[pt] Diversos trabalhos publicados recentemente analisam o impacto da entrada de bancos estrangeiros no mercado bancário brasileiro. Muitos desses estudos avaliam o impacto dessa entrada no desempenho dos bancos nacionais, na oferta de crédito e em outras variáveis. Outros estudos enfatizam a oferta de crédito e o papel social dos bancos como fontes de financiamento para a iniciativa privada. Entretanto nenhum estudo amplo foi realizado com o objetivo de se identificar quais são os fatores determinantes da estratégia de alocação de ativos dos bancos com atividades de varejo no Brasil, e se esses fatores diferem entre bancos privados nacionais, bancos estrangeiros e bancos estatais. Para responder essas questões este estudo analisa dados das demonstrações financeiras de 35 bancos referentes ao período de 2000 a 2003. Os bancos da amostra foram selecionados na lista dos 50 Maiores Bancos por Ativos Totais (-) Intermediações, elaborada pelo Banco Central do Brasil, com um critério adicional de terem apresentado mais de cinco agências bancárias em qualquer ano do período analisado. Este trabalho enfatiza as características dos bancos como fatores determinantes da estratégia de alocação de ativos. Foram realizados testes estatísticos e regressões de dados em painel considerando três grupos de regressores: tamanho do banco, tipo de controle, e sua estrutura de financiamento. Os resultados sugerem que há diferenças no financiamento dos ativos entre bancos privados nacionais, estrangeiros e estatais. / [en] Several recent papers analyze the impact of foreign bank entry on the Brazilian bank market. Some of these studies assess the impact of those entrances on domestic banks` performance, on the overall credit supply, and on other variables. Other studies emphasize the credit supply and the banks` social role as financiers of the private sector. Nevertheless, no comprehensive study has aimed at identifying the determinant factors of asset allocation strategies of banks with retail activities in Brazil, and whether these factors vary according to different bank ownership control - private domestic, government, and foreign. With the objective of shedding some light on this subject, this work analyzes the financial statements of 35 banks in the time period 2000 - 2003. The sampled banks were selected from the list of the Largest 50 Banks by Total Assets (-) Intermediations, organized by The Central Bank of Brazil, with the additional criteria of having more than 5 offices in any year of the considered time period. The present work emphasizes the banks` characteristics as determinant factors of asset allocation strategies. Statistical tests and panel data analysis were run allowing for three regressor groups: bank size, ownership control, and funding structure. The results suggest there are significant differences between private domestic, government, and foreign banks in financing their assets.

巴塞爾銀行監督委員會(Basel Committee on Banking Supervision, BCBS) 於2010年發布巴塞爾資本協定三。為強化銀行流動性風險管理，新增兩項流動性風險量化衡量指標：流動性覆蓋比率(Liquidity Coverage Ratio, LCR)以及淨穩定資金比率(Net Stable Funding Ratio, NSFR)。我國於2015年開始將流動性覆蓋比率納入監管要求，亦將於2018年開始導入淨穩定資金比率。然而在提高銀行風險控管及標準的同時，銀行需考量其股東權益報酬。新規範的實施使銀行需要進行調整以符合法規，過往鮮少有研究針對本國銀行探討其資產配置調整與結構調整。本研究除探討個案銀行如何在巴塞爾資本協定三框架下調整其資產負債配置與結構，更進一步探討其各項調整對銀行之獲利能力以及各項法定比率之影響，希望能幫助銀行在未來調整結構之前能更了解其決策所帶來之影響。 本研究發現，在不提高資產負債表規模的情況下，可以透過銀行結構調整達到巴塞爾資本協定三於2019年之標準，同時提高銀行獲利能力；在適度提高資產負債表規模的情況之下，其獲利能力高於不提高資產負債表規模之情況。此外，本研究針對不同情境探討銀行應如何調整資產負債配置與銀行結構。風險趨避情境相較於風險偏好下，應在存放款方面，吸收更多長天期之存款、降低長期放款占比；資產配置方面則應增加政府公債占比。由於巴塞爾資本協定三採階段性實施，本研究針對個案銀行2015到2019 年之資產負債配置與銀行結構做研究，發現個案銀行隨著法規越趨嚴格，應提高公司債占比並同時降低權益類等相對風險較高之資產占比；另一方面為達到淨穩定資金比率要求，銀行應提高其長期存款占比。最後，本研究針對各項結構與資產負債配置調整做更深入的分析，探討其對於各項指標之敏感度，以實際的量化數字表示每項變動的影響，以利銀行在做決策時更了解其決策之利與弊。 / Basel Committee on Banking Supervision (BCBS) released Basel III in 2010. In order to ensure the maintenance and stability of funding and liquidity profiles of banks’ balance sheets, two liquidity standards, Liquidity Coverage Ratio(LCR) and Net Stable Funding Ratio(NSFR), were introduced in Basel III. To in line with international norm, Taiwan government plans to implement LCR and NSFR in 2015 and 2018 respectively. However, there is a trade-off between return and risk. With the implement of new law, how to adjust banks’ asset allocation becomes a critical issue. In this study, we focus on business structure and ways to adjust A bank’s asset allocation. We found that A bank can meet government’s requirements and increase it’s return on equity without increasing balance sheet size by adjusting business structure; In the situation where balance sheet size is increased, A bank can meet the requirements with higher return on equity than where the balance sheet size isn’t increased. In three different scenarios: risk seeking, risk neutral and risk aversion, we found that A bank should increase more long-term deposits and decrease long-term loans in risk aversion scenario than in risk seeking scenario. In risk aversion scenario, A bank should also hold more government bonds than in risk seeking scenario. From 2015 to 2019, the requirements become stricter and stricter, A bank should hold more corporate bonds and less securities. At the same time, A bank should increase more long-term deposits to meet the NSFR requirement. The research also shows how business structure and asset allocation changes can affect A bank’s related required ratio and return on equity. Our findings can help A bank makes more precise decision by knowing actual quantitative influence before they implement the new policies.

增強式學習（Reinforcement Learning）透過與環境不斷的互動來學習，以達到極大化每一期報酬的總和的目標，廣泛被運用於多期的決策過程。基於這些特性，增強式學習可以應用於建立需不斷動態調整投資組合配置比例的動態資產配置策略。 本研究應用Deep Q-Learning演算法建立動態資產配置策略，研究如何在每期不同的環境狀態之下，找出最佳的配置權重。採用2007年7月2日至2017年6月30日的美國中大型股的股票ETF及投資等級的債券ETF建立投資組合，以其日報酬率資料進行訓練，並與買進持有策略及固定比例投資策略比較績效，檢視深度增強式學習在動態資產配置適用性。 / Reinforcement learning learns by interacting with the environment continuously, in order to achieve the target of maximizing the sum of each return. It has been used to solve multi-period decision making problem broadly. Because of these characteristics, reinforcement learning can be applied to build the strategies of dynamic asset allocation which keep reallocating the mix of portfolio consistently. In this study, we apply deep Q-Learning algorithm to build the strategies of dynamic asset allocation. Studying how to find the optimal weights in the different environment. We use Large-Cap, Mid-Cap ETFs and investment-grade bond ETFs in the U.S. to build up the portfolio. We train the model with the data of daily return, and then we measure its performance by comparing with buy-and-hold and constant-mix strategy to check the fitness of deep Q-Learning.

The financial markets have a complex structure and the modelling techniques have recently been more and more complicated. So for a portfolio manager it is very important to find better and more sophisticated modelling techniques especially after the 2007-2008 banking crisis. The idea in this thesis is to find the connection between the components in macroeconomic environment and portfolios consisting of assets from OMX Stockholm 30 and use these relationships to perform Tactical Asset Allocation (TAA). The more specific aim of the project is to prove that dynamic modelling techniques outperform static models in portfolio theory. / Den finansiella marknaden är av en väldigt komplex struktur och modelleringsteknikerna har under senare tid blivit allt mer komplicerade. För en portföljförvaltare är det av yttersta vikt att finna mer sofistikerade modelleringstekniker, speciellt efter finanskrisen 2007-2008. Idéen i den här uppsatsen är att finna ett samband mellan makroekonomiska faktorer och aktieportföljer innehållande tillgångar från OMX Stockholm 30 och använda dessa för att utföra Tactial Asset Allocation (TAA). Mer specifikt är målsättningen att visa att dynamiska modelleringstekniker har ett bättre utfall än mer statiska modeller i portföljteori.

[pt] A teoria moderna de carteiras estabelece que a alocação ótima de ativos é uma função da média-variância da distribuição dos retornos. Na prática, estes retornos são modelados por distribuições Gaussianas e seus parâmetros são estimados a partir dos dados históricos do mercado, utilizando técnicas descritivas da estatística Frequentista. A dinâmica atual dos mercados globalizados gera períodos aleatórios de alta e baixa volatilidade e/ou saltos nos retornos dos ativos, provocando mudanças de regime ou quebras estruturais na série temporal dos retornos, tornando-os não Gaussianos. Consequentemente, a teoria moderna de carteiras precisa ser adaptada para atender a estas novas condições do mercado. Para contornar o problema das mudanças de regime, propõe-se a substituição do mecanismo de otimização baseada no índice de Sharpe pela otimização baseada na medida Ômega. Isto porque a medida Ômega tem a vantagem de quantificar o risco-retorno de qualquer distribuição de probabilidade e não somente distribuições Gaussianas como acontece com o índice de Sharpe, ou seja, as distribuições de retornos não Gaussianos provocadas pelas mudanças de regime são tratadas naturalmente pela medida Ômega. Para contornar o problema das quebras estruturais, propõe-se a substituição do procedimento de estimação dos parâmetros da distribuição dos retornos, baseada em técnicas da estatística Frequentista por técnicas da estatística Bayesiana. Isto porque a estatística Bayesiana, tem a vantagem de combinar as informações públicas do mercado (dados históricos dos retornos) com informações privadas do investidor (visões prospectivas do mercado) permitindo corrigir a quebra estrutural e, na sequência, tratar o retorno não Gaussiano, utilizando o mecanismo de otimização baseada na medida Ômega. / [en] Modern portfolio theory states that the optimal asset allocation is a function of the mean-variance of the distribution of returns. In practice, these returns are modeled by Gaussian distributions and their parameters are estimated from historical market data, using descriptive techniques of Frequentist statistics. The current dynamics of globalized markets generate random periods of high and low volatility and/or jumps in asset returns, causing regime shifts or structural breaks in the time series of returns, making them non Gaussian. Consequently, modern portfolio theory needs to be adapted to meet these new market conditions. To circumvent the problem of regime shifts, it is proposed to replace the optimization mechanism based on the Sharpe index by the optimization based on the Omega measure. This is because the Omega measure has the advantage of quantifying the risk-return of any probability distribution and not only Gaussian distributions as with the Sharpe index, that is, non Gaussian returns distributions caused by regime shifts are treated naturally by the Omega measure. To circumvent the problem of structural breaks, it is proposed to replace the estimation procedure for the parameters of the distribution of returns, based on Frequentist statistics techniques, by Bayesian statistical techniques. This is because the Bayesian statistic has the advantage of combining public market information (historical return data) with private investor information (prospective market views) allowing to correct the structural break, and subsequently, treating the non Gaussian return using the optimization based on the Omega measure.

Mean variance optimization has shortcomings making the strategy far from optimal from an investor’s perspective. The purpose of the study is to conduct an empirical investigation as to how modern methods of portfolio optimization address the shortcomings associated with mean variance optimization. Equal risk contribution, the Most diversified portfolioand a modification of the Minimum variance portfolio are considered as alternatives to the mean variance model. Portfolio optimization models introduced are explained in detail and solved using the optimization algorithms Cyclical coordinate descent and Alternating direction method of multipliers. Through implementation and backtesting using a diverse set of indices representing various asset classes, the study shows that the mean variance model suffers from high turnover and sensitivity to input parameters in comparison to the modern alternatives. The sophisticated asset allocation models equal risk contribution and the most diversified portfolio do not rely on expected return as an input parameter, which is seen as an advantage, and are not affected to the same extent by the shortcomings associated with mean variance optimization. The paper concludes by discussing the findings critically and suggesting ideas for further research. / Maximering av avkastning i samband med minimering av varians, på engelska kallat Mean variance optimization, är inte optimalt ur en investerares synpunkt. Syftet med denna uppsats är att genomföra en empirisk studie av hur moderna metoder för portföljallokering adresserar de problem som är förknippade med Mean variance optimization. Mer specifikt undersöks allokeringsstrategierna Equal risk contribution, Most diversified portfolio samt en variant av Minimum variance som ersättare till Mean variance optimization. Allokeringsmetoderna beskrivs detaljerat och löses med optimeringsalgoritmerna Cyclical coordinate descent och Alternating direction method of multipliers. Genom implementering och historisk simulering med ett antal index som representerar olika tillgångsslag visar studien att Mean variance optimization innebär hög portföljomsättning och har en större känslighet för ingångsparametrar i jämförelse med de moderna alternativen. De sofistikerade allokeringsmodellerna Equal risk contribution och Most diversified portfolio bygger inte på ingångsparametern förväntad avkastning, vilket ses som en fördel, och drabbas inte i samma utsträckning av problemen associerade med Mean variance optimization. Studien avslutas med att diskutera resultatet kritiskt och ge förslag på vidare studier som bygger på den teori och det resultat som har presenterats.

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本研究探討長期投資人之最適資產配置問題，並著重於通貨膨脹風險之分析。第一部份討論確定提撥退休金制度下，機構投資人或高所得自然人如何擬定投資策略規避通貨膨脹風險，達到極大化期末財富效用期望值。此研究擴展Battocchio與Menoncin (2004)所建構資產模型，不僅探討市場風險，亦考量通貨膨脹不確定性與基金費用誘因、下方風險保護兩機制，研究對資產配置行為之影響，並依動態規劃方法求得投資策略公式解。第二部份則強調下方風險之重要性，檢視在最低保證收益下，長期投資人跨期資產配置之財富管理議題，並回顧Deelstra et al.(2003)之模型架構，依平賭方法求得投資策略公式解，研究結果顯示基金投資策略可表示為最適CRRA(γ,T)型態共同基金與最低收益避險之組合。另一方面，如何估計通貨膨脹風險亦為本文強調之重點。Campbell和Viceira (2001)首次納入通貨膨脹風險並探討跨期投資議題，結論市場缺乏通貨膨脹連動投資標的時，投資人將減碼長期債持有比例。Brennan和Xia (2002)假設通貨膨脹率服從Ornstein-Uhlenbeck過程，結論投資人之避險需求隨持有債券到期日與投資期限改變。但以上結論未將通貨膨脹學習機制納入模型，因此，在第三部份提出依學習機制修正之投資策略可顯著增加財富效用，並分析在不同參數設定下，學習機制對於期末財富效用之影響。 / In this study, we study three essays of asset allocation problem for long term investors, which means that in this discourse we emphasis the importance of inflation risk. In the first topic, we derive the dynamic optimal investment strategy of the defined contribution pension schemes which include two mechanisms of partial floor protection and incentive fees and their benchmarks. We find investors should hold high proportion of stock index fund to hedge the inflation risk; moreover, the ratio of incentive fees to the setting of benchmark will change the optimal investment trend of underlying assets. In the second topic, we introduce the optimal investment portfolio with minimum guarantees and show that the fund manager should adjust the optimal weights of underlying assets with the ratio of the guarantee fund's value to the value of fund. Finally, this work focuses on how to precisely predict the dynamics of inflation rate. We apply learning method to adjust the prediction of inflation process and we use numerical analysis to study the effect of learning mechanism under different parameter setting.

本論文包含台灣壽險業資產配置國際化及匯率避險兩個主題。首先，探討台灣壽險業積極向主管機關申請核准提高國外資產配置比率，與美國投資人偏好投資自己國家資產不一致的現象，是台灣壽險業資產配置不得不的策略，或是國際化的迷思? 以25家壽險公司2004 年至2008年財務資料實證結果發現，國外資產納入投資組合對壽險業投資績效有利。如果將樣本公司依據所有權區分為本資公司與外資公司，資料顯示，本資公司國際資產配置較為積極，惟其報酬績效與外資公司差異並不顯著。研究亦發現，2008年美國次貸風暴顯著負向影響台灣壽險公司國外投資報酬，即提高國外資產配置雖可提高報酬，惟匯率風險、信用風險及系統風險暴露亦相對提高，建議壽險公司於追球較高報酬同時，應同時加強風險管理。另實證亦發現，資產規模愈大公司之投資報酬率相對較遜，建議於追求保費市占率成長時，應重視投資報酬績效的實質提升。 第二部分探討2004年至 2008年台灣壽險業國外投資匯率風險管理策略對投資績效影響。以整體產業觀察，匯率避險對投資報酬率有正面效果；本資公司避險策略相較外資公司積極，報酬率亦相對較優；股票上市公司有財報揭露股價波動之壓力，經理人有較強誘因採取避險策略，投資報酬率相較優於股票未上市公司，惟差異並不顯著。實證結果支持Glen and Jorion (1993) and Campbell et al. (2010)避險可以降低匯率風險提升投資報酬績效之研究結論，2006年實施之34號會計公報，顯著影響本資公司與上市公司之避險行為。 / In this study, we study two essays on international asset allocation and the currency hedging problem for Taiwan life insurer industry. In the first essay, we investigate the high percentage of foreign investments placed by Taiwan life insurers and how this phenomenon is at odds with the bias for investing at home common among American investors. The holdings of 25 Taiwan life insurance companies, between the years 2004 and 2008, are scrutinized with a view towards evaluating home bias and its financial impact. We find that foreign investment has proven profitable for the life insurance industry. However, if the life insurance industry is divided into two categories according to its ownership structure, i.e., domestic-owned and foreign-owned companies, and that while the performance of investments made by domestic-owned life insurers differs from that of foreign-owned life insurers, the difference is insignificant. We also found that global financial turmoil in 2008 had a massively negative impact on the foreign investments of Taiwan life insurance companies and firm size and return on investment is negative correlated, suggesting that life insurers should focus on enhancing investment performance and risk management. In the second essay, we examine the currency hedging strategy and its impact on the performance of Taiwan life insurance industry investments from 2004 to 2008. We find that currency hedging strategies have yielded positive results, overall, for the industry. However, if the life insurance industry is divided into two categories according to its ownership structure, i.e., domestic-owned and foreign-owned companies, the results show that the currency hedging strategies employed by the domestic-owned companies enjoy advantages over those of foreign-owned firms. If the sample is further divided into those publicly listed on the TAIEX and others, our results show that a hedging strategy has positive effects on listed company. Our findings support the work in Glen and Jorion (1993) and Campbell et al. (2010), which reveal that hedging strategies improve foreign investment returns and can reduce currency risks in comparison to non-hedging strategies. Our empirical results indicate that SFAS No. 34 has a significant effect on currency hedging behavior among domestically owned and listed companies.

隨著我國壽險業資產比率快速增加，投資績效不但影響眾多保戶的權益，更影響整體經濟之安定，但面臨現今全球金融和經濟環境劇烈動盪，加上壽險同業間激烈競爭的情況，我國壽險公司如何訂定投資策略做好資產配置，對壽險公司的營運健全而言相當重要。現行保險法第一百四十六條限制壽險公司的投資上限，但法令限制對壽險公司資產配置的影響為何，本研究將透過Markowitz的平均數－變異數投資組合模式對我國整體壽險業及國泰人壽、南山人壽、新光人壽及富邦人壽探討之；並以夏普指數、崔納指數及詹森指數，評估上述四家壽險公司之資金運用績效；另藉由分析壽險業之資金成本是否低於實際投資率，以達成研究壽險業長期資產配置之穩健度。 本研究主要結論如下：1.運用Markowitz 投資組合模型所推導出的效率前緣，以最大Sharpe Measure評估，不受法令限制下所建立之最佳投資組合，較有受法令限制下所建立之最佳投資組合的期望報酬率高，且分散風險的效果較佳。2.整體壽險業及前四大壽險公司之實際投資報酬率皆低於其實際投資組合之期望報酬，顯示壽險業於資金運用的靈活度及績效性有改善的空間。3.以績效評估指標求出前四大壽險公司的資金運用績效，發現新光人壽在此三項評估指標皆位居最後；國泰人壽在評估中皆名列前茅。4.新光人壽的平均實際報酬率低於平均資金成本，應控管好資金成本並加強投資組合之績效；富邦人壽平均實際報酬率高過平均資金成本最多，顯示富邦人壽在資金成本控管及投資績效有良好之表現。整體壽險業的實際報酬率亦高於平均資金成本，顯示我國壽險業於營運狀況正常。 / With the life insurance companies’ assets ratio rapidly increasing, the investment performances affect not only the right of a number of policy holders, but also affect the economic stability. However, facing the dynamic global financial and economic environment and the keen competition in the domestic life insurance industry, the life insurance companies need to adopt the proper investment strategy. Law of Insurance 146th p restricts the investment upper limit of the life insurance company. This research will use Markowitz MV model to discuss the influence of this investment restriction on life insurance companies’ asset allocation by the samples of Life industry, Cathay Life Insurance, Nan Shan Life Insurance, Shin Kong Life Insurance, and Fubon Life Insurance, and evaluate the performances of these four life insurance companies by Sharpe ratio, Treynor ratio, and Jensen’s measure. This research also analyze the cost of capital and real rate of return of these companies to examine the stability of life insurance industry’s long term asset allocation. The conclusions of this research are as follows: 1.Evaluated by the Markowitz efficient frontier and the Sharpe measure, there is the higher expected rate of return and better diversification with no investment restriction. 2.The actual rates of return of the life insurance industry and the above four life insurance companies are below the expected rates of returns of their portfolio evaluated be the Sharpe measure, which means the life insurance industry need to prove their capital allocation. 3. Comparing the performance of the life insurance companies by the performance indicator, we find the then Shin Kong Life Insurance is the last, while Cathay Life Insurance has a good score. 4. We also find the real rate of return of Shin Kong Life Insurance is lower than its cost of capital, which means Shin Kong Life Insurance need to adjust its cost of capital and the investment performance. Meanwhile, Fubon Life Insurance is the excellent in controlling the cost of capital and investment. The real rate of return of the Life insurance industry is higher than its cost of capital, and that shows the Life insurance industry has normal operation.