Portfolio optimisation under changing risk via time‐varying beta

Since the dawn of the 21st century, major global economies have maintained prolonged periods of low interest rates to stimulate economic growth, establishing a stable low-interest-rate macroenvironment. However, in recent years, driven by factors such as elevated inflation and tightening monetary policies, the global interest rate landscape has embarked upon a structural transition from prolonged low rates towards higher rates. This shift has profoundly impacted the pricing logic of financial markets and investors' asset allocation frameworks. Traditional allocation strategies now face the risk of becoming obsolete, creating an urgent need for systematic research to provide theoretical and practical support. Focusing on the impact of evolving interest rate environments on investors' asset allocation decisions, this analysis examines asset performance across different interest rate cycles, with a specific emphasis on transition periods. By examining representative cases of high and low interest rates, it traces the historical performance of various asset classes during distinct interest rate phases and presents data analysis. Research findings indicate that bonds and growth equities demonstrate superior performance during low-interest-rate periods, while value equities, commodities, and cash-like assets yield more stable returns during high-interest-rate phases. Key signals identifying interest rate transition periodssuch as unexpected inflation surges and central bank policy shiftsare identified. Risk management strategies proposed include diversified allocation, dynamic duration adjustment, and increased allocation to inflation-hedging assets, offering practical guidance for investors to navigate interest rate cycles and optimise asset allocation.

We propose a novel asset allocation model using a Markov process of states defined by clustered efficient frontier coefficients. While most research in Markov models of the market characterize regimes using return and volatility, we instead propose characterizing these states using efficient frontiers, which provide more information on the interactions of underlying assets that comprise the market. Efficient frontiers can be decomposed to their functional form, a square-root second-order polynomial defined by three coefficients, to provide a dimensionality reduction of the return vector and covariance matrix. Each month, the proposed model hierarchically clusters the monthly coefficients data up to the current month, to characterize the market states, then defines a Markov process on the sequence of states. To incorporate these states into portfolio optimization, for each state, we calculate the tangency portfolio using only return data in that state. We then take the expectation of these weights for each state, weighted by the probability of transitioning from the current state to each state. To empirically validate our proposed model, we employ three sets of assets that span the market, and show that our proposed model significantly outperforms benchmark portfolios.

Every person or family will face a series of important expenses. In order to avoid shocks from these expenses for individuals or families, people will probably have precautionary saving incentives which influence their strategic asset allocation. This paper reviews domestic and foreign literatures about asset allocation and precautionary saving. This paper proposes a new asset allocation model framework including both social insurance and precautionary saving. At the same time this paper gets two-period model from previous model framework and obtains analytical solution. The main innovations of the paper are combining social insurance and precautionary saving with asset allocation model, and explaining the significance of analytical solution.?n

Various analytical techniques are available for modeling multivariate time series data. One such approach is the State Space Model, which can be employed to model this type of data. In this study, the data to be analyzed are data on the Indonesia Rupiah (IDR) exchange rate (ExR) against the US Dollar (USD), oil and gas exports (OGE), money supply (MS) and non-oil and gas exports (non-OGE) from January 2008 to December 2019. The aim of this study is to identify the most suitable state space model for the given data. In this research, the state space method will be applied to multivariate time series data, with the state space represented in the Vector Autoregressive (VAR) model to explore the interrelationships among groups of observed variables. The VAR model is a statistical technique used to analyze the relationships between variables in the dataset, employing the Granger Causality Test. The state space model is utilized to model and forecast multiple interconnected time series, where the variables exhibit dynamic interactions and to examine additional unobserved variables in the time series data. Based on the analysis results and the minimum value of the Akaike Information Criterion (AIC), the optimal VAR model identified is the VAR (6) model. The results of forecasting values using the state space model show that the predicted values and the real values for the state space model are very closed to each other.

Asset allocation models have evolved in complexity with the development of modern portfolio theory, but they continue to operate under the assumption of investor rationality and other assumptions that do not hold in the real world. For this reason, academics and industry professionals make efforts to understand the behavioral biases of decision makers and the implications these biases have on asset allocation strategies. This chapter reviews the building blocks of asset allocation, involving stocks, bonds, real estate, and cash. It also examines the history and theory behind two of the most popular portfolio management strategies: mean-variance optimization and the Black-Litterman Model. Finally, the chapter examines five common behavioral biases that have direct implications for asset allocation: familiarity, status quo, framing, mental accounting, and overconfidence. Each behavioral bias discussion contains examples, warning signs, and steps to correct the emotional or cognitive errors in decision making.

Unemployment Risks and Private Unemployment Insurance

The state space form is an enormously powerful tool which opens the way to handling a wide range of time series models. Once a model has been put in state space form, the Kalman filter may be applied and this in turn leads to algorithms for prediction and smoothing. The state space form is described in the first section of this chapter, while the second section develops the Kalman filter. Prediction and smoothing are described in sections 3.5 and 3.6 respectively. The Kalman filter also opens the way to the maximum likelihood estimation of the unknown parameters in a model. This is done via the prediction error decomposition and a full account can be found in section 3.4. The present chapter can be read independently of the rest of the book, and taken as a guide to the uses of the state space models in areas outside engineering. On the other hand, those interested primarily in the practical aspects of structural time series modelling will be reassured to know that they do not have to master all the technical details of the Kalman filter set out here. The most important parts of the chapter with which to become familiar are sections 3.1 and 3.5, the earlier parts of sections 3.2, 3.4 and 3.6, and, for those interested in non-linear models, sub-section 3.7.1. The reader will also benefit by at least skimming through the remaining sections, since there is some reference back to the various algorithms in later chapters and it is useful to have some idea of what these algorithms do and how they fit into the overall picture.

This paper sets out a basic framework for solving a stochastic portfolio problem using dynamic programming (DP). Dynamic portfolio decisions are concerned with simultaneous decisions on savings and asset allocation whereby asset returns, such as on equity and bonds, are stochastic as in Campbell and Viceira (Strategic asset allocation, portfolio choice for long-term investors, 2002). In contrast to CV (2002) we do not use a local approximation method to solve the stochastic model but rather use a global solution procedure such as DP. Whereas CV (2002) solve their model by assuming a constant consumption-wealth ratio and equity premium, we can allow both to be time varying. Different variances of equity and bond returns are explored in their impact on saving and asset allocation decisions and on the value function. The stochastic dynamic portfolio decision method proposed here allows for online decisions as data on asset returns are available in real time. The method is set up in a way such that it also helps to make fund decisions online for various types of investment opportunities.

Multi-period stochastic optimization models for dynamic asset allocation

The shift from defined benefit to defined contribution plans in the United States has drawn new attention to the effect of participants' asset allocation decisions on their financial resources for retirement. This paper develops a stochastic simulation algorithm to evaluate the effect of holding a broadly diversified portfolio of common stocks, or a portfolio of index bonds, on the distribution of 401(k) account balances at retirement. We compare the alternative distributions of retirement wealth both by showing the empirical distribution of potential wealth values, and by computing the expected utility of these outcomes under standard assumptions about the structure of household preferences. Our analysis highlights the critical role of other sources of wealth, such as Social Security, defined benefit pension annuities, and saving outside retirement plans in determining the expected utility cost of holding equities in the retirement account. Our findings also demonstrate the importance of the equity premium in affecting investors' utility from different retirement asset allocations. Viewed from the beginning of a working career, and given the historical pattern of returns on stocks and bonds, a household that does not have extremely high risk aversion would achieve a higher expected utility by holding a portfolio of stocks rather than bonds.

Frequency mismatch has been a problem in time series econometrics. Many monthly economic and financial indicators are normally aggregated to match quarterly macroeconomic series such as Gross Domestic Product when performing econometric analysis. However, temporal aggregation, although widely accepted, is prone to information loss. To address this issue, mixed frequency modelling is employed by using state space models with time-varying parameters. Quarter-on-quarter growth rate of GDP estimates are treated as monthly series with missing observation. Using Kalman filter algorithm, state space models are estimated with eleven monthly economic indicators as explanatory variables. A one-step-ahead forecast for GDP growth rates is generated and as more indicators are included in the model, the predicted values became closer to the actual data. Further evaluation revealed that among the group competing models, using Consumer Price Index (CPI), growth rates of Philippine Stock Exchange Index (PSEi), Exchange Rate, Real Money Supply, Wholesale Price Index (WPI) and Merchandise Exports are the more important determinants of GDP growth and generated the most desirable forecasts (lower forecast errors).

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

We develop a preference-based model for dynamic asset allocation in an international diversified portfolio, subjecting to a home country event, using stochastic information filtering The model is formulated by maximizing the expected utility of the investor's terminal wealth, which is expressed as a function of the ratio of standard deviation on foreign asset returns to the standard deviation on home asset returns. The model test results illustrate that home bias holds in the absence of home event risk. It has also revealed that when there is a home event risk, the ratio of standard deviations on both asset returns and the percentage jump size exhibit a deterministic asset weights pattern. In addition, we observed that when a severe political issue or an economic event occurs, the home investors decrease the proportion of home asset while increase the proportion of foreign asset. This foreign bias trend arises when the home event risk information is imperfectly spilled over to foreign assets.

Conditional asset allocation (CAA) involves using key past economic and financial data to produce forecasts of expected returns for the various asset classes involved in the asset allocation decision. Traditional forecasting models for asset returns, in particular linear regression models and ARIMA time series based models, often provide economically meaningful asset allocation decisions. These results, however, only seem to possess power in-sample, and the out-of-sample performance of such methodologies suggests, consistent with efficient market theory, that there is no economic benefit from undertaking conditional asset allocation.

The paper presents a broad general review of the state space approach to time series analysis. It begins with an introduction to the linear Gaussian state space model. Applications to problems in practical time series analysis are considered. The state space approach is briefly compared with the Box–Jenkins approach. The Kalman filter and smoother and the simulation smoother are described. Missing observations, forecasting and initialisation are considered. A representation of a multivariate series as a univariate series is displayed. The construction and maximisation of the likelihood function are discussed. An application to real data is presented. The treatment is extended to non-Gaussian and nonlinear state space models. A simulation technique based on importance sampling is described for analysing these models. The use of antithetic variables in the simulation is considered. Bayesian analysis of the models is developed based on an extension of the importance sampling technique. Classical and Bayesian methods are applied to a real time series.