Narrow Results

Our study is the first to examine the pricing effect of U.S. trade policy uncertainty (TPU) on Chinese stocks. We estimate the U.S. TPU beta, which measures Chinese stock exposure to the U.S. TPU index. Both portfolio analyses and cross-sectional regressions suggest a significantly negative relation between the U.S. TPU beta and expected returns, which cannot be explained by other pricing factors. The stocks in the lowest U.S. TPU beta quintile can generate 3.48% higher annual returns compared to stocks in the highest U.S. TPU beta quintile. Furthermore, we provide two potential mechanisms that include a real economy channel and a behavioral finance channel using vector autoregression models. Our results indicate that the negative premium can be explained by both demanding more of high TPU beta stocks in hedging against adverse effects from TPU and selling more of low TPU beta stocks due to pessimistic beliefs of noise trader.

This paper digests three-factor model by exploring the average impacts of factors on portfolio returns and how factors interact with each other. To do this, we use SHapley Additive exPlanations method (SHAP) to interpret the results obtained by XGBoost. We find that the factors have different impacts on portfolio returns and interact with each other in different ways. We also find that the average impacts of factors on portfolio returns are similar before and after the publication of the three-factor model and the 2008 financial crisis but the interactions between factors vary across times.

We develop an asset pricing model based on the interaction of heterogeneous trading groups. In addition to the two main trader groups, fundamentalists and trend-chasing chartists, we include a third significant group known as contrarian chartists. We model the case of opportunistic contrarian behavior, where the contrarian group disagrees with the trend-chasing chartists only when the return differential is high. We also consider absolute contrarian behavior, in which the contrarians consistently disagree with trend-chasers. The models are nonlinear planar maps, exhibiting period doubling, Neimark–Sacker and global bifurcations leading to local chaotic behavior. Absolute contrarian behavior is found to have a moderating effect on price change, while opportunistic contrarian behavior is found to further complicate the price cycles present in other models.

A simple asset pricing model is developed to take into account two important characteristics in global investments: market segmentation and noise trader risk. Our results show the removal of international investment barriers and cross-border listings have not led to a fully integrated international capital market. We also show that different degree of investor rationality across borders induces an additional component of risk premium which is related to the "noise spill-over effect".

This paper proposes a theoretical model which is used to illustrate that transactions costs and a risk premium are not sufficient to explain the excess currency bid-ask spread. It illustrates that only in market structures that engender market power can foreign exchange dealers widen their currency bid-ask spread to exploit adverse economic forces and also exploit a relatively price inelastic demand for foreign exchange to charge higher-than-market-determined risk premiums thus charging an excess currency bid-ask spread.

The paradox of the Stop-Loss-Start-Gain trading strategy is resolved by showing that along the hyperfinite timeline the strategy incurs infinitesimal losses summing up to a non-infinitesimal amount. As a consequence, the Black–Scholes formula is derived using only hyperreal arithmetic and Riemann sum, probably the most elementary derivation thus far.

Several financial markets impose daily price limits on individual securities. Once a price limit is triggered, investors observe either the limit floor or ceiling, but cannot know with certainty what the true equilibrium price would have been in the absence of such limits. The price limits in most exchanges are typically based on a percentage change from the previous day's closing price, and can be expressed as return limits. We develop a Bayesian forecasting model in the presence of return limits, assuming that security returns are governed by identically and independently shifted-exponential random variables with an unknown parameter. The unique features of our Bayesian model are the derivations of the posterior and predictive densities. Several numerical predictions are generated and depicted graphically. Our main theoretical result with policy implications is that when return-limit regulations are tightened, the price-discovery process is impeded and investor's welfare is reduced.

A new framework for asset price dynamics is introduced in which the concept of noisy information about future cash flows is used to derive the corresponding price processes. In this framework an asset is defined by its cash-flow structure. Each cash flow is modelled by a random variable that can be expressed as a function of a collection of independent random variables called market factors. With each such "X-factor" we associate a market information process, the values of which we assume are accessible to market participants. Each information process consists of a sum of two terms; one contains true information about the value of the associated market factor, and the other represents "noise". The noise term is modelled by an independent Brownian bridge that spans the interval from the present to the time at which the value of the factor is revealed. The market filtration is assumed to be that generated by the aggregate of the independent information processes. The price of an asset is given by the expectation of the discounted cash flows in the risk-neutral measure, conditional on the information provided by the market filtration. In the case where the cash flows are the dividend payments associated with equities, an explicit model is obtained for the share-price process. Dividend growth is taken into account by introducing appropriate structure on the market factors. The prices of options on dividend-paying assets are derived. Remarkably, the resulting formula for the price of a European-style call option is of the Black–Scholes–Merton type. We consider the case where the rate at which information is revealed to the market is constant, and the case where the information rate varies in time. Option pricing formulae are obtained for both cases. The information-based framework generates a natural explanation for the origin of stochastic volatility in financial markets, without the need for specifying on an ad hoc basis the dynamics of the volatility.

Even though investors' view of risk is generally regarded as related to the downside of the return distribution the CAPM beta is still a widely used measure of systematic risk. A number of studies compare the empirical performance of CAPM beta and downside beta in explaining the variation in portfolio returns and report mixed results. This paper provides a basis for explaining such mixed results. Using data generating processes in the mean-variance and mean-lower partial moment frameworks, analytical relationships between the CAPM beta and downside beta are derived. The derived relationships reveal that the association between the two systematic risk measures is to a great extent dependent on the volatility of the market portfolio returns and the deviation of the target rate from the risk-free rate. How the relationships derived here may be used in practice is demonstrated using empirical data.

In this manuscript, we develop a multilevel framework for the pricing of a European call option based on multiresolution techniques. In this approach, the Black–Scholes equation is transformed via finite differences into a system of linear equations, where the form of the implicit operator is used to construct coarse grid projectors. The reduction of the computational resource is achieved by truncating small wavelet coefficients. However, because traditional wavelets fail to prevent oscillations from developing in the Greeks, a multilevel approach is used to retain smoothness in Gamma by incorporating derivative information into the multiresolution analysis.

This paper provides a detailed overview of the current research linking systemic risk, financial crises and contagion effects among assets on the one hand with asset allocation and asset pricing theory on the other hand. Based on the ample literature about definitions, measurement and properties of systemic risk, we derive some elementary ingredients for models of financial contagion and assess the current state of knowledge about asset allocation and asset pricing with explicit focus on systemic risk. The paper closes with a brief outlook on future research possibilities and some recommendations for the further development of capital market models incorporating financial contagion.

We introduce the notion of κ-entropy (κ ∈ ℝ, |κ| ≤ 1), starting from Kaniadakis' (2001, 2002, 2005) one-parameter deformation of the ordinary exponential function. The κ-entropy is in duality with a new class of utility functions which are close to the exponential utility functions, for small values of wealth, and to the power law utility functions, for large values of wealth. We give conditions on the existence and on the equivalence to the basic measure of the minimal κ-entropy martingale measure. Moreover, we provide characterizations of its density as a κ-exponential function. We show that the minimal κ-entropy martingale measure is closely related to both the standard entropy martingale measure and the well known q-optimal martingale measures. We finally establish the convergence of the minimal κ-entropy martingale measure to the minimal entropy martingale measure as κ tends to 0.

We construct a model for liquidity risk and price impacts in a limit order book setting with depth, resilience and tightness. We derive a wealth equation and a characterization of illiquidity costs. We show that we can separate liquidity costs due to depth and resilience from those related to tightness, and obtain a reduced model in which proportional costs due to the bid-ask spread is removed. From this, we obtain conditions under which the model is arbitrage free. By considering the standard utility maximization problem, this also allows us to obtain a stochastic discount factor and an asset pricing formula which is consistent with empirical findings (e.g., Brennan and Subrahmanyam (1996); Amihud and Mendelson (1986)). Furthermore, we show that in limiting cases for some parameters of the model, we derive many existing liquidity models present in the arbitrage pricing literature, including Çetin et al. (2004) and Rogers and Singh (2010). This offers a classification of different types of liquidity costs in terms of the depth and resilience of prices.

A heat kernel approach is proposed for the development of a novel method for asset pricing over a finite time horizon. We work in an incomplete market setting and assume the existence of a pricing kernel that determines the prices of financial instruments. The pricing kernel is modeled by a weighted heat kernel driven by a multivariate Markov process. The heat kernel is chosen so as to provide enough freedom to ensure that the resulting model can be calibrated to appropriate data, e.g. to the initial term structure of bond prices. A class of models is presented for which the prices of bonds, caplets, and swaptions can be computed in closed form. The dynamical equations for the price processes are derived, and explicit formulae are obtained for the short rate of interest, the risk premium, and for the stochastic volatility of prices. Several of the closed-form models presented are driven by combinations of Markovian jump processes with different probability laws. Such models provide a basis for consistent applications in various market sectors, including equity markets, fixed-income markets, commodity markets, and insurance. The flexible multidimensional and multivariate structure on which the resulting price models are based lends itself well to the modeling of dependence across asset classes. As an illustration, the impact of spiraling debt, a typical feature of a financial crisis, is modeled explicitly, and the contagion effects can be readily observed in the dynamics of the associated asset returns.

We look at the economic significance and at the robustness of the new-generation, tent-shaped return-predicting factors in US Treasuries. We find that, in itself, the precise tent shape is neither robust nor important for predictability. However, we explain why the high number of regressors needed to build a tent factor are required for high predictability, and we provide an economic interpretation for the finding.

We consider the problem of determining the Lévy exponent in a Lévy model for asset prices given the price data of derivatives. The model, formulated under the real-world measure $\mathbb{P}$, consists of a pricing kernel ${\{{\pi }_t\}}_{t\ge 0}$ together with one or more non-dividend-paying risky assets driven by the same Lévy process. If ${\{S_t\}}_{t\ge 0}$ denotes the price process of such an asset, then ${\{{\pi }_t{S}_t\}}_{t\ge 0}$ is a $\mathbb{P}$-martingale. The Lévy process ${\{{\xi }_t\}}_{t\ge 0}$ is assumed to have exponential moments, implying the existence of a Lévy exponent $\psi (\alpha )=t^{-1}\mathrm{\text{log}}𝔼({\mathrm{\text{e}}}^{\alpha {\xi }_t})$ for $\alpha$ in an interval $A\subset ℝ$ containing the origin as a proper subset. We show that if the prices of power-payoff derivatives, for which the payoff is $H_T={({\zeta }_T)}^q$ for some time $T>0$, are given at time $0$ for a range of values of $q$, where ${\{{\zeta }_t\}}_{t\ge 0}$ is the so-called benchmark portfolio defined by ${\zeta }_t=1/{\pi }_t$, then the Lévy exponent is determined up to an irrelevant linear term. In such a setting, derivative prices embody complete information about price jumps: in particular, the spectrum of the price jumps can be worked out from current market prices of derivatives. More generally, if $H_T={(S_T)}^q$ for a general non-dividend-paying risky asset driven by a Lévy process, and if we know that the pricing kernel is driven by the same Lévy process, up to a factor of proportionality, then from the current prices of power-payoff derivatives we can infer the structure of the Lévy exponent up to a transformation $\psi (\alpha )\to \psi (\alpha +\mu )-\psi (\mu )+c\alpha$, where $c$ and $\mu$ are constants.

A growing body of literature suggests that heavy tailed distributions represent an adequate model for the observations of log returns of stocks. Motivated by these findings, here, we develop a discrete time framework for pricing of European options. Probability density functions of log returns for different periods are conveniently taken to be convolutions of the Student’s $t$-distribution with three degrees of freedom. The supports of these distributions are truncated in order to obtain finite values for the options. Within this framework, options with different strikes and maturities for one stock rely on a single parameter — the standard deviation of the Student’s $t$-distribution for unit period. We provide a study which shows that the distribution support width has weak influence on the option prices for certain range of values of the width. It is furthermore shown that such family of truncated distributions approximately satisfies the no-arbitrage principle and the put-call parity. The relevance of the pricing procedure is empirically verified by obtaining remarkably good match of the numerically computed values by our scheme to real market data.

This paper presents an asset-pricing model for an integrated financial economy in a multi-currency framework in which three risk dimensions drive asset prices: global risk, regional risk and country-specific risk. Under this framework, all risks are common since, by trading assets across countries, agents can load on foreign risk. However, the model’s solution imposes restrictions on the loading coefficients. As a result, only the dispersion in global and regional coefficients is needed to explain currency returns. The model is tested with a linear-factor model at the currency-pair level using a sample of 42 countries located in five different regions. It is shown that, as the model predicted, regional and global factors help explain the dispersion in currency returns.

This paper studies the effects of strategic debt service, asymmetric information and their interaction on the valuation of corporate securities and on corporate financing decisions. By introducing information asymmetry into a continuous-time setting, our model is able to integrate these two factors in a unified framework. Such a model allows for obtaining valuation results in a separating equilibrium.

The basic results of this paper imply that the risk premium of debt could be partly contributed by information effect. This part of risk premium could be very significant for those good firms with a project which will produce much higher cash flows than what the market expects. We also find that a firm's financing decision depends on its primitives: firms are more apt to rely on equity if they have: (1) high growth potential, (2) riskier projects, (3) higher ratio of intangible assets to total assets and (4) lesser information asymmetry; firms would prefer debt, otherwise.

The cross-sectional relationship between expected returns and amortized spreads is studied in an overlapping-generations economy with an average investor. The commonality in liquidity is directly incorporated into the asset-pricing relation. In a static equilibrium, the amortized spread of an asset is related to its expected return through four channels; namely: the equilibrium zero-beta rate, the market risk premium, a level effect, and an incremental sensitivity effect. Although both are present over the entire period, their relative importance shifts from a significant level to a significant sensitivity effect from the earlier to most recent sub-period in the Canadian stock market.