Monte Carlo Simulation in the Integrated Market and Credit Portfolio Model

Credit granting institutions deal with large portfolios of assets. These assets represent credit granted to obligors as well as investments in securities. A common size for such a portfolio lies from anywhere between 400 to 10,000 instruments. The essential goal of the credit institution is to minimize their losses due to default. By default we mean any event causing an asset to stop producing income. This can be the closure of a stock as well as the inability of an obligor to pay their debt, or even an obligor's decision to pay out all his debt. Minimizing the combined losses of a credit portfolio is not a deterministic problem with one clean solution. The large number of factors influencing each obligor, different market sectors, their interactions and trends, etc. are more commonly dealt with in terms of statistical measures. Such include the expectation of return and the volatility of each asset associated with a given time horizon. In this sense, we consider in the following the expected loss and risk associated with the assets in a credit portfolio over a given time horizon of (typically) 10 to 30 years. We use a Monte Carlo approach to simulate the loss of a portfolio in multiple scenarios, which leads to a distribution function for the expected loss of the portfolio over that time horizon. Second, we compare the results of the simulation to a Gaussian approximation obtained via the Lindeberg-Feller Theorem. Consistent with our expectations, the Gaussian approximation compares well with a Monte Carlo simulation in case of a portfolio of very risky assets. Using a model which produces a distribution of expected losses allows credit institutions to estimate their maximum expected loss with a certain confidence interval. This in turn helps in taking important decisions about whether to grant credit to an obligor, to exercise options or otherwise take advantage of sophisticated securities to minimize losses. Ultimately, this leads to the process of credit risk management

Portfolio Selection via Topological Data Analysis

Portfolio management is an essential part of investment decision-making. However, traditional methods often fail to deliver reasonable performance. This problem stems from the inability of these methods to account for the unique characteristics of multivariate time series data from stock markets. We present a two-stage method for constructing an investment portfolio of common stocks. The method involves the generation of time series representations followed by their subsequent clustering. Our approach utilizes features based on Topological Data Analysis (TDA) for the generation of representations, allowing us to elucidate the topological structure within the data. Experimental results show that our proposed system outperforms other methods. This superior performance is consistent over different time frames, suggesting the viability of TDA as a powerful tool for portfolio selection

FDI Contribution to Capital Flows and Investment in Capacity

The paper surveys a theory of FDI, which captures a unique feature: hands-on management standards, that enable investors to react in real time to a changing economic environment. Equipped with superior managerial skills, foreign direct investors are able to outbid portfolio investors for the top productivity firms in a particular industry in which they have specialized in the source country. Consequently, FDI investors would make investment, both larger, and of higher quality (namely, with large rates of returns), than the domestic investors. The theory can explain both two-way FDI flows among developed countries, and one-way FDI flows from developed to developing countries. Gains to the host country from FDI stem from the informational value of FDI. The predictions of the theory are consistent with evidence from panel data: larger FDI coefficients in the domestic investment and output growth regressions relative to the portfolio equity flow and international loan coefficients, reflect a more significant role for FDI in the domestic investment process than other types of capital inflows.

Economic forces and the OMXS30 - A study of economic variables' ability to predict stock returns on the OMXS30

This paper investigates whether the information from a number of different economic variables have the ability to predict stock returns on the OMXS30. The estimated forecast models are evaluated with a number of standard metrics to find the best performing models. These models are then used in combination with two portfolio strategies and tested over different time periods and forecast horizons, thereby giving an insight into the economic value of the underlying forecast models actual performance. The economic results are then compared with a benchmark model consisting of a simple buy and hold strategy to find the best performing combination of portfolio strategies and forecast models. Generally, none of the used economic variables are found to be a consistent predictor over all time periods, even though a few managed portfolios succeed in outperforming the buy and hold strategy over some specific time periods, only one portfolio (Switch Rec Bivariate OilExch 1 month) outperforms the buy and hold over all time periods. In the end we conclude that our findings are not conclusive enough to verify whether the final outcomes of the successful portfolio management strategies are the case of ‘good’ forecasting, or that of randomness and luck

Option Formulas for Mean-Reverting Power Prices with Spikes

Electricity prices are known to be very volatile and subject tofrequent jumps due to system breakdown, demand shocks, and inelasticsupply. Appropriate pricing, portfolio, and risk management modelsshould incorporate these spikes. We develop a framework to priceEuropean-style options that are consistent with the possibility ofmarket spikes. The pricing framework is based on a regime jump modelthat disentangles mean-reversion from the spikes. In the model thespikes are truly time-specific events and therefore independent fromthe mean-reverting price process. This closely resembles thecharacteristics of electricity prices, as we show with Dutch APX spotprice data in the period January 2001 till June 2002. Thanks to theindependence of the two price processes in the model, we breakderivative prices down in a mean-reverting value and a spike value. Weuse this result to show how the model can be made consistent withforward prices in the market and present closed-form formulas forEuropean-style options.mean reversion;electricity price modelling;energy markets;option pricing;power spikes

Pathways to Defense Budget Reform

Excerpt from the Proceedings of the Nineteenth Annual Acquisition Research SymposiumThe Planning-Programming-Budgeting-Execution (PPBE) process is the most powerful system of incentives affecting acquisition management in the Department of Defense. It is the conduit to money. A key feature of PPBE is the program of record concept that relies on a multi-year planning process. Not only does the program of record hamper technology adoption through adherence to baselines, it creates barriers to interoperability by stovepiping program decisions. Many researchers have detailed the inadequacies of PPBE and the need for embracing a portfolio management approach that aligns with best practices found in commercial and international organizations. This paper dives deeper into the history of how the legislative and executive branches managed defense budget portfolios in the 1960s and before, as well as how PPBE upended those traditional processes. First, it traces the reduction in execution flexibility over time by documenting the budget structure and thresholds for reprogramming. Second, it examines criteria for effective oversight in the PPBE and portfolio settings. The paper concludes that execution flexibility in the form of portfolio budgeting is not only consistent with economic efficiency, it is consistent with United States traditions of congressional control.Approved for public release; distribution is unlimited

Forward utilities and Mean-field games under relative performance concerns

We introduce the concept of mean field games for agents using Forward utilities of CARA type to study a family of portfolio management problems under relative performance concerns. Under asset specialization of the fund managers, we solve the forward-utility finite player game and the forward-utility mean-field game. We study best response and equilibrium strategies in the single common stock asset and the asset specialization with common noise. As an application, we draw on the core features of the forward utility paradigm and discuss a problem of time-consistent mean-field dynamic model selection in sequential time-horizons.Comment: 24 page

Volatility clustering in land markets

Purpose– The purpose of this paper is to investigate the volatility clustering in the return of land markets through both theoretical and empirical approaches.Design/methodology/approach– Using extensive monthly panel data at the provincial level from 1986 to 2013, the authors identify the existence of time-correlated and time-varying returns in Canadian land markets.Findings– Consistent with the proposed theory, volatility clustering in land markets tends to be observed in more populated areas.Originality/value– The result has significant implications for portfolio management, economic theory and government policy by revealing the systematic pattern of volatility clustering in land markets.This is the author accepted manuscript. The final version is available from Emerald via http://dx.doi.org/10.1108/PM-02-2014-000