Oil prices,US stock return,and the dependence between their quantiles

In this article, we examine the relationship between oil prices and US equities by proposing a novel quantile-on-quantile (QQ) approach to construct estimates of the effect that the quantiles of oil price shocks have on the quantiles of the US stock return. This approach captures the dependence between the distributions of oil price shocks and the US stock return and uncovers two nuance features in the oil–stock relationship. First, large, negative oil price shocks (i.e. low oil price shock quantiles) can affect US equities positively when the US market is performing well (i.e. at high US return quantiles). Second, while negative oil price shocks could affect the US stock market, the influence of positive oil price shocks is weak, which suggests that the relationship between oil prices on the US equities is asymmetric.     

Conditions Ensuring the Decomposition of Asset Demand for All Risk-Averse Investors

Abstract

The paper explores how the demand for a risky asset can be decomposed into an investment effect and a hedging effect by all risk-averse investors. This question has been shown to be complex when considered outside of the mean-variance framework. Dependence among returns on the risky assets is restricted to quadrant dependence and it is found that the demand for one risky asset can be decomposed into an investment component based on the risk premium offered by the asset and a hedging component used against the fluctuations in the return on the other risky asset. The paper also discusses how the class of quadrant-dependent distributions is related to that of two-fund separating distributions. This contribution opens up the search for broader distributional hypotheses suitable to asset demand models. Examples are discussed.     

Tail dependence between Bitcoin and financial assets: Evidence from a quantile cross-spectral approach

Using daily price data for Bitcoin and 10 representative financial assets from the stock, commodity, gold, foreign exchange and bond markets from 2011 to 2019, we study the tail dependence between returns for Bitcoin and these other financial assets using the novel “quantile cross-spectral dependence” approach of Baruník and Kley (2019). We find evidence of right-tail dependence between Bitcoin returns and the S&P 500 in the long term and weaker normal return dependence between Bitcoin and the US Dollar (USD)–Euro (EUR) foreign exchange rate in the monthly term. In addition, we note that the dependence between Bitcoin and commodity as well as oil, and silver decrease the most within their respective medium return quantiles over the short term. Furthermore, we document a one-way causality running from each of the financial assets considered to Bitcoin in different quantiles of the return distribution. In sum, our findings support the notion that Bitcoin can provide financial diversification in certain return quantiles (i.e., bear, normal, or bull asset conditions) and time frequencies (i.e., short, medium, or long term investment horizon).     

Alternative statistical distributions for estimating value-at-risk: theory and evidence

A number of applications presume that asset returns are normally distributed, even though they are widely known to be skewed leptokurtic and fat-tailed and excess kurtosis. This leads to the underestimation or overestimation of the true value-at-risk (VaR). This study utilizes a composite trapezoid rule, a numerical integral method, for estimating quantiles on the skewed generalized t distribution (SGT) which permits returns innovation to flexibly treat skewness, leptokurtosis and fat tails. Daily spot prices of the thirteen stock indices in North America, Europe and Asia provide data for examining the one-day-ahead VaR forecasting performance of the GARCH model with normal, student??s t and SGT distributions. Empirical results indicate that the SGT provides a good fit to the empirical distribution of the log-returns followed by student??s t and normal distributions. Moreover, for all confidence levels, all models tend to underestimate real market risk. Furthermore, the GARCH-based model, with SGT distributional setting, generates the most conservative VaR forecasts followed by student??s t and normal distributions for a long position. Consequently, it appears reasonable to conclude that, from the viewpoint of accuracy, the influence of both skewness and fat-tails effects (SGT) is more important than only the effect of fat-tails (student??s t) on VaR estimates in stock markets for a long position.     

Asymmetric tail dependence between green bonds and other asset classes

This study analyzes the tail-dependence between green bonds and other asset classes including energy markets, stock markets, and conventional bonds. The study employs the cross- quantilogram method to identify the cross-quantile dependence between green bonds and other assets. Our data set covers the U.S. and European asset markets between October 2014 and February 2021. The empirical results show that the spillovers between asset classes and green bonds vary widely across the quantiles, indicating that the hedging benefits of green bonds against conventional asset classes differ across extreme and normal market conditions.     

Risk and efficiency in the Central and Eastern European banking industry under quantile analysis

This paper estimates cost efficiency in the banking industry of 11 Central and Eastern European (CEE) countries over the period 1998–2005 using a quantile regression analysis. Our purpose is to investigate for the first time whether cost efficiency in CEE banks differs across quantiles of the conditional distribution. We employ stochastic frontier analysis across quantiles using the Distribution-Free Approach. The reported evidence demonstrates lower efficiency scores for higher conditional distributions. The paper goes further into a second-stage analysis to investigate how risk, measured by non-performing loans and loans loss provisions, affects bank efficiency across quantiles. This second-stage analysis finds that risk asserts a negative impact on cost efficiency, especially in high-order quantiles. Finally, the paper investigates the relationship between bank-specific ‘z’ variables, such as structural reforms, bank concentration and profitability, and cost efficiency across quantiles.     

EVIDENCE OF SHIFTS IN PORTFOLIO ASSET COMPOSITION AS A MARKET TIMING TOOL

Previous research has investigated portfolio timing success by analyzing possible shifts in the beta of professionally managed portfolios. The methodology used by these studies usually involves calculating the betas of portfolios under varing market conditions using ex post holding period yields. Since a portfolio's beta can shift for reasons other than timing efforts, the results and interpretation of this type of analysis are limited. This paper takes a more direct approach to the analysis of timing by analyzing shifts in the asset composition of professionally managed portfolios. The asset composition is first analyzed to determine if portfolio managers are attempting to adjust risk exposure. Any shifts that are identified are compared to the market conditions that existed subsequent to the shift to determine if the shift was appropriate in terms of correct timing.     

Optimal Reinsurance and Investment for a Jump Diffusion Risk Process under the CEV Model

Abstract

We consider an optimal reinsurance-investment problem of an insurer whose surplus process follows a jump-diffusion model. In our model the insurer transfers part of the risk due to insurance claims via a proportional reinsurance and invests the surplus in a “simplified” financial market consisting of a risk-free asset and a risky asset. The dynamics of the risky asset are governed by a constant elasticity of variance model to incorporate conditional heteroscedasticity. The objective of the insurer is to choose an optimal reinsurance-investment strategy so as to maximize the expected exponential utility of terminal wealth. We investigate the problem using the Hamilton-Jacobi-Bellman dynamic programming approach. Explicit forms for the optimal reinsuranceinvestment strategy and the corresponding value function are obtained. Numerical examples are provided to illustrate how the optimal investment-reinsurance policy changes when the model parameters vary.     

Pricing Perpetual Options for Jump Processes

Abstract

We consider two models in which the logarithm of the price of an asset is a shifted compound Poisson process. Explicit results are obtained for prices and optimal exercise strategies of certain perpetual American options on the asset, in particular for the perpetual put option. In the first model in which the jumps of the asset price are upwards, the results are obtained by the martingale approach and the smooth junction condition. In the second model in which the jumps are downwards, we show that the value of the strategy corresponding to a constant option-exercise boundary satisfies a certain renewal equation. Then the optimal exercise strategy is obtained from the continuous junction condition. Furthermore, the same model can be used to price certain reset options. Finally, we show how the classical model of geometric Brownian motion can be obtained as a limit and also how it can be integrated in the two models.     

Do asset impairments and the associated disclosures resolve uncertainty about future returns and reduce information asymmetry?

The objective of this study is to provide insights into how Australian listed firms are implementing AASB 136 Impairments of Assets. Our first concern is whether uncertainty about future returns and information asymmetry motivates the recognition of asset impairments. We find no evidence that the recognition of asset impairments is associated with higher uncertainty about future returns. Furthermore, we find no evidence that the recognition of asset impairments is associated with higher information asymmetry. Our second concern is whether asset impairments and the associated disclosures provide information that reduces uncertainty about future returns and information asymmetry. While we find some evidence that asset impairments are associated with decreases in information asymmetry before the financial crisis, during the financial crisis, asset impairments are associated with increases in both measurement uncertainty and information asymmetry.     

Asset allocation and portfolio performance: Evidence from university endowment funds

We use university endowment funds to study the relationship between asset allocation decisions and performance in multiple asset class portfolios. Although endowments differ substantially in asset class composition, policy portfolio returns and volatilities are remarkably similar across the sample. The risk-adjusted performance of the average endowment is negligible, but actively managed funds generate significantly larger alphas than passive ones. This is consistent with endowment managers exploiting their security selection abilities by over-weighting asset classes in which they have superior skills. Contrary to both theory and prevailing beliefs, asset allocation is not related to portfolio returns in the cross-section but does indirectly influence performance.     

A skeptical appraisal of asset pricing tests

It has become standard practice in the cross-sectional asset pricing literature to evaluate models based on how well they explain average returns on size-B/M portfolios, something many models seem to do remarkably well. In this paper, we review and critique the empirical methods used in the literature. We argue that asset pricing tests are often highly misleading, in the sense that apparently strong explanatory power (high cross-sectional R²s and small pricing errors) can provide quite weak support for a model. We offer a number of suggestions for improving empirical tests and evidence that several proposed models do not work as well as originally advertised.     

Long memory in financial markets: A heterogeneous agent model perspective

During last decades, studies on asset pricing models witnessed a paradigm shift from rational expectation and representative agent to an alternative, behavioral view, where agents are heterogeneous and boundedly rational. In this paper, we model the financial market as an interaction of two types of boundedly rational investors — fundamentalists and chartists. We examine the dynamics of the market price and market behavior, which depend on investors' behavior and the interaction of the two types of investors. Numerical simulations of the corresponding stochastic model demonstrate that the model is able to replicate the stylized facts of financial time series, in particular the long-term dependence (long memory) of asset return volatilities. We further investigate the source of the long memory according to asset pricing mechanism of our model, and provide evidences of long memory by applying the modified R/S analysis. Our results demonstrate that the key parameter that has impact on the long memory is the speed of the price adjustment of the market maker at the equilibrium of demand and supply.     

Modeling the contemporaneous duration dependence for high-frequency stock prices

This paper proposes a novel copula approach to model the contemporaneous duration dependence for high-frequency (HF) stock prices via the bivariate hazard function. This method is useful in understanding the mechanism through which the prices of financial assets jointly adjust to reflect new information. In the empirical analysis, we use the HF data on the APPLE and IBM stocks to illustrate the feasibility of our approach. In brief, the main findings are as follows: (1) there is a strong evidence of contemporaneous duration dependence between the prices of these stocks and (2) as a result the estimators of the bivariate hazard function are sensitive to the choice of copulas under our study.     

Diversification in the hedge fund industry

We provide evidence of a significant relation between diversification and performance in the hedge fund industry. Measuring diversification across four distinct dimensions, we find a significant positive relation between hedge fund performance and diversification across sectors and asset classes. We show that on a risk adjusted basis, hedge funds that diversify across sectors and asset classes outperform other funds by an average of 1.1% per year. However, diversification across styles and geographies exhibits a significant negative association with hedge fund returns. Funds that diversify across styles and geographies underperform other funds by an average of 1% per year. For fund of hedge funds, we find a significant positive relation between performance and diversification across sectors. However, diversifying across asset classes and geographies is found to exhibit a negative relation with fund performance. Finally, we find that the motive to engage in diversification is consistent with managerial incentive structure in the hedge fund industry.     

Black swan events and safe havens: The role of gold in globally integrated emerging markets

There is evidence to suggest that gold acts as both a hedge and a safe haven for equity markets over recent years, and particularly during crises periods. Our work extends the recent literature on hedging and diversification roles of gold by analyzing its interaction with the stock markets of the leading emerging economies, the BRICS. While they generally exhibit a high growth rate, these economies still experience a pronounced vulnerability to external shocks, particularly to commodity price fluctuations. Using a multi-scale wavelet approach and a GARCH-based copula methodology, we mainly show evidence of: (i) the time-scale co-evolvement patterns between BRICS stock markets and gold market, with some profound regions of concentrated extreme variations; and (ii) a strong time-varying asymmetric dependence structure between those markets. These findings are essential for risk diversification and portfolio hedging strategies among the investigated markets.     

Asset pricing with skewed-normal return

Despite the fact that it is easy to see intuitively why skewness and coskewness should matter for asset pricing, it is difficult to build a model that links analytically skewness premia to deep structural parameters governing preferences and the distribution of shocks. This paper takes up the challenge and studies the effect of skewness and coskewness on asset valuation. To reach this important goal, asset returns skewness is modeled with promising Azzalini’s [1985. Scandinavian Journal of Statistics 12, 171–178] skew-normal distribution. With this assumption, we are now able to derive explicit expressions of assets skewness premiums and to shed a new light on asset valuation.     

Quantile regression analysis of hedge fund strategies

Extending previous work on hedge fund pricing, this paper introduces the idea of modelling the conditional quantiles of hedge fund returns using a set of risk factors. Quantile regression analysis provides a way of understanding how the relationship between hedge fund returns and risk factors changes across the distribution of conditional returns. We propose a Bayesian approach to model comparison which provides posterior probabilities for different risk factor models that can be used for model averaging. The most relevant risk factors are identified for different quantiles and compared with those obtained for the conditional expectation model. We find differences in factor effects across quantiles of returns, which suggest that the standard conditional mean regression method may not be adequate for uncovering the risk-return characteristics of hedge funds. We explore potential economic impacts of our approach by analysing hedge fund single strategy return series and by constructing style portfolios.     

Integrating market and credit risk: A simulation and optimisation perspective

We introduce a modelling paradigm which integrates credit risk and market risk in describing the random dynamical behaviour of the underlying fixed income assets. We then consider an asset and liability management (ALM) problem and develop a multistage stochastic programming model which focuses on optimum risk decisions. These models exploit the dynamical multiperiod structure of credit risk and provide insight into the corrective recourse decisions whereby issues such as the timing risk of default is appropriately taken into consideration. We also present an index tracking model in which risk is measured (and optimised) by the CVaR of the tracking portfolio in relation to the index. In-sample as well as out-of-sample (backtesting) experiments are undertaken to validate our approach. The main benefits of backtesting, that is, ex-post analysis are that (a) we gain insight into asset allocation decisions, and (b) we are able to demonstrate the feasibility and flexibility of the chosen framework.