Risk Management: Impact Of The Macroeconomic Variables In The Stock Market Sectors

This research enhances the importance of the risk management and decision analysis, specifically addresses the impacts of macroeconomic variables in the stock market.  The theoretical framework covers the evolution of investment analysis tools from portfolio management to recent financial theories applied to the Mexican Stock Market. In one of the recent works, Francisco Lopez, a Mexican researcher discovered evidences of different impacts of macroeconomic variables in the Mexican stock exchange market at a productive sector level.  The scope of this work is to expand and continue to exploring these findings.  The strategy is to apply statistical analysis over longitudinal data to identify and understand such impacts to the Mexican stock market. Volatility and risk impacts in the market create a challenging environment to the decision makers; analysis models could be enhanced to include these macroeconomic variables and assess impacts on specific productive sectors; the application of this knowledge will provide direct benefits to the market, the productive sectors and to the economy participants

Collective dynamics, diversification and optimal portfolio construction for cryptocurrencies

Since its conception, the cryptocurrency market has been frequently described as an immature market, characterized by significant swings in volatility and occasionally described as lacking rhyme or reason. There has been great speculation as to what role it plays in a diversified portfolio. For instance, is cryptocurrency exposure an inflationary hedge or a speculative investment that follows broad market sentiment with amplified beta? This paper aims to investigate whether the cryptocurrency market has recently exhibited similarly nuanced mathematical properties as the much more mature equity market. Our focus is on collective dynamics and portfolio diversification in the cryptocurrency market, and examining whether previously established results in the equity market hold in the cryptocurrency market, and to what extent.Comment: Equal contributio

Aspects of volatility targetting for South African equity investors

We consider so-called volatility targeting strategies in the South African equity market. These strategies are aimed at keeping the volatility of a portfolio consisting of a risky asset, typically an equity index, and cash fixed. This is done by changing the allocation of the assets based on an indicator of the future volatility of the risky asset. We use the three month rolling implied volatility as an indicator of future volatility to influence our asset allocation. We compare investments based on different volatility targets to the performance of bonds, equities, property as well as the Absolute Return peer mean. We examine risk and return characteristics of the volatility targeting strategy as compared to different asset classes.http://www.sajems.org/am201

Aspects of volatility targeting for South African equity investors

We consider so-called volatility targeting strategies in the South African equity market. These strategies are aimed at keeping the volatility of a portfolio consisting of a risky asset, typically an equity index, and cash fixed. This is done by changing the allocation of the assets based on an indicator of the future volatility of the risky asset. We use the three month rolling implied volatility as an indicator of future volatility to influence our asset allocation. We compare investments based on different volatility targets to the performance of bonds, equities, property as well as the Absolute Return peer mean. We examine risk and return characteristics of the volatility targeting strategy as compared to different asset classes

Estimation of flexible fuzzy GARCH models for conditional density estimation

In this work we introduce a new flexible fuzzy GARCH model for conditional density estimation. The model combines two different types of uncertainty, namely fuzziness or linguistic vagueness, and probabilistic uncertainty. The probabilistic uncertainty is modeled through a GARCH model while the fuzziness or linguistic vagueness is present in the antecedent and combination of the rule base system. The fuzzy GARCH model under study allows for a linguistic interpretation of the gradual changes in the output density, providing a simple understanding of the process. Such a system can capture different properties of data, such as fat tails, skewness and multimodality in one single model. This type of models can be useful in many fields such as macroeconomic analysis, quantitative finance and risk management. The relation to existing similar models is discussed, while the properties, interpretation and estimation of the proposed model are provided. The model performance is illustrated in simulated time series data exhibiting complex behavior and a real data application of volatility forecasting for the S&P 500 daily returns series

Essays on Macro-Finance Relationships

In my dissertation, I study relationships between macroeconomics and financial markets. In particular, I empirically investigate the links between key macroeconomic indicators, such as output, inflation, and the business cycle, and the pricing of financial assets. The dissertation comprises three essays. The first essay investigates how the entire term structure of interest rates is influenced by regime-shifts in monetary policy. To do so, we develop and estimate an arbitrage-free dynamic term-structure model which accounts for regime shifts in monetary policy, volatility, and the price of risk. Our results for U.S. data from 1985-2008 indicate that: i) the Fed\u27s reaction to inflation has changed over time, switching between more active and less active monetary policy regimes,: ii) the yield curve in the more active regime was considerably more volatile than in the less active regime, and: iii) on average, the slope of the yield curve in the more active regime was steeper than in the less active regime. The steeper yield curve in the more active regime reflects higher term premia that result from the risk associated with a more volatile future short-term rate given a more sensitive response to inflation. The second essay examines the predictive power of the entire yield curve for aggregate output. Many studies find that yields for government bonds predict real economic activity. Most of these studies use the yield spread, defined as the difference between two yields of specific maturities, to predict output. In this paper, I propose a different approach that makes use of information contained in the entire term structure of U.S. Treasury yields to predict U.S. real GDP growth. My proposed dynamic yield curve model produces better out-of-sample forecasts of real GDP than those produced by the traditional yield spread model. The main source of this improvement is in the dynamic approach to constructing forecasts versus the direct forecasting approach used in the traditional yield spread model. Although the predictive power of yield curve for output is concentrated in the yield spread, there is also a gain from using information in the curvature factor for the real GDP growth prediction. The third essay investigates time variation in CAPM betas for book-to-market and momentum portfolios across stock market volatility regimes. For our analysis, we jointly model market and portfolio returns using a two-state Markov-switching process, with beta and the market risk premium allowed to vary between low and high volatility regimes. Our empirical findings suggest strong time variation in betas across volatility regimes in most of the cases for which the unconditional CAPM can be rejected. Although the regime-switching conditional CAPM can still be rejected in many cases, the time-varying betas help explain portfolio returns much better than the unconditional CAPM, especially when market volatility is high

Some contributions to filtering theory with applications in financial modelling

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Two main groups of filtering algorithms are characterised and developed. Their applicability is demonstrated using actuarial and financial time series data. The first group of algorithms involved hidden Markov models (HMM), where the parameters of an asset price model switch between regimes in accordance with the dynamics of a Markov chain. We start with the known HMM filtering set-up and extend the framework to the case where the drift and volatility have independent probabilistic behaviour. In addition, a non-normal noise term is considered and recursive formulae in the online re-estimation of model parameters are derived for the case of students’ t-distributed noise. Change of reference probability is employed in the construction of the filters. Both extensions are then tested on financial and actuarial data. The second group of filtering algorithms deals with sigma point filtering techniques. We propose a method to generate sigma points from symmetric multivariate distributions. The algorithm matches the first three moments exactly and the fourth moment approximately; this minimises the worst case mismatch using a semidefinite programming approach. The sigma point generation procedure is in turn applied to construct algorithms in the latent state estimation of nonlinear time series models; a numerical demonstration of the procedure’s effectiveness is given. Finally, we propose a partially linearised sigma point filter, which is an alternative technique for the optimal state estimation of a wide class of nonlinear time series models. In particular, sigma points are employed for generating samples of possible state values and then a linear programming-based procedure is utilised in the update step of the state simulation. The performance of the filtering technique is then assessed on simulated, highly non-linear multivariate interest rate process and is shown to perform significantly better than the extended Kalman filter in terms of computational time

Otimização robusta de portfólios: Avaliação da eficiência sob condições de risco e incerteza na abordagem de estado de baixa do mercado.

O objetivo desta tese é apresentar uma nova proposta para formação de portfólios robustos a partir da análise estocástica de eficiência de ações de empresas negociadas na Bolsa de Valores, Mercadorias e Futuros de São Paulo (BM&FBovespa). Para isto, informações dos ativos em períodos de baixa do mercado (worst state) foram agrupados por meio do agrupamento hierárquico (hierarchical clustering), e então submetidos a uma análise estocástica de eficiência por meio do modelo Chance Constrained Data Envelopment Analysis. Por fim, para se obter a ideal participação de cada ativos, estes foram submetidos a um modelo clássico da alocação de capital. Os portfólios formados com o método proposto foram analisados e comparados a outros formados por diferentes modelos. A utilização em conjunto de tais abordagens abastecidas de informações de pior estado do mercado permitiu a formação de portfólios robustos que apresentaram um maior retorno acumulado no período de validação, resultaram em portfólios com menores valores beta, e ainda permitiram a inserção de variáveis fundamentalistas na formação dos portfólios

Empirical Analysis of Regime-Focused Asset Allocation Strategies within a Markov Switching Framework

This thesis consists of three papers examining the relationship between key macro-economic variables and optimal asset allocation strategies. We find evidence that asset prices behave differently depending upon the underlying economic regime. A regime-based asset allocation strategy seeks to integrate a full suite of securities across the full business cycle. We find additional evidence supporting the linkages in the literature between dynamic portfolio optimization and tactical rebalancing across unique state spaces. Paper 1 seeks to test and confirm whether the joint distribution of equity, fixed income and gold returns pursue a dynamic, non-linear pattern. We illustrate the benefits of utilising a time-varying, Markov-switching regime-based framework to forecast expected returns. Long-run historical monthly returns dating back to 1968 were used to assess return predictability. We adopt a unique approach for our empirical analysis amongst the existing regime-shifting literature by segmenting our full 50-year sample period (1968-2019) into three specific regimes (1968-1983), (1984-2007) & (2008-2019). We find evidence that supports the presence of a low-volatility premium. Economic regimes appear to be ordered by the intrinsic nature of their volatility. We have produced robust evidence supporting the negative risk-reward relationship between international equity markets and volatility. Our findings support the theories that exposures to gold offer attractive diversification benefits, particularly to equity investors. Across all four of the individual study sample periods monthly gold returns outperform during periods of excess volatility. Regime classification is structured upon a combination of empirical evidence and proven economic principles. Regimes are ordered in terms of factor exposures to economic growth, inflation and volatility. We construct a 2 x 2 factor model of growth and inflation characterised by a four-quadrant internal system. These internal regimes are classified by a combination of factors. The first order effects relate to the inter-relationship or covariance between growth and inflation. The second order effects constitute the policy response to this environment. Multiple linear modelling equations are used to identify causal relationships between dependent financial assets and our predictor variables. These were split between regime-agnostic, contiguous data sampling methods and regime-specific, non-contiguous data sampling. The findings appear consistent with the prevailing macroeconomic theory that broader equity market returns outperform gold, fixed income and commodity assets during specific market regimes and that gold should outperform the S&P500 across inflationary regimes. In paper 3 there was a focus on whether dynamic asset allocation strategies can capture enhanced portfolio opportunities through profitable sector pivots, factor exposures and optimization. We developed a unique leading indicator framework utilising statistically significant predictor variables to inform the regime-based asset allocation process. Furthermore, robustness checks were conducted across a diverse range of assets including individual equity sectors, mutual funds, tradeable assets and investment factors. This study is distinctive in its approach of utilising this Bayesian grounded leading indicator framework and in the scope of the assets used to test its robustness