A survey on risk-return analysis

This paper provides a review of the main features of asset pricing models. The review includes single-factor and multifactor models, extended forms of the Capital Asset Pricing Model with higher order co- moments, and asset pricing models conditional on time-varying volatility.Asset pricing, CAPM

Beta Risk and Regime Shift in Market Volatility

In this paper, we relate the returns in the thirty securities in the Dow Jones index to regime shifts in stock market volatility. We apply a Markov switching process of order one to market volatility and examine the variation in the securities' returns in different volatility regimes. We test the significance of the risk premium in different market regimes and we find evidence of relationship between market volatility and securities beta risk.Markov regime-switching, market volatility, beta risk

Wavelet timescales and conditional relationship between higher-order systematic co-moments and portfolio returns: evidence in Australian data

This paper investigates association between portfolio returns and higher-order systematic co-moments at different timescales obtained through wavelet multi-scaling- a technique that decomposes a given return series into different timescales enabling investigation at different return intervals. For some portfolios, the relative risk positions indicated by systematic co-moments at higher timescales is different from those revealed in raw returns. A strong positive (negative) linear association between beta and co-kurtosis and portfolio return in the up (down) market is observed in raw returns and at different timescales. The beta risk is priced in the up and down markets and the co-kurtosis is not. Co-skewness does not appear to be linearly associated with portfolio returns even after the up and down market split and is not priced.Wavelet multi-scaling, higher-order systematic co-moments, asset pricing

Performance of Indian commercial banks (1995-2002): an application of data envelopment analysis and Malmquist productivity index

This paper investigates efficiency using data envelopment analysis (DEA) and productivity growth using Malmquist index in a sample of Indian commercial banks over the period 1995-2002. Using total deposits and operating expenses as input and loans and other earning assets as output in the DEA analysis we observe no significant growth in productivity during the sampled period. The rate of increase in technical efficiency though small is likely to be due to scale efficiency compared to managerial efficiency. In general, smaller banks are less efficient and highly DEA-efficient banks have a high equity to assets and high return to average equity ratios. There has been no growth in productivity in private sector banks where as the public sector banks appears to demonstrate a modest positive change through 1995-2002. Technological change in the public sector banks reveals a growth while the private sector banks experienced a negative growth of almost the same magnitude.Indian banks, productivity change, DEA, Malmquist index

Association between Markov regime-switching market volatility and beta risk: Evidence from Dow Jones industrial securities

In this paper, the volatility of the return generating process of the market portfolio and the slope coefficient of the market model is assumed to follow a Markov switching process of order one. The results indicate very strong evidence of volatility switching behaviour in a sample of returns in the S&P500 index. In three of the thirty securities in the Dow Jones index, the estimated slope in the market model show strong switching behaviour. In these three securities the low risk state is more persistent than the high-risk state. For each security we estimate the conditional probabilities that the security is in the high (low) risk state given the market is in the high (low) volatility regime and show that this information can be used to classify securities into three distinct groups. There is no association between these groups and the securities' constant beta estimated in the market model and the Sharpe index. Some directions for further research are discussed.Asset pricing, Markov regime-switching, market volatility, beta risk

Modelling the Risk and Return Relation Conditional on Market Volatility and Market Conditions

This paper investigates whether the risk-return relation varies, depending on changing market volatility and up/down market conditions. Three market regimes based on the level of conditional volatility of market returns are specified - 'low', 'neutral' and 'high'. The market model is extended to allow for these three market regimes and a three-beta asset-pricing model is developed. For a set of US industry sector indices using a cross-sectional regression, we find that the beta risk premium in the three market volatility regimes is priced. These significant results are uncovered only in the pricing model that accommodates up/down market conditions.CAPM, conditional market volatility, modelling conditional betas

Association between Markov regime-switching market volatility and beta risk: Evidence from Dow Jones industrial securities.

In this paper, the volatility of the return generating process of the market portfolio and the slope coefficient of the market model is assumed to follow a Markov switching process of order one. The results indicate very strong evidence of volatility switching behaviour in a sample of returns in the S&P500 index. In three of the thirty securities in the Dow Jones index, the estimated slope in the market model show strong switching behaviour. In these three securities the low risk state is more persistent than the high-risk state. For each security we estimate the conditional probabilities that the security is in the high (low) risk state given the market is in the high (low) volatility regime and show that this information can be used to classify securities into three distinct groups. There is no association between these groups and the securities' constant beta estimated in the market model and the Sharpe index. Some directions for further research are discussed.Asset pricing, Markov regime-switching, market volatility, beta risk

Beta Risk and Regime Shift in Market Volatility

In this paper, we relate security returns in the thirty securities in the Dow Jones index to regime shifts in the market portfolio (S&P500) volatility. We model market volatility as a multiple-state Markov switching process of order one and estimate non-diversifiable security risk (beta) in the different market volatility regimes. We test the significance of the premium of the beta risk associated with the different market regimes and find evidence of a relationship between security return and beta risk when conditional on the up and down market movement.Markov regime-switching, Market volatility, Beta risk.

Is systematic downside beta risk really priced? Evidence in emerging market data

Several studies advocating safety first as a major concern to investors propose downside beta risk as an alternative to the traditional systematic risk-beta. Downside measures are concerned with a subset of the data and therefore the results in the studies that consider the downside beta only may be biased. This study addresses this issue by including downside co-skewness risk in addition to the downside beta risk in the pricing model. In a sample of 27 emerging markets two-stage rolling regression analysis fails to support pricing models with downside risk measures. In a cross-sectional analysis inclusion of downside co-skewness improves model fit. When considered together, downside beta is potential and downside co-skewness is a risk to the rational investor. Even though our results are inconclusive the evidence strongly suggests a need for further investigation of co-skewness risk in pricing models that adopt a downside risk framework.Beta, Downside risk, Emerging markets

Wavelet timescales and conditional relationship between higher- order systematic co-moments and portfolio returns: evidence in Australian data

This paper investigates association between portfolio returns and higher-order systematic co-moments at different timescales obtained through wavelet multi-scaling- a technique that decomposes a given return series into different timescales enabling investigation at different return intervals. For some portfolios, the relative risk positions indicated by systematic co-moments at higher timescales is different from those revealed in raw returns. A strong positive (negative) linear association between beta and co-kurtosis and portfolio return in the up (down) market is observed in raw returns and at different timescales. The beta risk is priced in the up and down markets and the co-kurtosis is not. Co-skewness does not appear to be linearly associated with portfolio returns even after the up and down market split and is not priced.Wavelet multi-scaling, higher-order systematic co-moments, asset pricing