International asset allocation with time varying risk: an analysis and implementation

This paper focuses on the impact of economic policy uncertainty on international asset allocation and international capital flows. Our results show that economic policy uncertainty shocks have a negative impact on the international asset allocation, which can be explained from the real economic activity channel and the expectation channel. We also explore a full fledge of country level heterogeneities about the economic policy uncertainty shocks on international asset allocation. Specifically, good institutional quality, transparent information, good information access to the international financial market and bilateral informational link help to alleviate the negative effect that economic policy uncertainty shock does to asset allocation. And a healthy public and external sector also help to alleviate the negative effect. While the importance of government in the economy amplifies the negative effect of economic policy uncertainty shocks to asset allocation. JEL classification numbers: E44, G11, G15. Keywords: Economic policy uncertainty, Global fund allocation, Institutional quality, Global imbalance.

We propose a two-stage stochastic programming method for dynamic international portfolio management in a holistic view aiming at attaining an effective balance between portfolio's currency risk exposure and its realized expected return. By modeling the uncertainty with a scenario tree that reflects the historical distribution as well as related information, and elaborating an integrated optimization framework for asset allocation and currency risk hedging, state-contingent rebalancing decisions including capital allocation, asset selection, and appropriate currency hedging levels can be considered dynamically and automatically. According to extensive empirical analysis, our approach not only provides an effective decision tool for international asset allocation but also pay attention to incremental advantages arising from the jointly realization of currency forward contracts. We also illustrate potential benefits stemming from unrestricted application of currency forward contracts for speculative trading.

Summary The primary purpose of this paper is to propose empirical measures of the structural differences between two communities of plants or animals composed of the same species. Structure is defined to consist of; 1) the species in the community, 2) the pattern of interactions as represented by the covariance or correlation matrix of successive observations on each species, and 3) the mean abundances of each species in each of the two communities. Statistical tests are proposed for testing whether the covariance matrices and the vectors of mean densities for each community are equal and empirical measures of the differences between the covariance matrices and mean vectors are proposed. Given unequal covariance or correlation matrices the factor analysis model is used to derive empirical measures of the degree to which each variable of the ecosystem is responsible for the observed defferences in the pattern of interactions in each community. These tests and measures were applied to data gathered by Hunter (1966) on the abundances of six species of Drosophila censused monthly over a period of approximately two and a half years in two adjacent, but different habitats near Bogota, colombia. The two covariance matrices were significantly different indicating different patterns of interactions in the two Drosophila communities.

Recent advances in shrinkage-based high-dimensional inference

This study examines the risk management practices adopted by stock market investors to minimize financial losses and enhance investment performance. Risk management plays a critical role in stock market investments due to market volatility and economic uncertainties. The study employed a descriptive research design and used both primary and secondary data. Primary data were collected through a structured questionnaire from 124 respondents in Coimbatore City using a simple random sampling technique. Secondary data were sourced from journals, articles, websites, and reports published by the Securities and Exchange Board of India (SEBI). The study evaluates key risk management tools such as diversification, asset allocation, Value-at-Risk (VaR), beta analysis, stop-loss mechanisms, and algorithmic trading models. Frequency and correlation analyses were used to analyze the data. The findings indicate that although investors are generally aware of stock market risks, the relationship between risk awareness and risk identification is positive but statistically insignificant. The study concludes that the adoption of systematic risk management strategies can significantly improve investment decision-making and portfolio stability.

Particle Swarm Optimization (PSO) is an effective tool for solving nonlinear, non-convex optimization problems, offering a quick and efficient way to identify rational asset allocations. In PSO, each particle represents a specific asset allocation and moves within the search space to optimize the defined criteria. Particles update their positions based on personal experience (individual best) and the collective experience of the swarm (global best), gradually converging toward an optimal solution. Research on applying PSO to asset allocation demonstrates that this algorithm not only optimizes expected returns but also minimizes risk in investment portfolios. With its adaptability and computational speed, PSO can become a valuable tool for investors in formulating flexible and effective asset allocation strategies in a volatile financial environment.

Estimating large covariance and precision (inverse covariance) matrices has become increasingly important in high dimensional statistics because of its wide applications. The estimation problem is challenging not only theoretically due to the constraint of its positive definiteness, but also computationally because of the curse of dimensionality. Many types of estimators have been proposed such as thresholding under the sparsity assumption of the target matrix, banding and tapering the sample covariance matrix. However, these estimators are not always guaranteed to be positive-definite, especially, for finite samples, and the sparsity assumption is rather restrictive. We propose a novel two-stage adaptive method based on the Cholesky decomposition of a general covariance matrix. By banding the precision matrix in the first stage and adapting the estimates to the second stage estimation, we develop a computationally efficient and statistically accurate method for estimating high dimensional precision matrices. We demonstrate the finite-sample performance of the proposed method by simulations from autoregressive, moving average, and long-range dependent processes. We illustrate its wide applicability by analyzing financial data such S&P 500 index and IBM stock returns, and electric power consumption of individual households. The theoretical properties of the proposed method are also investigated within a large class of covariance matrices.

The prime focus of this study is to investigate the bilateral trade gap between Bangladesh and South Korea. To find the bilateral trade gap at the outset this paper will investigate total exports and imports between South Korea and Bangladesh and then this study focuses on examining the determinants of total bilateral trade during the period from 1991 to 2020. The study also investigates the strategies through which two countries can improve their bilateral trade relation and can find amicable ways to minimize trade gap. Firstly, the findings reveal that bilateral trade gap increased during the study period of last 30 years. Secondly, the study identifies some determinants namely asymmetry of output, currency risk, comparative advantage and inconsistency of trade friendly environment which led to high bilateral trade gap between the two countries. Finally, this paper suggests some policy recommendations such as hedging of currency risk, increasing Foreign Direct Investment and Research & Development allocations, as well as to create scope for maintaining Gross Domestic Product growth and to increase bilateral cooperation through deliberation amongst high governmental officials and political leaders of the two countries. A model is also developed to forecast the bilateral trade between these two countries in the foreseeable future.

PurposeThe purpose of this paper is to propose predictive models of speculative revaluation attacks, which would facilitate currency risk hedging in emerging and developed countries.Design/methodology/approachThe purpose of this paper is achieved using the methodology of multiple triangulation. Paper combines different theoretical perspectives (three generations of speculative attack models), two sources of data (emerging countries and developed countries) and three methods (logit regression, probit regression and artificial neural networks, ANN) for identification of leading indicators and forecasting of speculative attacks. Combination of multiple observations (data), underlying theories and methods allowed achieving least biased results.FindingsA list of leading indicators of speculative revaluation attacks was generated based on previous researches and three generations of speculative attacks' models. Qualitative and quantitative differences of speculative revaluation attacks in emerging and developed countries were identified. The decision matrix of currency risk hedging in the context of speculative devaluation and revaluation attacks was proposed.Research limitations/implicationsAlthough the sample of this researcher includes a wide range of countries (65 in total), their separation into developed and emerging countries is arbitrary (in the course of 35 years some countries have changed the status from emerging towards developed). The initial list of leading indicators is limited, includes mostly economic variables. It could be improved by encompassing political variables, credit ratings, consumer and business confidence indices.Practical implicationsDeveloped predictive models of speculative revaluation attacks may significantly reduce important element of risk – uncertainty – and, consequently, the cost of financial hedging.Originality/valueThis paper is one of the first public attempts to apply alternative methodology of ANN for forecasting speculative attacks. The results showed that latter method is more accurate than probit and logit regressions. Also, to the author's best knowledge, this is a first public attempt to separately analyse the phenomenon of speculative revaluation attacks.

This paper proposes a new tractable approach to solving asset allocation problems in situations with a large number of risky assets which pose problems for standard approaches. Investor preferences are assumed to be defined over moments of the wealth distribution such as its mean, variance, skew and kurtosis. Time-variations in investment opportunities are represented by a flexible regime switching process. In the context of a four-moment international CAPM specification that relates stock returns in five regions to returns on a global market portfolio, we find evidence of distinct bull and bear states. Ignoring regimes, an unhedged US investor's optimal portfolio is strongly diversified internationally. The presence of regimes in the return distribution leads to a large increase in the investor's optimal holdings of US stocks as does the introduction of skew and kurtosis preferences. Our paper therefore offers an explanation of the strong home bias observed in US investors' asset allocation based on regime switching and skew and kurtosis preferences.

The purpose of this study is to compare two major risk measurement models in financial markets—the Value at Risk (VaR) model and the Conditional Value at Risk (CVaR) model—and to evaluate their effectiveness and limitations in stock market risk management. First, through an in-depth analysis of the theoretical foundations of stock market risk, the paper clarifies the importance of risk for investment decision-making, in particular its impact on asset allocation and risk management strategies. The findings show that VaR, as a traditional risk measurement tool, is simple and convenient and enables a rapid assessment of market risk; however, when confronted with extreme tail events it exhibits certain shortcomings and cannot fully capture the true risk of extreme losses. By contrast, CVaR, by focusing on tail risk, makes it possible to identify potential extreme losses more accurately; especially during periods of sharp market volatility, its early-warning function substantially outperforms VaR. In the empirical part of the article, historical data for the Shanghai A-share index are used. The GARCH-M model is applied to estimate VaR and CVaR, and their values are compared when measuring stock market risk. The results indicate that, at the same confidence level, CVaR is always greater than VaR, which further confirms the advantage of CVaR in reflecting risk losses more accurately, especially tail risk. Overall, this study not only provides a theoretical basis for risk management in financial markets, but also offers recommendations for improving risk measurement methods in the stock market, emphasizing the need to account for tail risk when making investment decisions. The conclusions have important reference value for investors seeking to develop more scientifically grounded risk control strategies in a dynamic market environment.

Upcoming and existing large-scale surveys of galaxies require accurate theoretical predictions of the dark matter clustering statistics for thousands of mock galaxy catalogs. We demonstrate that this goal can be achieve with our new Parallel Particle-Mesh (PM) Nbody code (PPM-GLAM) at a very low computational cost. We run about 15,000 simulations with ~2 billion particles that provide ~1% accuracy of the dark matter power spectra P(k) for wave-numbers up to k~ 1h/Mpc. Using this large data-set we study the power spectrum covariance matrix, the stepping stone for producing mock catalogs. In contrast to many previous analytical and numerical results, we find that the covariance matrix normalised to the power spectrum C(k,k')/P(k)P(k') has a complex structure of non-diagonal components. It has an upturn at small k, followed by a minimum at k=0.1-0.2h/Mpc. It also has a maximum at k=0.5-0.6h/Mpc. The normalised covariance matrix strongly evolves with redshift: C(k,k')~delta(t)^alpha P(k)P(k'), where delta is the linear growth factor and alpha ~ 1-1.25, which indicates that the covariance matrix depends on cosmological parameters. We also show that waves longer than 1Gpc have very little impact on the power spectrum and covariance matrix. This significantly reduces the computational costs and complexity of theoretical predictions: relatively small volume ~ (1Gpc)^3 simulations capture the necessary properties of dark matter clustering statistics. All the power spectra obtained from many thousands of our simulations are publicly available.

Estimation of the high-dimensional banded covariance matrix is widely used in multivariate statistical analysis. To ensure the validity of estimation, we aim to test the hypothesis that the covariance matrix is banded with a certain bandwidth under the high-dimensional framework. Though several testing methods have been proposed in the literature, the existing tests are only powerful for some alternatives with certain sparsity levels, whereas they may not be powerful for alternatives with other sparsity structures. The goal of this paper is to propose a new test for the bandedness of high-dimensional covariance matrix, which is powerful for alternatives with various sparsity levels. The proposed new test also be used for testing the banded structure of covariance matrices of error vectors in high-dimensional factor models. Based on these statistics, a consistent bandwidth estimator is also introduced for a banded high dimensional covariance matrix. Extensive simulation studies and an application to a prostate cancer dataset from protein mass spectroscopy are conducted for evaluating the effectiveness of the proposed adaptive tests blue and bandwidth estimator for the banded covariance matrix.

High dimensional covariance matrices have attracted much attention of statisticians and econometricians during the past decades. Vast literature is devoted to the research in high dimensional covariance matrices. However, most of them are for constant covariance matrices. In many applications, constant covariance matrices are not appropriate, e.g. in portfolio allocation, dynamic covariance matrices would make much more sense. Simply assuming each entry of a covariance matrix is a function of time to introduce a dynamic structure would not work. In this paper, we are going to introduce a class of high dimensional dynamic covariance matrices in which a kind of additive structure is embedded. We will show the proposed high dimensional dynamic covariance matrices have many advantages in applications. An estimation procedure is also proposed to estimate the proposed high dimensional dynamic covariance matrices. Asymptotic properties are built to justify the proposed estimation procedure. Intensive simulation studies show the proposed estimation procedure works very well when sample size is finite. Finally, we apply the proposed high dimensional dynamic covariance matrices, together with the proposed estimation procedure, to portfolio allocation. The results look very interesting.

Optimal Asset Location and Allocation with Taxable and Tax-Deferred Investing