Suchergebnisse

AbstractWe develop a tactical asset allocation strategy that incorporates the effects of macroeconomic variables. The joint distribution of financial asset returns and the macroeconomic variables is modelled using a VAR with a multivariate GARCH (M‐GARCH) error structure. As a result, the portfolio frontier is time varying and subject to contagion from the macroeconomic variable. Optimal asset allocation requires that this be taken into account. We illustrate how to do this using three risky UK assets and inflation as a macroeconomic factor. Taking account of inflation generates portfolio frontiers that lie closer to the origin and offers investors superior risk–return combinations.

AbstractMarket events of the past ten years have sparked an interest in tactical asset allocation. In the current study we develop and test a model that incorporates currently available information into the tactical asset allocation process. The model provides an estimate of the probabilities that the upcoming market period will be bullish or bearish. Logit analysis is employed to determine which of the various timely and readily available data significantly affect these probabilities. These estimated probabilities are used to suggest the optimal allocations of funds over time between the risk‐free asset and the market portfolio. Then, several timing strategies are compared with a buy‐and‐hold portfolio. An asset allocation strategy based on the probabilities assigned by the logit model appears to achieve greater terminal wealth with less variability of returns. Similar results are obtained for both an initial sample (1962–76 in our model) and a holdout sample (1977–88).

This book covers each step in the asset allocation process, addressing as many of the relevant questions as possible along the way. How can we formulate expectations about long-term returns? How relevant are valuations? What are the challenges to optimizing the portfolio? Can factor investing add value and, if so, how can it be implemented? Which are the key performance drivers for each asset class, and what determines how they are correlated? How can we apply insights about the business cycle to tactical asset allocation? The book is aimed at finance professionals and others looking for a coherent framework for decision-making in asset allocation, both at the strategic and tactical level. It stresses analysis rather than pre-conceived ideas about investments, and it draws on both empirical research and practical experience to give the reader as strong a background as possible.--

During the Great Recession more than half of the total decline in Gross Domestic Product (GDP) in the US was, according to Berger and Vavra (2015), assigned to decreases in residential investment and new durable consumption goods. Further, residential investment had accounted for 58 percent and new durable consumption goods for 26.6 percent of GDP changes during recessions between 1960 and 2013. Since both are highly volatile, pro-cyclical, and weaken GDP in particular at the beginning of a recession, Leamer (2007) concludes, "Housing IS the business cycle". Residential structures and durable consumption goods are similar by nature; in contrast to most real assets, both are not input factors of production, but increase the consumer's utility directly. As a consequence, they are rather held by final consumers than firms, and thus, such assets are more private and serve a specific purpose. The similar internal and business cycle characteristics motivate the topics of the three essays in this thesis. These essays deal with puzzles and solution approaches or applications in differentiating between productive, market and utility augmenting, private assets inside Dynamic Stochastic General Equilibrium (DSGE) models. DSGE models are a workhorse of modern business cycle research. In Chapter 2 and 3 the utility augmenting asset is the stock of houses, which is a composite of residential structures and land. In Chapter 4 this asset is represented by the stock of durable consumption goods. While the field of housing and the business cycle was a niche, the Great Recession made it fashionable (see e.g. Davis and Nieuwerburgh (2015) or Iacoviello (2010)). The pioneer work of Davis and Heathcote (2005) takes the neoclassical perspective, where markets are perfect and the supply side induces the entire business cycle fluctuation. The pioneer work of Iacoviellon(2005) uses the New Keynsian perspective, where markets are imperfect and the demand side accounts for business cycles as well. The former work tries to account for stylized facts of the business cycle by modeling a multi-sectoral input-output linked supply structure. The following stylized facts are valid for the most developed economies: i) residential investment is at least moderately more volatile than business investment, ii) house prices are at least twice as volatile as GDP, and iii) house prices, business investment as well as GDP are positively correlated with residential investment. While Davis and Heathcote (2005) achieve success in explaining the stylized facts concerning the quantities, they fail doing so for the facts concerning house prices. Iacoviello (2005) illustrates an amplification of demand shocks through housing collateral constraints, which coincides with observations made during the Great Recession. This is why a huge strand of literature builds on Iacoviello (2005) and reduces the structural form of the supply side to the extent that some business cycle statistics could barely be matched. Further, the literature concludes that supply-side – or strictly spoken technology – shocks solely cannot account for the positive correlation of prices and quantities in the housing market and the high residential investment volatility at once (see e.g. Iacoviello and Neri (2010)). This is first due to the inability of the Davis and Heathcote (2005) model to account for the positive correlation between house prices and residential investment. Additionally, in partial equilibrium, a shift in the demand curve leads to positively correlated prices and quantities and a shift in the supply curve has the opposite effect. However, to account for the volatility of residential investments, it is mostly assumed that large shocks affect the supply of residential goods. Chapters 2 and 3 check to which extent the stylized facts, including quantities and prices, could be explained in neoclassic – strictly spoken Real Business Cycle (RBC) – models with deeper structures and real frictions. To also gain insights into asset pricing in general, Chapter 3 additionally investigates asset return behavior. The large contribution to the business cycle of residential investment and new durables, their high volatility, and their pro-cyclicality make them an interesting object of stabilizing policy. Note that investment goods have a high intertemporal substitutability as long as the depreciation rate is low. Thus, small changes in taxes and subsidies have a leverage effect. Thereby, e.g. by shifting a fraction of investment activities from a potential recovered future into a recessive present, one can smooth the business cycle with low fiscal spending. This is why the German government included a durables subsidy, i.e. a cash for clunkers program, in their fiscal stimulus program during the Great Recession 2008 and 2009. The third essay considers the Great Recession in Germany, the concerning stabilization policy and in particular the cash for clunkers program through the lens of the stochastic neoclassical growth model.