Submitted by Yugo Watari (ywatari@gmail.com) on 2017-09-19T16:23:48Z No. of bitstreams: 1 main.pdf: 2611498 bytes, checksum: 1f50a4c20e7433334a4e2b45acd23424 (MD5) / Approved for entry into archive by Thais Oliveira (thais.oliveira@fgv.br) on 2017-09-19T16:31:51Z (GMT) No. of bitstreams: 1 main.pdf: 2611498 bytes, checksum: 1f50a4c20e7433334a4e2b45acd23424 (MD5) / Made available in DSpace on 2017-09-19T17:31:18Z (GMT). No. of bitstreams: 1 main.pdf: 2611498 bytes, checksum: 1f50a4c20e7433334a4e2b45acd23424 (MD5) Previous issue date: 2017-08-21 / We approach portfolio construction with risk based allocation, using volatility as the measure of risk, and applying to the stock markets. We start by obtaining generic risk factors based on the approach of Fama&French; and them we decompose the volatility in risk contributions of those generic risk factors. Differing from previous works, instead of allocating in indexes that represent the generic risk factors, we allocate at the asset level, in hopes that this will lead to reproducing the effects of inveting on those indexes, which brings additional complexity to the problem. This was motivated by investors not always having access to invest in theses indexes. Finally, for the purpose of illustration, we apply the metodology to the brazilian stock markets, selecting as risk factors, the five Fama&French risk factors. We obtain portfolios with the desired risk contributions, but as we look in to the weights of each risk factor, there is alocations of weights in the risk factors not related to those of Fama&French, even though the risk contributions are neutralized. We argue that these allocations are preventing from obtaining exposures to the distinct characteristics of each Fama&French risk factor. / A construção de portfólios, ou seja, a definição da composição de uma carteira de ativos, é abordada, nesse trabalho, pela ótica da alocação baseada em contribuições do risco, medida via volatilidade, aplicada a uma carteira de ações. O objetivo é a construção de portfolios, via as contribuições de riscos; para isto construímos fatores de riscos genéricos baseados na abordagem de Fama&French; na sequência aplicamos uma metodologia para distribuir a volatilidade como contribuições de risco destes fatores genéricos. Diferentemente de outros trabalhos, ao invés de alocar em índices que representem estes fatores de riscos genéricos, alocamos diretamente nos ativos na expectativa de conseguirmos reproduzir o efeito de investir nestes índices, o que traz uma complexidade adicional. Esta abordagem foi motivada por nem sempre termos acesso à investir nesses índices. Finalmente, a título de ilustração, a metodologia foi aplicada ao mercado brasileiro de ações, em particular utilizando os fatores do modelo Fama&French de 5 fatores. Obtivemos portfolios com as contribuições de riscos desejadas em relação aos fatores de Fama&French, mas ao se analisar a alocação dos pesos dos fatores de riscos sobre os portfolios obtidos, verificamos que são alocados pesos a fatores que não estão relacionados aos de Fama&French, apesar das contribuições de risco destas estarem neutralizadas. E por fim argumentamos que estas alocações evitam a captura das características distintas de cada fator que gostaríamos de reproduzir.

Following the global financial crisis, both risk-based and heuristic portfolio construction methods have received much attention from both academics and practitioners since these methods do not rely on the estimation of expected returns and as such are assumed to be more stable than Markowitz's traditional mean-variance portfolio. In 2016, Lopéz de Prado presented the Hierarchical Risk Parity (HRP), a new approach to portfolio construction which combines hierarchical clustering of assets with a heuristic risk-based allocation strategy in order to increase stability and improve out-of-sample performance. Using Monte Carlo simulations, Lopéz de Prado was able to demonstrate promising results. This thesis attempts to evaluate HRP using walk-forward analysis and historical data from equity index and bond futures, against more realistic benchmark methods and using additional performance measures relevant to practitioners. The main conclusion is that applying hierarchical clustering to risk-based portfolio construction does indeed improve the out-of-sample return and Sharpe ratio. However, the resulting portfolio is also associated with a remarkably high turnover, which may indicate numerical instability and sensitivity to estimation errors. It is also identified that Lopéz de Prado's original HRP approach has an undesirable property and alternative approaches to HRP have consequently been developed. Compared to Lopéz de Prado's original HRP approach, these alternative approaches increase the Sharpe ratio with ~10% and reduce the turnover with 60-65%. However, it should be noted that compared to more mainstream portfolios the turnover is still rather high, indicating that these alternative approaches to HRP are still somewhat unstable and sensitive to estimation errors. / Efter den globala finanskrisen har intresset för riskbaserade och heuristiska metoder för portföljallokering ökat inom såväl akademin som finansindustrin. Det ökade intresset grundar sig i att dessa metoder inte kräver estimering av förväntad avkastning och därför kan antas vara mer stabila än portföljer med grund i Markowitz moderna portföljteori. Lopéz de Prado presenterade 2016 en ny metod för portföljallokering, Hierarchical Risk Parity (HRP), som kombinerar hierarkisk klustring med en heuristisk riskbaserad portföljkonstruktion och vars syfte är att öka stabiliteten och förbättra avkastningen. Baserat på Monte Carlo-simuleringar har Lopéz de Prado lyckats påvisa lovande resultat. Syftet med detta examensarbete är att utvärdera HRP med hjälp av walk-forward-analys och empirisk data från aktieindex- och obligationsterminer. I denna utvärdering jämförs HRP med andra vanliga portföljmetoder med avseende på prestandamått relevanta för portföljförvaltare. Den huvudsakliga slutsatsen är att tillämpning av hierarkisk klustring inom ramen för riskbaserad portföljallokering förbättrar såväl den absoluta avkastningen som Sharpekvoten. Däremot är det tydligt att vikterna i en HRP-portfölj har hög omsättning över tid, vilket kan tyda på numerisk instabilitet och hög känslighet för skattningsfel. Vidare har en oönskad egenskap i Lopéz de Prados ursprungliga HRP-metod identifierats, varför två alternativa HRP-metoder har utvecklats inom ramen för examensarbetet. Jämfört med Lopéz de Prados ursprungliga metod förbättrar de två alternativa metoderna Sharpekvoten med 10% och minskar omsättningen av portföljvikterna med 60-65%. Det bör dock understrykas att även de nya metoderna har en förhållandevis hög omsättning, vilket tyder på att numerisk instabilitet och hög känslighet för skattningsfel till viss del fortfarande kvarstår.