The intertemporal capital asset pricing model with dynamic conditional correlations

The intertemporal capital asset pricing model of Merton (1973) is examined using the dynamic conditional correlation (DCC) model of Engle (2002). The mean-reverting DCC model is used to estimate a stock’s (portfolio’s) conditional covariance with the market and test whether the conditional covariance predicts time-variation in the stock’s (portfolio’s) expected return. The risk-aversion coefficient, restricted to be the same across assets in panel regression, is estimated to be between two and four and highly significant. The risk premium induced by the conditional covariation of assets with the market portfolio remains positive and significant after controlling for risk premia induced by conditional covariation with macroeconomic, financial, and volatility factors.     

Stock market volatility and equity returns: Evidence from a two-state Markov-switching model with regressors

This paper proposes a two-state Markov-switching model for stock market returns in which the state-dependent expected returns, their variance and associated regime-switching dynamics are allowed to respond to market information. More specifically, we apply this model to examine the explanatory and predictive power of price range and trading volume for return volatility. Our findings indicate that a negative relation between equity market returns and volatility prevails even after having controlled for the time-varying determinants of conditional volatility within each regime. We also find an asymmetry in the effect of price range on intra- and inter-regime return volatility. While price range has a stronger effect in the high volatility state, it appears to significantly affect only the transition probabilities when the stock market is in the low volatility state but not in the high volatility state. Finally, we provide evidence consistent with the ‘rebound’ model of asset returns proposed by Samuelson (1991), suggesting that long-horizon investors are expected to invest more in risky assets than short-horizon investors.     

The long-run performance of firms that issue convertible debt: an empirical analysis of operating characteristics and analyst forecasts

Many firms issue hybrid securities, such as convertible debt, instead of standard securities like straight debt or common equity. Theoretical arguments suggest that convertible debt minimizes costs for firms facing high debt- and equity-related external financing costs. Theory also suggests that an appropriately designed convertible security provides efficient investment incentives. We show, however, that firms on average perform poorly following the issuance of convertible debt. The empirical evidence suggests that the efficient investment decisions predicted by theory are not in fact achieved by the actual design and issuance of convertible debt securities. An alternative interpretation of convertible debt offers is that investors ration the participation of some issuers in the seasoned equity market.     

A Markov-switching generalized additive model for compound Poisson processes,with applications to operational loss models

This paper is concerned with modelling the behaviour of random sums over time. Such models are particularly useful to describe the dynamics of operational losses, and to correctly estimate tail-related risk indicators. However, time-varying dependence structures make it a difficult task. To tackle these issues, we formulate a new Markov-switching generalized additive compound process combining Poisson and generalized Pareto distributions. This flexible model takes into account two important features: on the one hand, we allow all parameters of the compound loss distribution to depend on economic covariates in a flexible way. On the other hand, we allow this dependence to vary over time, via a hidden state process. A simulation study indicates that, even in the case of a short time series, this model is easily and well estimated with a standard maximum likelihood procedure. Relying on this approach, we analyse a novel data-set of 819 losses resulting from frauds at the Italian bank UniCredit. We show that our model improves the estimation of the total loss distribution over time, compared to standard alternatives. In particular, this model provides estimations of the 99.9% quantile that are never exceeded by the historical total losses, a feature particularly desirable for banking regulators.     

Bank capital buffers in a dynamic model

We estimate a dynamic structural banking model to examine the interaction between risk-weighted capital adequacy and unweighted leverage requirements, their differential impact on bank lending, and equity buffer accumulation in excess of regulatory minima. Tighter risk-weighted capital requirements reduce loan supplies and lead to an endogenous fall in bank profitability, reducing bank incentives to accumulate equity buffers and, therefore, increasing the incidence of bank failure. Alternatively, tighter leverage requirements increase lending, preserve bank charter value, and incentives to accumulate equity buffers leading to lower bank failure rates.     

Fiscal shocks and spillovers in a dynamic two-country model

International Tax and Public Finance - This paper examines the cross-border effects of domestic fiscal shocks on foreign economic activities by constructing a two-country general equilibrium model....     

A random forest-based model for crypto asset forecasts in futures markets with out-of-sample prediction

In this study, a price prediction model for futures markets of crypto assets is presented. Random Forest was used to study three scenarios as a function of input variables: technical indicators, candlestick patterns and both simultaneously. In turn, the model parameters, the time intervals, and the most suitable investment horizons were studied. In addition to showing the results from the model, a one-year out-of-sample prediction was simulated. The entire year of 2020 was chosen because the three possible stock market scenarios occurred in this year: a sideways market, a bear market resulting from the global pandemic and an end-of-year bull market. Last, this out-of-sample simulation was analyzed as a real operation, that is, by retraining the model after each new collection of data, so that the model had the maximum information at all times. In conclusion, using candlestick patterns instead of technical indicators, improves the efficiency of the results.     

A Markov model for valuing asset prices in a dynamic bargaining market

This paper proposes a Markov chain model for studying the impact on asset prices of illiquidity associated with search and bargaining in an economy. The economy consists of finitely many agents who can trade only when they find each other, and any trade between agents changes the population of the agent types which affects the asset price in the future. Assuming that the equilibrium utility as well as the trade price is proportional to the asset dividend, we obtain the asset prices in steady state. Through extensive numerical experiments, we observe that the equilibrium prices exhibit the cutoff phenomenon (i.e. crash) as the fraction of pessimistic agents becomes large. Models with a market maker as well as irrational agents are also considered.     

Testing for long-run PPP in a system context: Evidence for the US,Germany and Japan

This paper extends the current literature on PPP by re-examining the validity of the PPP hypothesis for the three key currencies of the recent floating exchange rate period, in a multilateral framework. We argue that PPP testing is more adequate in a system context, which takes into account the dynamic interactions of exchange rates and prices of more than two economies, simultaneously. In the system analysis framework, some form of causality among the variables under consideration is also assessed empirically with the aid of weak exogeneity tests. The results illustrate the importance of the multilateral testing. Positive evidence for PPP is found: long-run PPP is supported for the US and Germany but also for the US and Japan, in contrast to evidence of earlier empirical studies. In addition, causality is found running from the US prices to the exchange rates and German and Japanese prices.     

Learning and shifts in long-run productivity growth

An extensive literature has analyzed the macroeconomic effects of shocks to the level of aggregate productivity; however, there has been little corresponding research on sustained shifts in the growth rate of productivity. In this paper, we examine the effects of shocks to productivity growth in a dynamic general equilibrium model where agents do not directly observe whether shocks are transitory or persistent. We show that an estimated Kalman filter model using real-time data describes economists’ long-run productivity growth forecasts in the United States extremely well and that filtering has profound implications for the macroeconomic effects of shifts in productivity growth.     

International stock–bond correlations in a simple affine asset pricing model

We use an affine asset pricing model to jointly value stocks and bonds. This enables us to derive endogenous correlations and to explain how economic fundamentals influence the correlation between stock and bond returns. The presented model is implemented for G7 post-war economies and its in-sample and out-of-sample performance is assessed by comparing the correlations generated by the model with conventional statistical measures. The affine framework developed in this paper is found to generate stock–bond correlations that are in line with empirically observed figures.     

A study on long-run inefficiency levels of a panel dynamic cost frontier under the framework of forward-looking rational expectations

This paper aims to provide a theoretical underpinning of the dynamic efficiency model pioneered by [Ahn, S.C., Good, D.H., Sickles, R.C., 2000. Estimation of long-run inefficiency levels: A dynamic frontier approach. Econometric Reviews 19, 461–492]. In the context of a quadratic loss function this paper formulates a multi-period forward-looking rational expectations model on the evolution of the technical inefficiency level, which correctly produces a dynamic panel data model. The model is illustrated using panel data of 112 French banks. Encouraging evidence of superiority in favor of the model is reached. Substantial cost inefficiency prevails in this industry, where the constituent banks are characterized as having volatile adjustment speeds toward their long-run steady states. The sample banks exhibit increasing returns to scale and product-mix economies.     

Non-homogeneous volatility correlations in the bivariate multifractal model

In this paper, we consider an extension of the recently proposed bivariate Markov-switching multifractal model of Calvet, Fisher, and Thompson [2006. “Volatility Comovement: A Multifrequency Approach.” Journal of Econometrics 131: 179–215]. In particular, we allow correlations between volatility components to be non-homogeneous with two different parameters governing the volatility correlations at high and low frequencies. Specification tests confirm the added explanatory value of this specification. In order to explore its practical performance, we apply the model for computing value-at-risk statistics for different classes of financial assets and compare the results with the baseline, homogeneous bivariate multifractal model and the bivariate DCC-GARCH of Engle [2002. “Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models.” Journal of Business & Economic Statistics 20 (3): 339–350]. As it turns out, the multifractal model with heterogeneous volatility correlations provides more reliable results than both the homogeneous benchmark and the DCC-GARCH model.     

Bivariate asymmetric GARCH models with heavy tails and dynamic conditional correlations

A bivariate generalized autoregressive conditional heteroskedastic model with dynamic conditional correlation and leverage effect (DCC-GJR-GARCH) for modelling financial time series data is considered. For robustness it is helpful to assume a multivariate Student-t distribution for the innovation terms. This paper proposes a new modified multivariate t-distribution which is a robustifying distribution and offers independent marginal Student-t distributions with different degrees of freedom, thereby highlighting the relationship among different assets. A Bayesian approach with adaptive Markov chain Monte Carlo methods is used for statistical inference. A simulation experiment illustrates good performance in estimation over reasonable sample sizes. In the empirical studies, the pairwise relationship between the Australian stock market and foreign exchange market, and between the US stock market and crude oil market are investigated, including out-of-sample volatility forecasts.     

A dynamic balance sheet management model for a Canadian chartered bank

This paper presents a linear programming (LP) model based on Markowitz portfolio theory for solving the balance sheet management problem for the domestic assets and liabilities of a Canadian chartered bank. Given the bank's initial position, its economic forecasts, and the constraints under which it operates, the model will determine the current and expected future balance sheet adjustments which will meet the bank's expected profits goal with the minimum possible risk. By parametrically varying the expected profits goal, the model will generate the set of risk-return efficient decisions. Bankers need examine only the set of efficient decisions to choose their optimal solution.     

Covariance dependent kernels,a Q-affine GARCH for multi-asset option pricing

This paper introduces a class of multivariate GARCH models that extends the existing literature by explicitly modeling correlation dependent pricing kernels. A large subclass admits closed-form recursive solutions for the moment generating function under the risk-neutral measure, which permits efficient pricing of multi-asset options. We perform a full calibration to three bivariate series of index returns and their corresponding volatility indexes in a joint maximum likelihood estimation. The results empirically confirm the presence of correlation dependance in addition to the well known variance dependance in the pricing kernel. The model improves both the overall likelihood and the VIX-implied likelihoods, with a better fitting of marginal distributions, e.g., 15% less error on one-asset option prices. The new degree of freedom is also shown to significantly impact the shape of marginal and joint pricing kernels, and leads to up to 53% differences for out-of-the-money two-asset correlation option prices.     

On the role of market insurance in a dynamic model

Durables like cars or houses are a substantial component in the balance sheets of households. These durables are exposed to risk and can be insured in the market. We build a dynamic model in which agents have three possibilities to cope with the risk exposure of the durable stock: (i) purchase of market insurance, (ii) buffer-stock saving of the riskless asset or (iii) adjustment of the durable stock. We calibrate our model to the US economy and find a small role for market insurance.

Transaction costs and asset prices: a dynamic equilibrium model

In this article we study the effects of transaction costs onasset prices. We assume an overlapping generations economy witha riskless, liquid bond, and many risky stocks carrying proportionaltransaction costs. We obtain stock prices and turnover in closedform. Surprisingly, a stock's price may increase in transactioncosts, and a more frequently traded stock may be less adverselyaffected by an increase in transaction costs. Calculations basedon the 'marginal' investor overestimate the effects of transactioncosts. For realistic parameter values, transaction costs havevery small effects on stock prices but large effects on turnover.