Forecasting The South African Business Cycle Using Fourier Analysis

2018

SAJEMS

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Background: Procyclicality plays a pivotal role in finance in both thriving and crisis periods. This influence stems not only from the way market participants behave but also from risk metrics used and regulatory capital amassed and released during bust and boom periods, respectively. The introduction of the regulatory Countercyclical Capital Buffer aims to thwart procyclicality by accumulating (releasing) capital in upswings (downswings), subsequently reducing the amplitude of the financial cycle and promoting macroprudential stability. The timing of the accumulation and release of buffer capital is critical so identifying accurate indicators is important. Aim: This paper applies a Kalman filter to South African data and confirms the procyclicality of the Basel Committee on Banking Supervision (BCBS) proposal. Setting: For South Africa, studies suggest alternatives such as residential property indices because research has demonstrated that the BCBS proposal is procyclical rather than countercyclical. Methods: This paper applies a Kalman filter to South African data and compares the results obtained with those filtered using the Hodrick–Prescott filter. Results: Results indicate that buffer signals are dependent upon the filter employed. Conclusion: Buffer signals are strongly dependent upon the filter employed to detect procyclicality. The South African Reserve Bank and other regulators should reconsider the use of the Hodrick–Prescott filter and entertain the possibility of using the Kalman filter instead.

Section: Jan-65supporting

confidence: 88%

Filter selection for countercyclical capital buffers

Visser

2018

SAJEMS

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“…This sample size was selected to include at least three full South African business cycles. This has been shown to be % seven years (Botha, 2004;Thomson and van Vuuren, 2016). In addition, these data embrace a period of non-volatile growth (2003)(2004)(2005)(2006)(2007)(2008), and considerable turbulence (1998-2000 (the Asian crisis and the dotcom crash) and 2008-2011 (the credit crisis)).…”

Section: Datamentioning

confidence: 89%

Investment Implications of the Fractal Market Hypothesis

Karp

2019

Ann. Finan. Econ.

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The Efficient Market Hypothesis (EMH) has been repeatedly demonstrated to be an inferior — or at best incomplete — model of financial market behavior. The Fractal Market Hypothesis (FMH) has been installed as a viable alternative to the EMH. The FMH asserts that markets are stabilized by matching demand and supply of investors’ investment horizons while the EMH assumes that the market is at equilibrium. A quantity known as the Hurst exponent determines whether a fractal time series evolves by random walk, a persistent trend or mean reverts. The time dependence of this quantity is explored for two developed market indices and one emerging market index. Another quantity, the fractal dimension of a time series, provides an indicator for the onset of chaos when market participants behave in the same way and breach a given threshold. A relationship is found between these quantities: the larger the change in the fractal dimension before breaching, the larger the rally in the price index after the breach. In addition, breaches are found to occur principally during times when the market is trending.

“…The choice of 5y monthly sample period was based on the observation that fund managers generally use between three and five years of monthly return data for portfolio construction and performance metric estimation (Marhfor, 2016). The authors opted for the longer-range to include more return data and embrace a greater fraction of the business cycle (the average business cycle frequency in South Africa is about seven years (Thomson & van Vuuren, 2016), so the 5y sample period encapsulates almost one full cycle). Shorter periods encompass too few data to provide robust metrics and longer periods use too many return data because markets change considerably over longer periods, styles alter, market dynamics adapt to different trends.…”

Section: Resultsmentioning

confidence: 99%

Optimal omega-ratio portfolio performance constrained by tracking error

Gunning

Investment Management and Financial Innovations

2020

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The mean-variance framework coupled with the Sharpe ratio identifies optimal portfolios under the passive investment style. Optimal portfolio identification under active investment approaches, where performance is measured relative to a benchmark, is less well-known. Active portfolios subject to tracking error (TE) constraints lie on distorted elliptical frontiers in return/risk space. Identifying optimal active portfolios, however defined, have only recently begun to be explored. The Ω – ratio considers both down and upside portfolio potential. Recent work has established a technique to determine optimal Ω – ratio portfolios under the passive investment approach. The authors apply the identification of optimal Ω – ratio portfolios to the active arena (i.e., to portfolios constrained by a TE) and find that while passive managers should always invest in maximum Ω – ratio portfolios, active managers should first establish market conditions (which determine the sign of the main axis slope of the constant TE frontier). Maximum Sharpe ratio portfolios should be engaged when this slope is &gt; 0 and maximum Ω – ratios when &lt; 0.