Research articles for the 2019-02-17

A Comparison of Economic Agent-Based Model Calibration Methods
Donovan Platt

Interest in agent-based models of financial markets and the wider economy has increased consistently over the last few decades, in no small part due to their ability to reproduce a number of empirically-observed stylised facts that are not easily recovered by more traditional modelling approaches. Nevertheless, the agent-based modelling paradigm faces mounting criticism, focused particularly on the rigour of current validation and calibration practices, most of which remain qualitative and stylised fact-driven. While the literature on quantitative and data-driven approaches has seen significant expansion in recent years, most studies have focused on the introduction of new calibration methods that are neither benchmarked against existing alternatives nor rigorously tested in terms of the quality of the estimates they produce. We therefore compare a number of prominent ABM calibration methods, both established and novel, through a series of computational experiments in an attempt to determine the respective strengths and weaknesses of each approach and the overall quality of the resultant parameter estimates. We find that Bayesian estimation, though less popular in the literature, consistently outperforms frequentist, objective function-based approaches and results in reasonable parameter estimates in many contexts. Despite this, we also find that agent-based model calibration techniques require further development in order to definitively calibrate large-scale models. We therefore make suggestions for future research.

Alice’s Adventures in Factorland: Three Blunders That Plague Factor Investing
Arnott, Robert D.,Harvey, Campbell R.,Kalesnik, Vitali,Linnainmaa, Juhani T.
Factor investing has failed to live up to its many promises. Its success is compromised by three problems that are often underappreciated by investors. First, many investors develop exaggerated expectations about factor performance as a result of data mining, crowding, unrealistic trading cost expectations, and other concerns. Second, for investors using naive risk management tools, factor returns can experience downside shocks far larger than would be expected. Finally, investors are often led to believe their factor portfolio is diversified. Diversification can vanish, however, in certain economic conditions, when factor returns become much more correlated. Factor investing is a powerful tool, but understanding the risks involved is essential before adopting this investment framework.

Bail-In vs Bail-Out: Bank Resolution and Liability Structure
Leanza, Luca,Sbuelz, Alessandro,Tarelli, Andrea
We study the optimal liability structure of a bank under different resolution regimes and capital requirements. We do so by developing a structural model, allowing for bail-in and default events triggered either endogenously or by an external regulator, for a bank holding insured deposits and issuing covered (non-bail-inable) and uncovered (bail-inable) debt. As opposed to a bail-out resolution regime, a credible bail-in resolution regime endogenously reduces leverage and mitigates default risk. A strict enforcement of the Common Equity Tier 1 (CET1) capital requirement, as introduced by the Basel III regulation, entails a dramatic reduction of the optimal bank leverage.

Central Bank Digital Currencies: Preliminary Legal Observations
Nabilou, Hossein
Breakthroughs in financial technology (fintech), ranging from early coins and banknotes to card payments, e-money, mobile payments, and more recently, cryptocurrencies portend transformative changes to the financial and monetary systems. Bitcoin (BTC) and cryptocurrencies bear a significant resemblance to base money or central bank money (CeBM). This functional similarity can potentially pose several challenges to central banks in various dimensions. It may pose risks to central banks’ monopoly over issuing base money, to price stability, to the smooth operation of payment systems, to the conduct of monetary policy, and to the stability of credit institutions and the financial system. From among several potential policy responses, central banks have been investigating and experimenting with issuing central bank digital currency (CBDC). This paper investigates CBDC from a legal perspective and sheds lights on the legal challenges of introducing CBDC in the euro area. Having studied the potential impact of issuing CBDC by the European Central Bank (ECB), particularly on the banking and financial stability, on the efficient allocation of resources (i.e., credit), as well as on the conduct of monetary policy, the paper concludes that issuing CBDC by the ECB would face a set of legal challenges that need to be resolved before its launch at the euro area level. Resolving such legal challenges may prove to be an arduous task as it may ultimately need amendments to the Treaty on the Functioning of the European Union (TFEU).

Constrained Risk Budgeting Portfolios: Theory, Algorithms, Applications & Puzzles
Jean-Charles Richard,Thierry Roncalli

This article develops the theory of risk budgeting portfolios, when we would like to impose weight constraints. It appears that the mathematical problem is more complex than the traditional risk budgeting problem. The formulation of the optimization program is particularly critical in order to determine the right risk budgeting portfolio. We also show that numerical solutions can be found using methods that are used in large-scale machine learning problems. Indeed, we develop an algorithm that mixes the method of cyclical coordinate descent (CCD), alternating direction method of multipliers (ADMM), proximal operators and Dykstra's algorithm. This theoretical body is then applied to some investment problems. In particular, we show how to dynamically control the turnover of a risk parity portfolio and how to build smart beta portfolios based on the ERC approach by improving the liquidity of the portfolio or reducing the small cap bias. Finally, we highlight the importance of the homogeneity property of risk measures and discuss the related scaling puzzle.

Integrating electricity markets: Impacts of increasing trade on prices and emissions in the western United States
Steve Dahlke

This paper presents estimates of short-term relationships between regional electricity trade in the western United States (U.S.) and prices, emissions, and generation dispatch. Consistent with economic theory, I find a negative short term relationship between electricity prices in California and regional trade. Each 1 gigawatt hour (GWh) increase in California electricity imports is associated with an average $0.15 per megawatt hour (MWh) decrease in the California Independent System Operator (CAISO) wholesale electricity price. I also find a negative short term relationship between carbon dioxide emissions in California and electricity imports that is partially offset by a positive relationship between exports and emissions from neighboring regions. On net, each 1 GWh increase in regional trade is associated with a net 70 ton average decrease in CO2 emissions across the western U.S. The results suggest electricity imports mostly displace natural gas generation on the margin in CAISO, with each MWh of imports associated with 0.88 MWh decrease of CASIO natural gas. A small, positive relationship with SO2 and NOx emissions in neighboring states is also associated with increased exports to California. This is evidence that a small portion (less than 10%) of electricity imports into California are supplied by coal plants. This study suggests substantial short-term monetary benefits for California consumers can be achieved from market regionalization. It also provides evidence that California's cap and trade program has been relatively effective in limiting the carbon content of imported electricity, even absent a regional or national cap on CO2 and related concerns about carbon leakage. The conclusions suggest policy efforts to reduce trade barriers should move forward in parallel with strong greenhouse gas policies that cap emissions levels across the market region.

Predictability of Treasury Bond Returns: Risk Premia or Overreaction?
Rebonato, Riccardo
We examine whether the predictability and business-cycle dependence of excess returns in US Treasuries can be more naturally explained in terms of state-dependent risk premia or a specific cognitive bias (representativeness). We show that the extremely parsimonious cognitive-bias model in Shleifer and Gennaioli (2018) accounts very well for a large number of stylized facts about the predictability of excess returns, and their business-cycle dependence. We also test the risk-premium explanation by looking at the correlation between the payoff of the carry strategy and (several proxies for) consumption. When we do so we find that this correlation either has no explanatory power for returns, or has the wrong sign. We conclude that undue extrapolation in the future of recent information provides a parsimonious, simple and arguably more compelling account of excess returns than a risk-premium explanation.

Stochastic Deflator for an Economic Scenario Generator with Five Factors
Po-Keng Cheng,Frédéric Planchet

In this paper, we implement a stochastic deflator with five economic and financial risk factors: interest rates, market price of risk, stock prices, default intensities, and convenience yields. We examine the deflator with different financial assets, such as stocks, zero-coupon bonds, vanilla options, and corporate coupon bonds. We find required regularity conditions to implement our stochastic deflator. Our numerical results show the reliability of the deflator approach in pricing financial derivatives.

Strong convergence rates for Markovian representations of fractional Brownian motion
Philipp Harms

Fractional Brownian motion can be represented as an integral over a family of Ornstein-Uhlenbeck processes. This representation naturally lends itself to numerical discretizations, which are shown in this paper to have strong convergence rates of arbitrarily high polynomial order. This explains the potential, but also some limitations of such representations as the basis of Monte Carlo schemes for fractional volatility models such as the rough Bergomi model.

Supervised Deep Neural Networks (DNNs) for Pricing/Calibration of Vanilla/Exotic Options Under Various Different Processes
Ali Hirsa,Tugce Karatas,Amir Oskoui

We apply supervised deep neural networks (DNNs) for pricing and calibration of both vanilla and exotic options under both diffusion and pure jump processes with and without stochastic volatility. We train our neural network models under different number of layers, neurons per layer, and various different activation functions in order to find which combinations work better empirically. For training, we consider various different loss functions and optimization routines. We demonstrate that deep neural networks exponentially expedite option pricing compared to commonly used option pricing methods which consequently make calibration and parameter estimation super fast.

The Effect of Credit Supply Shocks on Economic Activity: A Threshold Vector Autoregression Approach
Wojnilower, Joshua
Following the recent U.S. financial crisis, a new generation of macroeconomic models considers theoretical constraints to the supply of credit. Concurrently, a growing body of literature demonstrates existence of a nonlinear relationship between credit market conditions, monetary policy, and real economic activity. This paper uses threshold vector autoregressions (TVARs) to determine if the relationship between the supply of credit and the real economy changed over time and under different credit market conditions during the U.S. post-war era. Results yield evidence that a quantitatively important relationship between the supply of credit and real economic activity existed during the entire era and separate from periods of financial stress. Findings, however, also offer evidence that the indirect effect of changes in the supply of credit on real economic activity, operating through their effect on macro risk premia, became quantitatively more important during periods of financial stress in the twenty-first century.

The F-Utility of Wealth: It’s All Relative
Muralidhar, Arun
Finance theory is based on a very simple, yet critical assumption that “individuals maximize the expected utility of wealth”. However, there are three crucial elements of this simple 6-word phrase that do not really stand the test of what investors actually do and one could argue, that the incorrect use of Modern Portfolio Theory (MPT) has led to the looming global retirement crisis. First, investors care about relative wealth (i.e., wealth relative to a goal) rather than absolute wealth, popularly called “Goals-Based Investing”. Second, individuals (or principals) are not always the ultimate decision makers â€" rather, many investment decisions are delegated to agents, which distorts behavior. Third, and most crucially, most investors do not appear to focus on utility functions, but rather seek to maximize risk-adjusted return. Instead, finance theory should start with the assumption that “investors delegate to maximize relative risk-adjusted returns.” This paper seeks to show how incorporating these three simple and completely realistic changes impacts asset pricing, asset allocation and the correct use of risk-adjusted performance measures. While the initial step requires a re-think of finance theory and models, the more urgent goal is to ensure retirement security as this new approach leads to financial innovation, better regulation and potentially better retirement outcomes.

Total, asymmetric and frequency connectedness between oil and forex markets
Jozef Baruník,Evžen Kočenda

We analyze total, asymmetric and frequency connectedness between oil and forex markets using high-frequency, intra-day data over the period 2007 -- 2017. By employing variance decompositions and their spectral representation in combination with realized semivariances to account for asymmetric and frequency connectedness, we obtain interesting results. We show that divergence in monetary policy regimes affects forex volatility spillovers but that adding oil to a forex portfolio decreases the total connectedness of the mixed portfolio. Asymmetries in connectedness are relatively small. While negative shocks dominate forex volatility connectedness, positive shocks prevail when oil and forex markets are assessed jointly. Frequency connectedness is largely driven by uncertainty shocks and to a lesser extent by liquidity shocks, which impact long-term connectedness the most and lead to its dramatic increase during periods of distress.