Thesis (M.Com. (Finance))--University of the Witwatersrand, Faculty of Commerce, Law and Management, School of Economic and Business Sciences, 2017 / This dissertation investigates the inclusion of volatility into the asset allocation decision, first as an asset class, and second as a tool for dynamic equity allocation. An examination on whether volatility exposure as an asset class has the necessary characteristics to form part of the broader investment universe is conducted. This is accomplished by comparing the risk-return characteristics of three naked option-selling strategies, a bull put spread strategy and a VIX futures strategy with the S&P 500 Index. Each volatility strategy is also included as part of a 30/30/40 volatility/equity/bond portfolio and compared to a traditional 60/40 equity/bond portfolio. Historically, the results indicate that all individual volatility strategies generated superior Sharpe ratios and exhibited less severe drawdowns than the S&P 500 Index, particularly during the 2008 Global Financial Crisis. Additionally, all volatility blended portfolios experienced better tail-risk profiles than the 60/40 equity/bond portfolio, with the naked option-selling strategies also generating similar returns as the 60/40 portfolio both over the full sample period as well during the period of recovery following the 2008 Global Financial Crisis. The results suggest that the returns associated with option-selling strategies are consistent, and have resulted in strong long-run risk-adjusted performance, qualifying short volatility exposure attained through option-selling strategies as an asset class. It however remains unclear whether the VIX futures strategy qualifies as an asset class given that it aims to exploit a market anomaly in the form of potentially non-priced volatility clustering in the S&P 500 Index. While the strategy generated considerable outperformance from 2004 to 2009, it underperformed from 2009 to 2016 suggesting that much of the non-priced volatility clustering has since been traded away. Drawing on the evidence of volatility clustering in equity markets, a managed volatility trading rule that regulates portfolio exposure between cash and equity based on how high the prevailing volatility level was relative to historical volatility levels is developed. Although transaction costs were not accounted for, the results indicated that the managed volatility trading rule has historically generated considerably superior Sharpe ratios than equity in developed and developing markets. In conclusion, volatility exposure attained through option-selling strategies has proven to be an attractive asset class, and historical evidence suggests that its inclusion into a traditional 60/40 equity/bond portfolio is likely to reduce the risk of future risk-adjusted underperformance relative to what had been achieved in the past. Additionally, the managed volatility trading rule remains an attractive alternative to investors who are precluded from investing in volatility as an asset class. / GR2018

The correct measure and definition of liquidity in finance literature remains an unresolved empirical issue. The main objective of the present study was to develop, validate and test the liquidity mismatch index (LMI) developed by Brunnermeier, Krishnamurthy and Gorton (2012) empirically. Building on the work of these prior studies, the study undertook to develop a measure of liquidity that integrates both market liquidity and funding liquidity within a context of asset liability management. Liquidity mismatch indices were developed and then tested empirically to validate them by regressing them against the known determinants of liquidity. Furthermore, the study investigated the nexus between liquidity and profitability. The unit of analysis was a panel of 12 South African banks over the period 2005–2015. The study developed two liquidity measures – the bank liquidity mismatch index (BLMI) and the aggregate liquidity mismatch index (ALMI) – whose performances were compared to and contrasted with the Basel III liquidity measures and traditional liquidity measures using a generalised method of moments (GMM) model. Overall, the two constructed liquidity indices performed better than other liquidity measures. Significantly, the ALMI provided a better macro-prudential liquidity measure that can be utilised in dynamic stochastic general equilibrium (DSGE) models, thus presenting a major contribution to the body of knowledge. Unlike the LMI, the BLMI and ALMI can be used to evaluate the liquidity of a given bank under liquidity stress events, which are scaled by theoretically motivated and empirically supported liquidity weights. The constructed BLMI contains information regarding the liquidity risk within the context of asset liability mismatches, and the measure used comprehensive data from bank balance sheets and from financial market measures. The newly developed liquidity measures are based on portfolio management theory as they account for the significance of liquidity spirals. Empirical results show that banks increase their liquidity buffers during times of turmoil as both BLMI and ALMI improved during the period 2007–2009. Subsequently, the improvement in economic performance resulted in a rise in ALMI but a decrease in BLMI. We found no evidence to support the theory that banks, which heavily depend on external funding, end up in serious liquidity problems. The findings imply that any policy implemented with the intention of increasing bank capital is good for bank liquidity since the financial fragility–crowding-out hypothesis is outweighed by the risk absorption hypothesis because the relationship between capital and bank liquidity is positive. / Finance, Risk Management and Banking / D. Phil. (Management Studies)