The sustainability of Malaysia's defined contribution pension system : implementation of deterministic linear programming

This article investigates, under deterministic linear programming model, asset allocation decision and optimal investment strategy for Malaysia’s defined contribution pension (DC) scheme -Employees Provident Fund (EPF). The model requires generation of scenarios and probabilities to represent future assets and liabilities streams. We employed Vector Autoregressive (VAR) model to generate future returns of five asset classes i.e. equity, money market instrument, Malaysia government bond with 1 and 10 years of maturity date and property. Future liabilities factors were derived from two sub-models; population and salary. In population model, the future status of the EPF members was determined using a Markov Chain model. Then, the random factors of assets and liabilities were used in the asset liability model (ALM) based on linear programming (LP) and fixed mix (FM) strategy. The results of the research are grouped in two levels. First, we briefly discuss the finding of the random factor model and then we analyse the optimal investment strategy for the EPF. In terms of finding an optimal investment strategy, the FM strategy generated higher expected terminal wealth than the LP strategy. This finding suggests that FM strategy is superior to the LP strategy. In addition, we find that the higher dividend distributed to the members may result in decreasing of the expected terminal wealth of the fund for both strategies. This portrays that dividend distribution policy may affect the financial soundness of the EPF in the long run

Modelling systemically important banks vis-à-vis the Basel Prudential Guidelines

Our paper investigates Indonesia’s systemically important banks (SIBs) using theoretical approaches—CoVaR, marginal expected shortfall (MES), and SRISK—to compare with the Basel guidelines as benchmark. We use Indonesian banks’ market and supervisory data over the 2008–2019 period. The research aims to seek intertheoretical model interaction and SIB ranking in concordance with the Basel guidelines as applied by a bank supervisor. The findings show that SRISK produced a more consistent ranking compared with CoVaR and MES. CoVaR and MES had higher intermodel correlation converted to 59% similarity in rankings. Further, all theoretical models are in line with the Basel guidelines, where the closest approximation is at 47%. The results indicate that policy makers could use scholarly models as validation tools and help improve supervision decision to identify systemically important institutions

Macroeconomics of systemic risk : transmission channels and technical integration

The avenue to find a balanced assessment of systemic financial institutions needs the integration of macro and micro granular datasets. This paper investigates how macroeconomic shocks affect systemic risk through several transmission channels. Employing Indonesia datasets over 2008–2019, we regressed three market models: CoVaR, MES, and SRISK using fixed effect, random effect, GARCH(1,1), and finite mixture models. The findings show that stock beta, market index, and exchange rate volatility amplify the systemic risk while the liquidity spread outcome varies due to different of model variables and the deepness of the country’s financial market. We propose a practical systemic risk assessment framework and samples of technical integration to capture the overall risk endogenously and externally expose the systemically important financial institutions

A secular increase in the equity risk premium

There is an increasing consensus that global ‘excess saving’ has contributed to a reduction in equilibrium real interest rates. While economists dispute the extent of the decline, few now question that a decline has taken place or that excess saving has played a causal role. A key implication of this narrative is a decline in yields of all assets, including but not restricted to government bond yields. Yet, since the turn of the century, yields on global equity have risen. A complementary explanation is that there has been an increase in the global equity risk premium (ERP), which has simultaneously pushed risk-free yield curves lower and equity yields higher. Applying a sign restrictions approach, I find that excess savings shocks were the predominant force affecting global real bond yields between the mid-1980s and 2000 but that ‘risk premium’ shocks have accounted for more of the decline in real bond yields since 2000

Financial Volatility and Real Economic Activity

The issue of financial volatility, especially since financial deregulation, has given rise to concerns regarding the effects of increased financial volatility on real economic activity. Two issues represent a substantial challenge to financial economists with respect to these concerns. The first relates to the identification of the causes of increased volatility in financial markets. Identification is a first step towards increasing both financial economists' and policy-makers' understanding of the interrelated causes of financial volatility. The second requires linking the effects of increased financial volatility to the real sector of the economy by examining the channels through which financial volatility influences fundamental economic variables. In order to address these two issues, the analysis initially develops and estimates a model which is capable of explaining the financial and business cycle determinates of movements in the conditional volatility of the Australian All Industrials stock market index. Evidence suggests that a significant linkage exists between the conditional volatility of the money supply. Models are then developed to examine how monetary volatility is transmitted to the volatility of financial asset prices, inflation and real output in an open economy. The results indicate that while financial volatility has increased to some extent since the late 1980s, this has been transferred non-uniformly towards increasing volatility of both real and financial activity

Efficiently Inefficient: How Smart Money Invests and Market Prices Are Determined, by Lasse Heje Pedersen

Book review: This book provides an academic treatment of investments in which we learn about trading strategies used by sophisticated investors in interviews with George Soros, who ‘broke the Bank of England’, and short seller Jim Chanos, who explains how he bets against companies with flawed business plans and fraudulent managers and how he uncovered Enron before its collapse. The book shows how financial markets operate and how securities are priced in an efficiently inefficient way

Financial Volatility and Real Economic Activity

Published in 1999. The issue of financial volatility, especially since financial deregulation, has given rise to concerns regarding the effects of increased financial volatility on real economic activity. Two issues represent a substantial challenge to financial economists with respect to these concerns. The first relates to the identification of the causes of increased volatility in financial markets. Identification is a first step towards increasing both financial economists' and policy-makers' understanding of the interrelated causes of financial volatility. The second requires linking the effects of increased financial volatility to the real sector of the economy by examining the channels through which financial volatility influences fundamental economic variables. In order to address these two issues, the analysis initially develops and estimates a model which is capable of explaining the financial and business cycle determinates of movements in the conditional volatility of the Australian All Industrials stock market index. Evidence suggests that a significant linkage exists between the conditional volatility of the money supply. Models are then developed to examine how monetary volatility is transmitted to the volatility of financial asset prices, inflation and real output in an open economy. The results indicate that while financial volatility has increased to some extent since the late 1980s, this has been transferred non-uniformly towards increasing volatility of both real and financial activity

The Economic Impacts (Multiplier Approach) of the Readymix Regional Distribution Centre at Rooty Hill, Blacktown

The purpose of this economic impact analysis (EIA) is to provide an economic assessment of both the direct and indirect effects relating to the proposed RDC on the local and regional economy

An overview of the determinants of financial volatility : an explanation of measuring techniques

The majority of asset pricing theories relate expected returns on assets to their conditional variances and covariance’s. Since conditional variances and covariance’s are not observable, researchers have to estimate conditional second moments relying on models. An important concern is the accuracy of these models and how researchers may estimate them more accurately. In this paper, various measures of volatility have been examined ranging from time invariant to time variant measures. In the former case one of the simplest measures examined was the standard deviation. A weakness of this measure is the assumption that volatility is constant, this being due to the standard deviation of returns increasing with the square root of the length of the period. Empirical evidence, however, shows us that the behavior of asset returns in the real world changes randomly over time. This led us to an examination of time variant models for measuring volatility

Financial volatility : issues and measuring techniques

This paper explains in non-technical terms various techniques used to measure volatility ranging from time invariant measures to time variant measures. It is shown that a weakness of the former measures arises from the underlying assumption that volatility is considered to be constant over time. This observation has led researchers to develop time variant measures based on the assumption that volatility changes over time. The introduction of the original ARCH model by Engle has spawned an ever increasing variety of models such as GARCH, EGARCH, NARCH, ARCH-M MARCH and the Taylor-Schwert model. The degree of sophistication employed in developing these models is discussed in detail as are the models characteristics used to capture the underlying economic and financial time series data including volatility clustering, leverage effects and the persistence of volatility itself. A feature of these more elaborate models is that they generally obtain a better fit to the data in-sample