Volume 26, Issue 2

Blockchain is a new technology slowly integrating our economy with crytocurrencies such as Bitcoin and many more applications. Bitcoin and other version of it (known as Altcoins) are traded everyday at various cryptocurrency exchanges and have drawn the interest of many investors. These new type of assets are characterised by wild swings in prices and this can lead to great profit as well as large losses. To respond to these dynamics, crypto investors need adequate tools to guide them through their choice of optimal portfolio selection. This paper presents a portfolio selection based on COGARCH and regular vine copula which are able to capture features such as abrupt jumps in prices, heavy-tailed distribution and dependence structure respectively, with the optimal portfolio achieved through the stochastic heuristic algorithm differential evolution known for its global search solution ability. This method shows great performance as compared with other available models and can achieve up to 50% of total returns in some periods of optimization.

Previous studies suggested that the power of unit root and stationarity tests can be improved by augmenting a testing regression model with stationary covariates. However, one practical problem arises since such procedures require finding the variables that satisfy certain conditions. The difficulty of finding satisfactory covariate has hindered using such desired tests. In this paper, we suggest using non-normal errors to construct stationary covariates in testing for stationarity. We do not need to look for outside variables, but we utilize the distributional information embodied in a time series of interest. The terms driven from the information on non-normal errors can be employed as valid stationary covariates. For this, we adopt the framework of stationarity tests of Jansson (Jansson, M. 2004. “Stationarity Testing with Covariates.” Econometric Theory 20: 56–94). We show that the tests can achieve much-improved power. We then present the response surface function estimates to facilitate computing the critical values and the corresponding p -values. We investigate the nature of shocks to the US macro-economic series using the updated Nelson–Plosser data set through our new testing procedure. We find stronger evidence of non-stationarity than their univariate counterparts that do not use the covariates.

The Federal Reserve responded to the great financial crisis deploying new monetary policy tools, the most notable of which being the expansion of its balance sheet. In a recent paper, Weale, M., and T. Wieladek. 2016. “What Are the Macroeconomic Effects of Asset Purchases?” Journal of Monetary Economics 79 (C): 81–93 show that the asset purchases were effective in stimulating economic activity as well as inflation and asset prices. Here I show that their results are state dependent: large scale asset purchase are effective only when financial markets are impaired. Financial markets are under stress when the effective risk-bearing capacity of the financial sector is drastically reduced, i.e. when the excess bond premium (EBP) of Gilchrist, S., and E. Zakrajšek. 2012. “Credit Spreads and Business Cycle Fluctuations.” The American Economic Review 102 (4): 1692–72 exceed a certain threshold. Using an estimated threshold vector autoregressive model conditional on the EBP regime, I show that an increase in the balance sheet has expansionary effects on GDP and inflation when EBP is high, but not when it is low (as its effects become mostly insignificant). I argue that the high EBP can be interpreted as a proxy of market dis-functioning so that only when this channel of transmission is on, the unconventional policy is particularly effective. This suggests that models of transmission of unconventional policies, based on asset purchases, should focus also on the market functioning channel and not only on the portfolio balance one.

In this paper, we propose a new approach to model structural change in cointegrating regressions using penalized regression techniques. First, we consider a setting with known breakpoint candidates and show that a modified adaptive lasso estimator can consistently estimate structural breaks in the intercept and slope coefficient of a cointegrating regression. Second, we extend our approach to a diverging number of breakpoint candidates and provide simulation evidence that timing and magnitude of structural breaks are consistently estimated. Third, we use the adaptive lasso estimation to design new tests for cointegration in the presence of multiple structural breaks, derive the asymptotic distribution of our test statistics and show that the proposed tests have power against the null of no cointegration. Finally, we use our new methodology to study the effects of structural breaks on the long-run PPP relationship.

This paper extends the threshold cointegration model developed by Gonzalo, J., and J. Y. Pitarakis. 2006. “Threshold Effects in Cointegrating Relationships.” Oxford Bulletin of Economics & Statistics 68: 813–33 and Chen, H. 2015. “Robust Estimation and Inference for Threshold Models with Integrated Regressors.” Econometric Theory 31 (4): 778–810 to allow for a time-varying threshold, which is a function of candidate variables that affect the separation of regimes. We derive the asymptotic distribution of the proposed least-square estimator of the threshold, and study the convergence rate of the threshold estimator. We also suggest test statistics for threshold effect and threshold constancy. Monte Carlo simulations point out that the convergence rate of the threshold estimator is consistent with the asymptotic theory, and the proposed tests have good size and power properties. The empirical usefulness of the proposed model is illustrated by an application to the US data to investigate the Fisher hypothesis.

In this paper, a new generator function is proposed and based on this function a new Archimedean copula is introduced. The new Archimedean copula along with three representatives of Archimedean copula family which are Clayton, Gumbel and Frank copulas are considered as models for the dependence structure between the returns of two stocks. These copula models are used to simulate daily log-returns based on Monte Carlo (MC) method for calculating value at risk (VaR) of the financial portfolio which consists of two market indices, Ford and General Motor Company. The results are compared with the traditional MC simulation method with the bivariate normal assumption as a model of the returns. Based on the backtesting results, describing the dependence structure between the returns by the proposed Archimedean copula provides more reliable results over the considered models in calculating VaR of the studied portfolio.

In a homogenous product market, customers’ different demand elasticities may lead to different prices. This study examined price discrimination’s effect on equilibrium points in Cournot duopoly games by assuming that each firm charges K prices and adjusts its strategies based on bounded rationality. In consideration of price discrimination, two discrete dynamic game systems with 2 K variables were introduced for players with homogenous or heterogenous expectations. The stability of the Nash equilibrium point was found to be independent of price discrimination. Given price discrimination, the stability of boundary stationary points for the system with homogenous players is different from that for the system with heterogenous players. Numerical simulations verified the critical point for the system with homogenous players from being stable to its bifurcation.