The merit of high-frequency data in portfolio allocation

This paper addresses the open debate about the usefulness of high-frequency (HF) data in large-scale portfolio allocation. Daily covariances are estimated based on HF data of the S&P 500 universe employing a blocked realized kernel estimator. We propose forecasting covariance matrices using a multi-scale spectral decomposition where volatilities, correlation eigenvalues and eigenvectors evolve on different frequencies. In an extensive out-of-sample forecasting study, we show that the proposed approach yields less risky and more diversified portfolio allocations as prevailing methods employing daily data. These performance gains hold over longer horizons than previous studies have shown

Optimal currency shares in international reserves: the impact of the euro and the prospects for the dollar

Foreign exchange reserve accumulation has risen dramatically in recent years. The introduction of the euro, greater liquidity in other major currencies, and the rising current account deficits and external debt of the United States have increased the pressure on central banks to diversify away from the US dollar. A major portfolio shift would significantly affect exchange rates and the status of the dollar as the dominant international currency. We develop a dynamic mean-variance optimization framework with portfolio rebalancing costs to estimate optimal portfolio weights among the main international currencies. Making various assumptions on expected currency returns and the variance-covariance structure, we assess how the euro has changed this allocation. We then perform simulations for the optimal currency allocations of four large emerging market countries (Brazil, Russia, India and China), adding constraints that reflect a central bank’s desire to hold a sizable portion of its portfolio in the currencies of its peg, its foreign debt and its international trade. Our main results are: (i) The optimizer can match the large share of the US dollar in reserves, when the dollar is the reference (risk-free) currency. (ii) The optimum portfolios show a much lower weight for the euro than is observed. This suggests that the euro may already enjoy an enhanced role as an international reserve currency ("punching above its weight"). (iii) Growth in issuance of euro-denominated securities, a rise in euro zone trade with key emerging markets, and increased use of the euro as a currency peg, would all work towards raising the optimal euro shares, with the last factor being quantitatively the most important. JEL Classification: F02, F30, G11, G15Currency optimizer, euro, Foreign reserves, international currencies

Evaluating Portfolio Value-at-Risk using Semi-Parametric GARCH Models

In this paper we examine the usefulness of multivariate semi-parametric GARCH models for portfolio selection under a Value-at-Risk (VaR) constraint. First, we specify and estimate several alternative multivariate GARCH models for daily returns on the S\&P 500 and Nasdaq indexes. Examining the within sample VaRs of a set of given portfolios shows that the semi-parametric model performs uniformly well, while parametric models in several cases have unacceptable failure rates. Interestingly, distributional assumptions appear to have a much larger impact on the performance of the VaR estimates than the particular parametric specification chosen for the GARCH equations. Finally, we examine the economic value of the multivariate GARCH models by determining optimal portfolios based on maximizing expected returns subject to a VaR constraint, over a period of 500 consecutive days. Again, the superiority and robustness of the semi-parametric model is confirmedmultivariate GARCH, semi-parametric estimation, Value-at-Risk, asset allocation

The Canadian Debt-Strategy Model: An Overview of the Principal Elements

As part of managing a debt portfolio, debt managers face the challenging task of choosing a strategy that minimizes the cost of debt, subject to limitations on risk. The Bank of Canada provides debt-management analysis and advice to the Government of Canada to assist in this task, with the Canadian debt-strategy model being developed to help in this regard. The authors outline the main elements of the model, which include: cost and risk measures, inflation-linked debt, optimization techniques, the framework used to model the government’s funding requirement, the sensitivity of results to the choice of joint stochastic macroeconomic term-structure model, the effects of shocks to macroeconomic and term-structure variables and changes to their long-term values, and the relationship between issuance yield and issuance amount. Emphasis is placed on the degree to which changes to the formulation of model elements impact key results. The model is an important part of the decision-making process for the determination of the government’s debt strategy. However, it remains one of many tools that are available to debt managers and is to be used in conjunction with the judgment of an experienced debt manager.Debt management; Econometric and statistical methods; Financial markets; Fiscal policy

Evaluating Portfolio Value-at-Risk using Semi-Parametric GARCH Models

In this paper we examine the usefulness of multivariate semi-parametric GARCH models for portfolio selection under a Value-at-Risk (VaR) constraint. First, we specify and estimate several alternative multivariate GARCH models for daily returns on the S&P 500 and Nasdaq indexes. Examining the within sample VaRs of a set of given portfolios shows that the semi-parametric model performs uniformly well, while parametric models in several cases have unacceptable failure rates. Interestingly, distributional assumptions appear to have a much larger impact on the performance of the VaR estimates than the particular parametric specification chosen for the GARCH equations. Finally, we examine the economic value of the multivariate GARCH models by determining optimal portfolios based on maximizing expected returns subject to a VaR constraint, over a period of 500 consecutive days. Again, the superiority and robustness of the semi-parametric model is confirmed.multivariate GARCH, semi-parametric estimation, Value-at-Risk, asset allocation.

Survey of quantitative investment strategies in the Russian stock market : Special interest in tactical asset allocation

Russia’s financial markets have been an uncharted area when it comes to exploring the performance of investment strategies based on modern portfolio theory. In this thesis, we focus on the country’s stock market and study whether profitable investments can be made while at the same time taking uncertainties, risks, and dependencies into account. We also pay particular interest in tactical asset allocation. The benefit of this approach is that we can utilize time series forecasting methods to produce trading signals in addition to optimization methods. We use two datasets in our empirical applications. The first one consists of nine sectoral indices covering the period from 2008 to 2017, and the other includes altogether 42 stocks listed on the Moscow Exchange covering the years 2011 – 2017. The strategies considered have been divided into five sections. In the first part, we study classical and robust mean-risk portfolios and the modeling of transaction costs. We find that the expected return should be maximized per unit expected shortfall while simultaneously requiring that each asset contributes equally to the portfolio’s tail risk. Secondly, we show that using robust covariance estimators can improve the risk-adjusted returns of minimum variance portfolios. Thirdly, we note that robust optimization techniques are best suited for conservative investors due to the low volatility allocations they produce. In the second part, we employ statistical factor models to estimate higher-order comoments and demonstrate the benefit of the proposed method in constructing risk-optimal and expected utility-maximizing portfolios. In the third part, we utilize the Almgren–Chriss framework and sort the expected returns according to the assumed momentum anomaly. We discover that this method produces stable allocations performing exceptionally well in the market upturn. In the fourth part, we show that forecasts produced by VECM and GARCH models can be used profitably in optimizations based on the Black–Litterman, copula opinion pooling, and entropy pooling models. In the final part, we develop a wealth protection strategy capable of timing market changes thanks to the return predictions based on an ARIMA model. Therefore, it can be stated that it has been possible to make safe and profitable investments in the Russian stock market even when reasonable transaction costs have been taken into account. We also argue that market inefficiencies could have been exploited by structuring statistical arbitrage and other tactical asset allocation-related strategies.Venäjän rahoitusmarkkinat ovat olleet kartoittamatonta aluetta tutkittaessa moderniin portfolioteoriaan pohjautuvien sijoitusstrategioiden käyttäytymistä. Tässä tutkielmassa keskitymme maan osakemarkkinoihin ja tarkastelemme, voidaanko taloudellisesti kannattavia sijoituksia tehdä otettaessa samalla huomioon epävarmuudet, riskit ja riippuvuudet. Kiinnitämme erityistä huomiota myös taktiseen varojen kohdentamiseen. Tämän lähestymistavan etuna on, että optimointimenetelmien lisäksi voimme hyödyntää aikasarjaennustamisen menetelmiä kaupankäyntisignaalien tuottamiseksi. Empiirisissä sovelluksissa käytämme kahta data-aineistoa. Ensimmäinen koostuu yhdeksästä teollisuusindeksistä kattaen ajanjakson 2008–2017, ja toinen sisältää 42 Moskovan pörssiin listattua osaketta kattaen vuodet 2011–2017. Tarkasteltavat strategiat on puolestaan jaoteltu viiteen osioon. Ensimmäisessä osassa tarkastelemme klassisia ja robusteja riski-tuotto -portfolioita sekä kaupankäyntikustannusten mallintamista. Havaitsemme, että odotettua tuottoa on syytä maksimoida suhteessa odotettuun vajeeseen edellyttäen samalla, että jokainen osake lisää sijoitussalkun häntäriskiä yhtä suurella osuudella. Toiseksi osoitamme, että minimivarianssiportfolioiden riskikorjattuja tuottoja voidaan parantaa robusteilla kovarianssiestimaattoreilla. Kolmanneksi toteamme robustien optimointitekniikoiden soveltuvan parhaiten konservatiivisille sijoittajille niiden tuottamien matalan volatiliteetin allokaatioiden ansiosta. Toisessa osassa hyödynnämme tilastollisia faktorimalleja korkeampien yhteismomenttien estimoinnissa ja havainnollistamme ehdotetun metodin hyödyllisyyttä riskioptimaalisten sekä odotettua hyötyä maksimoivien salkkujen rakentamisessa. Kolmannessa osassa käytämme Almgren–Chrissin viitekehystä ja asetamme odotetut tuotot suuruusjärjestykseen oletetun momentum-anomalian mukaisesti. Havaitsemme, että menetelmä tuottaa vakaita allokaatioita menestyen erityisen hyvin noususuhdanteessa. Neljännessä osassa osoitamme, että VECM- että GARCH-mallien tuottamia ennusteita voidaan hyödyntää kannattavasti niin Black–Littermanin malliin kuin kopulanäkemysten ja entropian poolaukseenkin perustuvissa optimoinneissa. Viimeisessä osassa laadimme varallisuuden suojausstrategian, joka kykenee ajoittamaan markkinoiden muutoksia ARIMA-malliin perustuvien tuottoennusteiden ansiosta. Voidaan siis todeta, että Venäjän osakemarkkinoilla on ollut mahdollista tehdä turvallisia ja tuottavia sijoituksia myös silloin kun kohtuulliset kaupankäyntikustannukset on huomioitu. Toiseksi väitämme, että markkinoiden tehottomuutta on voitu hyödyntää suunnittelemalla tilastolliseen arbitraasiin ja muihin taktiseen varojen allokointiin pohjautuvia strategioita

Optimal Currency Shares in International Reserves: The Impact of the Euro and the Prospects for the Dollar

Foreign exchange reserve accumulation has risen dramatically in recent years. The introduction of the euro, greater liquidity in other major currencies, and the rising current account deficits and external debt of the United States have increased the pressure on central banks to diversify away from the US dollar. A major portfolio shift would significantly affect exchange rates and the status of the dollar as the dominant international currency. We develop a dynamic mean-variance optimization framework with portfolio rebalancing costs to estimate optimal portfolio weights among the main international currencies. Making various assumptions on expected currency returns and the variance-covariance structure, we assess how the euro has changed this allocation. We then perform simulations for the optimal currency allocations of four large emerging market countries (Brazil, Russia, India and China), adding constraints that reflect a central bank%u2019s desire to hold a sizable portion of its portfolio in the currencies of its peg, its foreign debt and its international trade. Our main results are: (i) The optimizer can match the large share of the US dollar in reserves, when the dollar is the reference (risk-free) currency. (ii) The optimum portfolios show a much lower weight for the euro than is observed. This suggests that the euro may already enjoy an enhanced role as an international reserve currency ("punching above its weight"). (iii) Growth in issuance of euro-denominated securities, a rise in euro zone trade with key emerging markets, and increased use of the euro as a currency peg, would all work towards raising the optimal euro shares, with the last factor being quantitatively the most important.

The Relative Merits of Investable Hedge Fund Indices and of Funds of Hedge Funds in Optimal Passive Portfolios

Can the new investable hedge fund indices (IHF) enhance the performance of optimal passive portfolios made of equities and bonds? How do they compare to funds of hedge funds (FoHF) as well as to other alternative investments such as commodities and volatility? The conclusions depend crucially on forecasts of future expected excess returns for all assets as well as a careful conditioning of the data to reflect trading costs and remove unrealistic serial correlations. A naïve forecast based on recent historical performance leads to no allocations to either IHF or FoHF, a result explained by the performance of equities and commodities and limited diversification effects from hedge funds. Yet a forecast based on market equilibrium returns for all main asset classes but hedge funds, which are kept at their historical level, leads to the opposite result with optimal portfolios almost exclusively invested in hedge funds. Both conclusions are unrealistic and unstable. More reasonable allocations are obtained with the Black-Litterman (BL) approach to combining subjective views with equilibrium returns. Then both hedge funds instruments play a significant role in optimal passive portfolios if their expected excess returns are at least 1%. Long volatility positions are also likely to be attractive. However the BL approach can also be criticised.hedge funds, investable hedge funds indices, funds of hedge funds, commodities, VIX, mean-variance analysis, Sharpe Ratio, Adjusted Sharpe Ratio, Omega Ratio, Black Litterman model

Does it pay to invest in art? A selection-corrected returns perspective : [draft october 15, 2013]

This paper shows the importance of correcting for sample selection when investing in illiquid assets with endogenous trading. Using a large sample of 20,538 paintings that were sold repeatedly at auction between 1972 and 2010, we find that paintings with higher price appreciation are more likely to trade. This strongly biases estimates of returns. The selection-corrected average annual index return is 6.5 percent, down from 10 percent for traditional uncorrected repeat sales regressions, and Sharpe Ratios drop from 0.24 to 0.04. From a pure financial perspective, passive index investing in paintings is not a viable investment strategy once selection bias is accounted for. Our results have important implications for other illiquid asset classes that trade endogenously