasting volatility. This approach helped in developing a forecasting hybrid semi-parametric model known as the modified-Heston-ANN model. Our findings revealed that this integrated approach outperformed the individual model, offering more precise and robust volatility predictions

Pricing exchange options under hybrid stochastic volatility and interest rate models

This paper investigates the pricing of exchange options under hybrid models integrating stochastic volatility and stochastic interest rates. It aims to achieve two primary objectives. First, we derive a closed-form pricing formula for exchange options under a two-factor Heston–Hull–White hybrid model, which accounts for long-term volatility and exhibits relatively broad correlations among the dynamics of asset prices, volatilities, and interest rates. Second, we explore the Heston model’s integration with a generalized single-factor stochastic interest rate model, illustrating that the price is not dependent on the specific form of the interest rate process. A closed-form pricing formula for exchange options under this framework is also derived. Our numerical experiments support the proposed formulas and elucidate the effects of various parameters on option prices.

Dynamic Asset Pricing in a Unified Bachelier–Black–Scholes–Merton Model

We present a unified, market-complete model that integrates both Bachelier and Black–Scholes–Merton frameworks for asset pricing. The model allows for the study, within a unified framework, of asset pricing in a natural world that experiences the possibility of negative security prices or riskless rates. Unlike the classical Black–Scholes–Merton, we show that option pricing in the unified model differs depending on whether the replicating, self-financing portfolio uses riskless bonds or a single riskless bank account. We derive option price formulas and extend our analysis to the term structure of interest rates by deriving the pricing of zero-coupon bonds, forward contracts, and futures contracts. We identify a necessary condition for the unified model to support a perpetual derivative. Discrete binomial pricing under the unified model is also developed. In every scenario analyzed, we show that the unified model simplifies to the standard Black–Scholes–Merton pricing under specific limits and provides pricing in the Bachelier model limit. We note that the Bachelier limit within the unified model allows for positive riskless rates. The unified model prompts us to speculate on the possibility of a mixed multiplicative and additive deflator model for risk-neutral option pricing.

Asymmetric Effect of Monetary Policy on Asset Prices: Contractionary VS Expansionary Regimes

This research study seeks to investigate the asymmetrical impact of monetary policy on asset prices (stock price and house prices as indicators of asset prices). Understanding this asymmetry is crucial for policymakers and central banks to evaluate the implications of expansionary and contractionary monetary policies on asset price dynamics. The study examines both interest rates and the money supply, which are key tools of monetary policy, to determine if they have similar effects on asset prices across various economic regimes in 51 countries with diverse income levels for the period of 2009 to 2021. Using a linear and non-linear ARDL (NARDL) approach this study analyze the short-run and long-run impacts of monetary policy on stock and house prices, besides comparing the symmetric and asymmetric impacts of monetary policy shocks on these prices. The outcomes accentuate the importance of policymakers being cognizant of the impacts of different policy regimes on asset prices. Notably, the study reveals a significant and negative short-term impact of interest rates on asset prices under contractionary monetary policy. Conversely, expansionary monetary policy does not demonstrate a significant short-term effect on asset prices. This study makes substantial contributions by deepening insights into the efficacy of monetary policy, offering practical guidance for policymakers, and addressing a pivotal void in the extant scholarly discourse.

Corporate information preference and stock return volatility

ABSTRACT This paper models the effect of corporate information preference on stock return volatility based on optimization problems of information decisions for firms and investors. Our model hypothesizes a positive correlation between corporate information preference and volatility. Utilizing the ideal institutional background of the Chinese stock market, we empirically confirm that corporate information preference has a positive impact on volatility, particularly for firms facing more severe financial distress, limited investor attention, and less analyst coverage. Our study provides a new perspective for analyzing the interaction between information supply and asset price dynamics.

Make Hay While The Sun Shines: An Empirical Study of Maximum Price, Regret and Trading Decisions

Abstract Time-constant trading thresholds are optimal for a large class of preferences and asset price dynamics, including Expected Utility and the S-shaped reference-dependent utility of Prospect Theory. Such thresholds imply selling stocks at the maximum price since purchase. We use a large discount brokerage dataset containing US households’ trading records between 1991 and 1996 to document that in 31.6% of cases the stocks sold for a gain are sold on the day when the maximum since purchase occurs. However, not all stocks are sold at a maximum since purchase and the propensity to sell changes depending on how far in time and price the stock is with respect to this past maximum. We find that the propensity to sell initially increases as the price is closer to the past maximum but it then decreases when the price gets in the closest region to the past maximum, leading to an inverse U-shape; and that investors are less likely to sell a gain, the further away in time the maximum price occurred. Studying the joint effect of price and time distance, we find that the propensity to sell is highest at low time distance and high price distance from the maximum since purchase. We relate these findings to regret, belief updating, and attention.

Heterogeneous beliefs with information processing capacity constraints and asset pricing in a monetary economy

This paper proposes a monetary model to explore the influences of heterogeneous beliefs and information processing capacity constraints on the dynamics of asset prices. The capacity constraints not only influence the estimations of the capacity constrained investor, but also generate persistent disagreements among the investors. The model implies that reducing the levels of capacity constraints can alleviate the influences of heterogeneous beliefs and helps to stabilize financial markets. The model also reveals that introducing heterogeneous beliefs about both real and nominal sectors not only leads the stock with low monetary policy exposure to have significantly higher average return than the stock with high monetary policy exposure, but also can explain the mixed results about the relationship between the volatility and the risk premium of the aggregate stock market.

Option Pricing Using a Skew Random Walk Binary Tree

We develop a binary tree pricing model with underlying asset price dynamics following Itô–McKean skew Brownian motion. Our work was motivated by the Corns–Satchell, continuous-time, option pricing model. However, the Corns–Satchell market model is incomplete, while our discrete-time market model is defined in the natural world, extended to the risk-neutral world under the no-arbitrage condition where derivatives are priced under uniquely determined risk-neutral probabilities, and is complete. The skewness introduced in the natural world is preserved in the risk-neutral world. Furthermore, we show that the model preserves skewness under the continuous-time limit. We provide empirical applications of our model to the valuation of European put and call options on exchange-traded funds tracking the S&P Global 1200 index.

Heterogeneous beliefs with preference interdependence and asset pricing

We study the equilibrium implications of heterogeneous beliefs and preference interdependence on the dynamics of asset prices and the resulting market behaviors. For the purpose of capturing the preference interdependence, we allow each investor’s utility not only depends their own terminal wealth, but also depends on the other one’s. We show that the preference interdependence generates a form of “herding behavior”, that induces the investors’ beliefs to coverage with each other. Therefore, the degree of the influences of the heterogeneous beliefs is attenuated. We also present that the heterogeneous beliefs impose negative effects on the prices of stocks and call options, whereas generate positive effects on the prices of put options. Due to the attenuation effect, the preference interdependence can have a strong promoting effect on the prices of stocks and call options and a strong depressing effect on the prices of put options. Furthermore, the attenuation effect also imposes a strong depressing effect on the volatilities of stocks even there exists high degree of heterogeneous beliefs among the investors. Finally, we present that both the heterogeneous beliefs and the preference interdependence can result in co-movement between stock and option markets.

Another look at the price clustering behavior: evidence from the Muscat stock exchange

PurposeThe price clustering behavior is becoming a core part of the market efficiency theory especially with the development of trading strategies and the occurrence of major and unprecedented shocks which have led to severe inquiry regarding asset price dynamics and their distribution. However, research on emerging stock market is scant. The study contributes to the literature on price clustering by investigating an active emerging stock market, the Muscat stock market one of the Arabian Gulf Markets.Design/methodology/approachThis research adopts the artificial intelligence technique and other statistical estimation procedure in understanding the price clustering patterns in Muscat stock market and their main determinants.FindingsThe findings reveal that stock prices are marked by clustering behavior as commonly highlighted in the previous studies. However, we found strong evidence of price preferences to cluster on numbers closer to zero than to one. We also show that the nature of firm’s activity matters for price clustering behavior. In addition, firms with traded bonds in Oman market experienced a substantial less stock price clustering than other firms. Clustered stock prices are more likely to have higher prices and higher volatility of price. Finally, clustering raised when the market became highly uncertain during the Covid-19 crisis especially for the financial firms.Originality/valueThis study provides novel results on price clustering literature especially for an active emerging market and during the Covid-19 pandemic crisis.

Optimal investment in ambiguous financial markets with learning

We consider the classical multi-asset Merton investment problem under drift uncertainty, i.e. the asset price dynamics are given by geometric Brownian motions with constant but unknown drift coefficients. The investor assumes a prior drift distribution and is able to learn by observing the asset prize realizations during the investment horizon. While the solution of an expected utility maximizing investor with constant relative risk aversion (CRRA) is well known, we consider the optimization problem under risk and ambiguity preferences by means of the KMM (Klibanoff et al., 2005) approach. Here, the investor maximizes a double certainty equivalent. The inner certainty equivalent is for given drift coefficient, the outer is based on a drift distribution. Assuming also a CRRA type ambiguity function, it turns out that the optimal strategy can be stated in terms of the solution without ambiguity preferences but an adjusted drift distribution. To the best of our knowledge an explicit solution method in this setting is new. We rely on some duality theorems to prove our statements.Based on our theoretical results, we are able to shed light on the impact of the prior drift distribution as well as the consequences of ambiguity preferences via the transfer to an adjusted drift distribution, i.e. we are able to explain the interaction of risk and ambiguity preferences. We compare our results with the ones in a pre-commitment setup where the investor is restricted to deterministic strategies. It turns out that (under risk and ambiguity aversion) an infinite investment horizon implies in both cases a maximin decision rule, i.e. the investor follows the worst (best) Merton fraction (over all realizations of it) if she is more (less) risk averse than a log-investor. We illustrate our findings with an extensive numerical study.

Quantum Leap: A Price Leap Mechanism in Financial Markets

This study explores the quantum leapfrog mechanism within the context of quantum finance and presents a new interpretation of established financial models through a quantum perspective. In quantum physics, the well-documented phenomenon of particles tunneling through energy barriers has a parallel in finance. We propose a quantum financial leapfrog model in which asset prices make quantum leaps, penetrating market “energy barriers” in non-sequential advances. By leveraging the Hamiltonian operator and the Schrödinger equation, our approach simulates the dynamics of asset prices in a manner akin to the trajectories of particles in quantum mechanics. We draw an analogy between financial markets and gravitational fields, and from this we derive energy equations for pricing orbits. Using path integration techniques, we map out potential price transitions between these orbits, which are guided by the calculation of minimal energy barriers. Furthermore, we introduce a market “propagator” that aligns with the uncertainty principle, identifying the optimal price pathways. Our findings provide new insights and methodologies for navigating the complexities of financial markets, underscoring the significant potential of quantum approaches in the field of finance. These findings have theoretical implications for a variety of market stakeholders, offering strategic guidance and a reference point. We expect that the advancement of the quantum financial leapfrog theory will refine analytical methods and enhance investment strategies in practical financial applications.

A Continuous Time Framework for Sequential Goal-Based Wealth Management

We develop a continuous time framework for sequential goals-based wealth management. A stochastic factor process drives asset price dynamics and the client’s goal amount and income. We prove the weak dynamic programming principle for the value function of our control problem, which we show to be the unique viscosity solution of the corresponding Hamilton-Jacobi-Bellman equation. We develop an equivalent and computationally efficient representation of the Hamiltonian, which yields the optimal portfolio within a factor-dependent opportunity set defined by the maximum and minimum variance hypersurfaces. Our analysis shows that it is optimal to fund an expiring goal up to the level where the marginal benefit of additional fundedness is exceeded by the opportunity cost of diverting wealth from future goals. An all-or-nothing investor is more risk averse toward an approaching goal deadline if well funded, but more risk seeking if not on track with upcoming goals, compared with an investor with flexible goals. This paper was accepted by David Simchi-Levi, finance. Funding: This work was supported by the Natural Sciences and Engineering Research Council of Canada [Discovery Grant RGPIN-2020-06290] and Fi-Tek.

The Financial Market of Indices of Socioeconomic Well-Being

This study discusses how financial economic theory and its quantitative tools can be applied to create socioeconomic indices and develop a financial market for the so-called “socioeconomic well-being indices”. In this study, we quantify socioeconomic well-being by assigning a dollar value to the well-being factors of selected countries; this is analogous to how the Dow 30 encapsulates the financial health of the US market. While environmental, social, and governance (ESG) financial markets address socioeconomic issues, our focus is broader, encompassing national citizens’ well-being. The dollar-denominated socioeconomic indices for each country can be viewed as financial assets that can serve as risky assets for constructing a global index, which, in turn, serves as a “market of well-being socioeconomic index”. This novel global index of well-being, paralleling the Dow Jones Industrial Average (DJIA), provides a comprehensive representation of the world’s socioeconomic status. Through advanced financial econometrics and dynamic asset pricing methodologies, we evaluate the potential for significant downturns in both the socioeconomic well-being indices of individual countries and the aggregate global index. This innovative approach allows us to engineer financial instruments akin to portfolio insurance, such as index puts, designed to hedge against these downturn risks. Our findings propose a financial market model for well-being indices, encouraging the financial industry to adopt and trade these indices as mechanisms to manage and hedge against downturn risks in well-being.

On Quantum Ambiguity and Potential Exponential Computational Speed-Ups to Solving Dynamic Asset Pricing Models

On Quantum Ambiguity and Potential Exponential Computational Speed-Ups to Solving Dynamic Asset Pricing Models

HOW DO INVESTOR’S EXPECTATIONS AND EMOTIONS DRIVE FINANCIAL ASSET PRICES IN TIMES OF CRISES AND UNCERTAINTY: THE ANALYSIS OF EXPERTS’ OPINIONS

This paper analyses the main views of several experts (academics and professionals) who met at the 2nd IEAP (Investor’s Emotions and Asset Price) meeting that was organized at the IAE Lille University School of Management on the 1st Februrary 2023. In particular, we analyze, first, the dynamics of financial asset prices as well as commodities prices during the last decade and we provide some projections about the future. Second, we discuss the role played by investors’ emotions as well as sentiment in driving financial asset prices and the contribution of fundamental factors while distinguishing financial markets from commodities and cryptocurrencies. Third, we document the dynamics of financial asset prices in the context of uncertainty, geopolitical tensions, post-COVID-19 period and high inflation. Finally, we provide some suggestions about the further extension of usual financial asset price models to include behavioral factors (emotions, sentiment) as well as uncertainty.JEL Classification: G10, G12, G41.

Stock exchange activities in the conditions of the spread of artificial intelligence technologies

Methods. During the research, the following methods were used: general and specific – when distinguishing the specifics of stock market activity in the conditions of the spread of artificial intelligence; comparison – to study distinctive features in the activity of Ukrainian stock exchanges; analysis and synthesis – while identifying the existing approaches in economic science to defining the essence of the concept of «technical analysis»; grouping – when classifying technical analysis methods used in stock market activity. Results. The article examines stock market activity in the context of digitalization of the economy and, in particular, the spread of artificial intelligence. The directions of the influence of the latter on the mechanism of stock exchange transactions are demonstrated, and the main features of stock exchanges under the new realities are defined. The directions of activity of Ukrainian exchanges are analyzed and its specific features are determined in comparison with the work of similar structures in countries with a developed market. Specifics of carrying out technical analysis of exchange goods are considered. The theoretical approaches available in economic science to define the essence of the concept of «technical analysis» are singled out. The component systems of technical analysis methods are outlined and their classification is carried out. The potential capabilities of artificial intelligence in forecasting the price dynamics of stock exchange assets have been identified. Novelty. The specifics of stock market activity in the conditions of the spread of artificial intelligence are highlighted, its capabilities in forecasting price changes are demonstrated. Practical value. The results of the research can be useful both to scientists studying the problems of the functioning of the stock market, as well as to business analysts of stock market activity.

Communication, networks and asset price dynamics: a survey

In this paper, we provide a wide-ranging survey of the state of the art in the area of communication and asset price dynamics. We start out by documenting empirical evidence that social communication influences investment decisions and asset prices, before turning to the main modelling approaches in the literature (both past and present). We discuss models of belief-updating based on observed performance; models of herd behaviour; and models with social interactions that arise from preferences for conformity or contrarianism. Our main contribution is to introduce readers to a social network approach which has been widely used in the opinion dynamics literature, but only recently applied to asset pricing. In the final part, we show how recent contributions to both modelling and empirical work are using the social network approach to improve our understanding of financial markets and asset price dynamics. We conclude with some thoughts on fruitful avenues for future research.