Individualisation of a Fuzzy System for Asset Allocation

Multi-period stochastic optimization models for dynamic asset allocation

Chapter 13 - Stochastic programming models for strategic and tactical asset allocation—A study from Norwegian life insurance

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.

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

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.

Mitigating Estimation Risk in Asset Allocation: Diagonal Models Versus 1/N Diversification

Unemployment Risks and Private Unemployment Insurance

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.

Against the backdrop of the gradual deepening of interest rate liberalization, the decline in effective credit demand, the intensification of competitive involution among commercial banks, and the complex international economic and trade situation, commercial banks in mainland China have entered a stage of low interest rates and narrow interest margins. Coupled with the continuous exposure of risks in retail customer groups and small and micro enterprises, many commercial banks have chosen to phase in expanding and strengthening their corporate business segments to smoothly navigate economic cycles and enhance operational resilience and sustainability. How the corporate business segment optimizes its asset-liability structure through asset allocation to achieve high-quality development is a major issue worthy of consideration by the entire industry. From the perspective of a medium-sized national commercial bank, this paper explores and proposes four key basic customer groups, six asset allocation models, and fourteen key industries for layout, for reference, and research.

PurposeThe paper is aimed at modelling time varying betas via a state space representation in order to decompose the marginal contribution to risk of downside and upside deviations of asset returns in portfolio optimisation.Design/methodology/approachThe approach enables to take into account the relationship between risk and excess returns in up‐side and down‐side markets and to arrange a flexible asset allocation model which directly incorporates the investor risk tolerance to positive or negative expected market moves. The model volatility through state space models and the Kalman filter, widely used to recursively and optimally estimate time varying betas.FindingsThe study shows that the application of an asset allocation model which splits beta in two parts, one related to Bear and the other to Bull markets, and reconciles them with a non negative risk aversion parameter may produce interesting financial results if compared with typical passive portfolios. The proposed model was tested by conducting extensive empirical evaluations on a set securities belonging to eight different markets. The outcomes show that active strategies can be developed and can lead to better performances.Research implicationsThe research affects optimisation models in particular considering the volatility indicators usually estimated not only by researchers but also by practitioners.Originality/valueIn financial literature we find empirical evidence that the constant beta model may be inaccurate and hazardous to use in asset allocation decisions and many statistical techniques have been developed to estimate time dependent betas. Rolling regression procedures allow to capture beta dynamics but require the definition of the estimation period. The paper provides an empirical analysis referred both to European and American market data which let us to allocate assets avoiding the usual limits of standard volatility indicators.

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.

WITHDRAWN: Different simplified equations for FPGA-based cloud system development and mathematical models

A model of loan asset allocation and funding decisions is developed and applied to a regional Farm Credit Bank (FCB) using time-series data from 1970 to 1990. Estimated loan rate equations indicate that expected loan prepayment and default are significant sources of risk for which the FCB charged interest rate premiums. Theoretically, and practically, these rate premiums impart a positive slope to the bank's loan supply schedule. Loan supply elasticities and interest rate premiums are estimated. Long-term loan volume is responsive to changing loan interest rates, while short-term loan volume is not.

We develop an approximate solution method for the optimal consumption and portfolio choice problem of an infinitely long-lived investor with Epstein–Zin utility who faces a set of asset returns described by a vector autoregression in returns and state variables. Empirical estimates in long-run annual and post-war quarterly U.S. data suggest that the predictability of stock returns greatly increases the optimal demand for stocks. The role of nominal bonds in long-term portfolios depends on the importance of real interest rate risk relative to other sources of risk. Long-term inflation-indexed bonds greatly increase the utility of conservative investors.

This paper discusses optimal dynamic investment policies for investors, who make the investment decisions in each of the asset categories over time. We construct the framework integrating stochastic optimization and Monte Carlo simulation for dynamic asset allocation, and we propose the linear programming models using simulated paths to solve a large-scale problem in practice. Linear programming models can be formulated to adopt either a fixed-value rule or a fixed-amount rule instead of the general fixed-proportion rule. These formulations can be simply implemented and solved very fast. Some numerical examples are tested to illustrate the characteristics of the models. These models can be used to improve the trade-off between risk and expected wealth, and we can get interesting results for the dynamic asset allocation policies.